What I Learned About Investing from Darwin

by Pulak Prasad, 2023 (330 p.)

This was a fantastic book and the reviews on Amazon (of which there are already 78 after about one week since publication) are almost all extremely positive, with 90% giving the book a 5-star rating.  One reviewer writes: “When an investor delivers a return of 20%+pa (after fees) over 15 years, outperforming his index by 11%, you better listen.”  Another writes only that “it’s amazing, just buy it and study with pencil.” Yet another writes that Prasad’s book is an “exceptional work that seamlessly merges the realms of biology and finance.”  I second all these assessments and rate this book a must read not only for investors, but also evolutionary biology enthusiasts, philosophers, politicians, military strategists, and regulators.

Prasad is candid and comes across as humble and transparent, yet brilliant and deeply knowledgeable on investing and biology. As he claims in the beginning of the book: “I will not begrudge you for questioning my authority to speak on matters of evolution when I do not have a degree in evolutionary theory. My defense is the same as the one given by Mary Jane West-Eberhard in her stunningly original book Development Plasticity and Evolution: I can read.”  Indeed Prasad cites many notable scientists, books, and research papers. He also displays an impressive ability to explain complex theories while drawing precious parallels to the art of investing. Being a fan and devout believer in his approach, this book ranks among the best I have ever read.

On his website, Prasad establishes his credibility as a brilliant investor and shows that his simple approach has worked for decades. He grew up in India and went to seven different schools in his first twelve years of study, as his father was in the armed forces and was transferred to new locations every couple of years. He is gifted with a sense for numbers and thought he could be an engineer. Like Google’s Sundar Pichai, he earned an engineering degree from the Indian Institute of Technology (IIT), which is one of the world’s most prestigious technical universities. He got his first job in Unilever India, where he did not enjoy the work, so he left to pursue an MBA at the Indian Institute of Management.

At 54, Pulak Prasad is four years older than Sundar Pichai, but he landed a job at McKinsey & Company in 1992, which was 10 years before Pichai, and at the age of just 23. But while Pichai came to the U.S. to earn a Masters degree from Stanford and an MBA from Wharton before joining McKinsey, Prasad remained in India. Like several notable investors before him, Prasad left McKinsey to become an investor. He spent eight years at Warburg Pincus, four of which were as the co-head of India. He then left Warburg in 2007 to start Nalanda Capital, which focuses exclusively on listed Indian securities. Despite a rough start where he drew down over 50% of his investor’s capital during the bear market of 2008, Nalanda today manages about $5 billion, primarily for US and European institutions.

Apart from investing, and common to other great investors, Prasad has a passion for reading. For reasons not clear even to him, he became interested in Darwinian theory of evolution about a decade ago, when he started devouring books on the topic. Encouraged by readers of his investor quarterly letters, he decided to write this book on the parallels between evolutionary theory and investing. I had never heard of Prasad before, but after reading this book I became a big fan!

Pulak Prasad (54)

While I thoroughly enjoyed Prasad’s book and am in awe with his knowledge and writing skills, I took issue with one key contradiction in his philosophy: the insistence on low P/E ratios in order to buy an outstanding company, even though his objective is to hold them over the very long term. I passionately agree with most of what Prasad espouses, which I share in my extensive highlights and notes below, but it would be disingenuous of me to let this key disagreement go unmentioned. Prasad is well aware and transparent about the contradiction in wanting to own outstanding businesses “forever,” while only buying them when they are cheap. He does not proclaim that this is the only, nor necessarily the best way to do it, but that it is his way and he plans to stick with it. In the concluding chapter he writes: “We have a straightforward rule that is also easy to implement: Buy when the price is right.” But then he admits that “We have no way of figuring out the right price. Maybe some folks do. Good for them.”

Towards the end of the book Prasad gives an example of what he means by right price. “Let’s say we have valued a business at $100 per share. If the stock falls to $100 and our business assessment remains unchanged, we buy as much of the business as we can at or below $100.” My question is this: If he is so focused on owning outstanding companies forever, then why is some arbitrary near-term entry price so important? And why isn’t he a seller when the valuation is high? He summarizes his pushback against these common questions with two statements and one question: A great business usually surprises to the upsidevaluation multiples generally don’t stay benign for great businesses; and why should valuation be limited to only the next five or ten years?

Prasad is willing to hold onto businesses when they are trading at what others might consider egregious multiples, and he gives the example of holding onto stocks with trailing P/E multiples of 60x. But on the way in, he insists on buying his “forever” holdings at mid-teens trailing P/E multiples, that were, on average, at a discount to the Indian market valuations. How does he do this? He claims that he is willing to wait for the right time, which is basically when markets are crashing or when his “forever” companies are having serious short term problems, and that he is willing to wait for decades if that is what it takes.

I have several issues with his buy discipline, even though I respect it and admit that it has worked for him. The first is with the contention of holding “forever,” since even the most outstanding companies do not remain outstanding forever, just like trees don’t grow to the sky. I get it, and agree, that a small group of elite companies tend to distance themselves from the rest due to advantages that persist over the long term. Peter Thiel calls it exponential dominance in his book Zero to One (2014). This is a key thesis behind our investment approach at Victori Capital, and it was elegantly proposed by Vilfredo Pareto in the late 1800s. It applies not only to species and companies, but also to countries, pea-pods, and wealth accumulation generally. Pareto was not the first to observe this phenomenon. The Bible has a famous quote in The Gospel of Matthew (25:29): “For unto every one that hath shall be given, and he shall have abundance: but from him that hath not, shall be taken away even that which he hath.” In other words, the richer tend to get richer, and the poor tend to stay poor.

Matthew’s principle is why it makes sense, as an investor, to own for the long term, not only the best businesses, but the ones that have remained formidable for a long time and stand a good chance of getting even more formidable into the distant future. As I mentioned, Prasad does not disagree with this, but by demanding a low trailing P/E on the way in, he shuts the door on owning most of the very best businesses. And by not letting the very best businesses enter and remain in his portfolio, he incurs a huge opportunity cost that works against his ability to compound value with lower risk, which is his stated objective. What if that stock he thinks is worth $100 never gets even near $100, but instead compounds at 20% for 20 years, thereby going up 32-fold? With his inflexible and dogmatic P/E-based buying rule, he will most certainly miss these stocks, and as he admits, there are not a lot of them out there to be caught.

But it doesn’t stop there. What if the market does crash, as it has over the centuries and most certainly will again at some point in the future, thereby giving Prasad the opportunity to load up on these “forever” companies he has been waiting years to buy below the arbitrary P/E multiple he requires? How can he back up the truck on these stocks if he is fully invested in a portfolio filled with high P/E stocks that have been appreciating for many years? He could of course deploy leverage, but debt is something he claims to avoid like the plague. He can also sell the expensive stocks to buy the cheap ones, but he refuses to do that, and doing it could impose a high capital gain tax liability on investors that are not tax-free institutions.

These are conundrums that most fund managers face, and I felt like Prasad failed to properly address them – or at least he did not address them as well as Thomas Phelps did in 100 to 1 in the Stock Market (1972), or Phil Fisher did in Common Stocks and Uncommon Profits (1958). These two incredible investors both espoused focusing on long-term returns from outstanding companies, while eschewing near-term valuation filters such as trailing P/E ratios. Prasad mentions Phil Fisher’s “scuttlebutt” approach to discerning strong signals from weak ones, but perhaps he should have also mentioned what Phil Fisher said about getting caught up on near term valuation measures. Charlie Munger, Chuck Akre, and Terry Smith (to name a few amazing investors) have argued the same thing, which is that over the long term, the trailing P/E multiple at which and outstanding company trades is almost always irrelevant to what one receives in value. As usual, perhaps it was Warren Buffett who said it best: “Price is what you pay, value is what you get.”

Of course I am not suggesting I believe in paying any price for a good story. I never have in my 25 year career paid egregious multiples for a theme stock, even though I have witnessed people do it successfully all around me. As has been confirmed empirically by evolutionary scientists over the ages, outstanding companies, like long-lived species,have common traits that converge over long stretches of time. Like Prasad, we look for these traits by studying the long histories of our candidates and their industries. But unlike Prasad, when we find them we buy them with the intention of holding them for as long as they remain outstanding, by our definition.

Prasad espouses laziness in investing, but investing does not reward the lazy. It takes a lot of work to find the most outstanding companies, but it takes even more work to ensure that they remain outstanding. Just saying we never sell seems simple and, indeed, somewhat lazy, and I certainly do not subscribe to it. To paraphrase Phelps, serious investing is not something you can do well in your spare time, especially not after dinner.

Above I wrote that I take issue with the word forever, because nothing lasts forever. What the Brazilian songwriter Vinicius de Moraes wrote about love also applies to stocks: “What I can say about love is that it is not immortal, but more like a flame that burns infinitely while it lasts.” My version of this concept has been written on our whiteboard since we started Victori in 2014: Never fall in love with a stock, it will break your heart every time. This simple rule also applies to markets, fund managers, and countries.

For someone who invests exclusively in an emerging market and believes that investing mirrors evolution (which plays out over the very long term), Prasad should pay heed to what Todd Petzel points out in Chapter 2 of his excellent book, Modern Portfolio Management (2021): “The fact that the United States has managed to have a continuously trading stock market all during a period of relatively free capitalism and growing GDP does not guarantee that the next 200 years will be as kind. This is a common error in the study of financial markets. If the data shows that an event has not happened, it is too frequently inferred that it cannot happen. … Wars, revolutions, and hyperinflations have each destroyed entire asset markets. The investor that says, ‘Such things can’t happen to me,’ should instead say, ‘I suspect a complete wipeout is a small-probability event, but if it happens, how will I cope?’” The insurance company Mass Mutual captured it well in their famous tagline: You can’t predict. You can prepare.

Nassim Taleb argues a similar point in the prologue of The Black Swan (2007): “The sighting of the first black swan might have been an interesting surprise for a few ornithologists, but that is not where the significance of the story lies. It illustrates the severe limitation of our learning from observations or experience and the fragility of our knowledge. One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans.” This is why using terms like never selling or holding forever, is so dangerous.

In closing, Prasad has produced a masterpiece investment book that I would strongly recommend to anyone interested in investing and/or evolutionary biology. Regardless of your own view on when to buy or how to invest generally, there are many precious insights in this book that apply not only to investing but life in general. I feel indebted to Prasad for sharing all the knowledge he packed into this wonderful book, which I intend to read again and again as I evolve as a student of markets and practicing investor.


HIGHLIGHTED EXCERPTS [My notes are bolded]

If you explore the “evolution” section of any scientific journal (for example, Proceedings of the National Academy of Sciences, available at www.pnas.org), you will be dazzled by the range of research topics and the stunning advances being made by scientists. But the investment community? It doesn’t matter how you look at it—the data are ugly. Really ugly. And it shows that we, the fund managers, are idiots. According to a 2021 S&P report on the U.S. equity market (called the SPIVA U.S. Scorecard), across periods of five, ten, and twenty years, 75 to 90 percent of U.S. domestic funds underperformed the market. Let that fact sink in before you read any further. About 75 to 90 percent of fund managers, most of whom have graduate degrees, including MBAs, from elite schools and manage trillions of dollars, fail to beat the market. If you are part of the financial services community, you may believe that it is easier to outperform the small-cap market benchmarks. Not true. The market outperformed about 93 percent of small-cap funds during the ten-year period from 2011 to 2021. That is bad enough, but the dismal news does not end there. Not only are most U.S. funds underperforming, but they have also gotten worse over time. According to the same S&P report, in 2009, over three-and five-year periods (ten- and twenty-year periods were not reported), “only” 55 to 60 percent of U.S. domestic funds had underperformed the market.

Who Am I? I am an equity fund manager. In 2007, I founded an investment firm called Nalanda Capital, which currently manages a little more than US $ 5 billion invested in listed Indian securities. Nalanda’s investment philosophy can be summarized in ten words: We want to be permanent owners of high-quality businesses. Let me repeat that: We want to be permanent owners. We don’t invest unless we think we can own a business forever. A bad business that is dirt cheap? Pass. A mediocre business at a low price? Thanks, but no thanks. A high-quality business at a fair price? Give me more so I can never let goWe invest almost exclusively in businesses owned and run by entrepreneurs of which the entrepreneur is typically the largest shareholder, and we are usually the second largest.

Nalanda’s approach to investing comprises three straightforward, sequential steps: 1. Avoid big risks. 2. Buy high quality at a fair price. 3. Don’t be lazy— be very lazy. This straightforward investment process has led to the following outcome. One rupee (INR 1) invested in Nalanda’s first fund at its inception in June 2007 would have been worth INR 13.8 in September 2022. The same amount invested in India’s Sensex (the country’s large- cap index) would have been worth INR 3.9, and if invested in the Midcap Index would have been worth only INR 4. Over a little more than fifteen years, based on actual cash inflows and outflows, the annualized rupee return for this fund was 20.3 percent (after all our fees), and the fund beat both the Sensex and the Midcap Index by 10.9 percentage points. That is not a bad track record.

This experience across industries, companies, and continents may qualify me to pontificate on the peculiarities of a pizza delivery business model, but I will not begrudge you for questioning my authority to speak on matters of evolution when I do not have a degree in evolutionary theory. My defense is the same as the one given by Mary Jane West-Eberhard in her stunningly original book Development Plasticity and Evolution: I can read.

In 2000, in response to a question about his favorite books, Munger recommended The Selfish Gene by Richard Dawkins. I read the notes of this meeting in 2002 and decided to buy the book. My life hasn’t been the same since.

As mentioned, most investors have a poor long- term track record. The implication of this is obvious: Most investment methods don’t work over the long run. Ours has. And so here I am, sharing my thoughts with you.

Just as scientists can’t agree on the definition of what constitutes a species or a gene, investors have wildly different opinions on calculating something as simple as a business’s value.

I will argue that learning the skill of not investing is harder and more important than learning how to invest.

Let me borrow from the field of statistics to describe them. The first kind— dubbed a type I error 1 by statisticians who can never be blamed for being creative— occurs when I make a bad investment because I erroneously think it is a good one. It is the error of committing self-harm and is also called a false positive or error of commissionA type II error occurs when I reject a good investment because I erroneously think it is bad. This is the error of rejecting a potential benefit and can be termed a false negative or error of omission. Every investor, including Warren Buffett, makes these two errors on a regular basis. They either harm themselves or walk away from a great opportunity. As any statistician will tell you, the risk of these two errors is inversely related.  Minimizing the risk of a type I error typically increases the risk of a type II error, and minimizing the risk of a type II error increases the risk of a type I error. Intuitively, this seems logical. Imagine an overly optimistic investor who sees an upside in almost every investment. This individual will make several type I errors by committing to bad investments but also will not miss out on the few good investments.

Back to Buffett’s two rules. Despite losing money occasionally, what is he asking us to do when he orders us not to lose any? Buffett has never explicitly explained this (at least I have never found an explanation), but this is what I think he means: Avoid big risks. Don’t make type I errors. Don’t commit to an investment in which the probability of losing money is higher than the probability of making moneyThink about risk first, not return.

We at Nalanda never bring stock price volatility into a risk discussion. We define “risk” as the probability of capital loss. The higher the probability of loss, the higher the risk. If my investment in Company A is likely to lose more money over my investment in Company B, I will deem Company A to be “riskier” than Company B irrespective of past or future volatility in their stock prices.

A Great Investor Is a Great RejectorAccording to data from the World Bank, the United States had 4,400 listed companies in 2018. For simplicity, let’s assume a round 4,000. First, we need to decide how many of these are “good investments.” Let’s keep it simple and classify a “good investment” as one that will make us a decent return over the long run. It has a competent and honest management team, a modest growth rate, makes enough money, and has low leverage. Let’s assume that 25 percent of the listed universe comprises “good investments.” If you talk to practitioners— that is, actual investors— you will not get a number too far from this percentage. In any event, the exact percentage is less significant, as we will see. Thus, we can say there are 1,000 good investments and 3,000 bad investments in the U.S. listed universe by this logic. Again, don’t get too pained about this strict dichotomy; it is serving a purpose that we will get to.

If he sees a good investment (i.e., one in which he will make money), he makes a favorable investment decision 80 percent of the time. Thus, his rates of type I and type II errors are both 20 percent. If this star investor makes an investment decision, what is the probability that it is a good investment? You’d say 80 percent, right? Wrong. The answer is 57 percent. But why? Isn’t he right 80 percent of the time? How can we go from 80 percent to 57 percent? Here is how. There are 1,000 good investments in the market, and since this investor makes a type II error 20 percent of the time (i.e., he mistakenly rejects 20 percent of these), he will select only 800 companies from his list. The market also has 3,000 bad investments, and since he makes type I errors 20 percent of the time (i.e., he mistakenly accepts 20 percent of these), he will mistakenly select 600 companies from this list, thinking that they are good investments. Thus, his universe of what he thinks are good investments will be 1,400 companies (800 + 600). Are you with me? Good. Now to the most interesting part. Of these 1,400 businesses that the investor thinks are good investments, how many do you think are good investments? Only 800. Hence, the probability of his making a good investment will be 800 ÷ 1,400 = 57 percent.  Let me repeat this statement, which is the bedrock of our philosophy and that of the rest of this book: There are very few good investments in the market.

Let’s say that our investor, Investor A, decides to become better at rejecting bad investments and reduces his rate of type I errors from 20 percent to 10 percent. Thus, from the 3,000 bad investments in the market, he will select only 300 businesses (10 percent × 3,000). As his type II error rate remains 20 percent, he will erroneously reject 200 of the 1,000 good investments, thereby selecting 800 investments. Thus, Investor A has selected 1,100 investments (300 + 800), but only 800 of these are good. In this scenario, Investor A’s probability of selecting a good investment improves from 57 percent to 73 percent (800 ÷ 1,100). I assume you will admit that this is quite a dramatic improvement in his success rate. Investor B, unlike Investor A, is more focused on not missing out on good opportunities. He chooses to reduce his rate of type II errors from 20 percent to 10 percent and keeps his rate of type I errors at 20 percent. Thus, of the 1,000 good investments, he will select 900 (90 percent × 1,000), and of the 3,000 bad investments, he will mistakenly assume that 600 are good (20 percent × 3,000). Thus, Investor B has selected 1,500 investments (600 + 900), but only 900 are good. Thus, Investor B’s probability of selecting a good business improves from 57 percent to 60 percent (900 ÷ 1,500). This is an improvement— but only by 3 percentage points. Good, but not great.

Guess what happens if another investor, Investor C, improves his rate of both type I and type II errors from 20 percent to 10 percent. I was flabbergasted when I first saw the answer: 75 percent. This is barely above the 73 percent achieved by Investor A, who was focused only on reducing type I errors. A dramatic improvement in performance comes only when the rate of type I errors— errors of making bad investments— is reduced. Thus, whereas most investment books and college curricula focus on teaching how to make good investments, everyone would be better off by learning how not to make bad investments. An investment career is probably among the very few that rewards the skeptic more than the optimist. Buffett is the best investor in the world because he is the best rejector in the world. [AA Note:  While I understand that it is heresy to disagree with the statement that Buffett is the best investor in the world because he is the best rejector, I don’t agree.  He might be the best investor, but it is probably due to how cheap his cost of capital from insurance float is, how frequently it comes in, and permanent it is.  I also think that he benefits, more than most investors, from the Matthew Effect.]

There are myriad ways of learning life lessons— parents, siblings, one’s spouse, friends, books, movies, school, college, work, and leaders are a few sources that come to mind. I will never really know why I am what I am. But I am reasonably confident of the identity of my primary teacher and guide in the area of investing— my own mistakes.

Most investors will keep making type I errors (making bad investments) throughout their careers. At least I have, and I will. It’s inevitable. So when I say that we need to avoid type I errors, I am proposing that we try to avoid the avoidable type I error. By not taking on big risks. What is a big risk? It is not clear to me that a definition is possible or even desirable for a practitioner. Instead of defining “big risk,” let me describe the kinds of situations we avoid at Nalanda.

Being Wary of Criminals, Crooks, and Cheats People don’t change. Especially criminals, crooks, and cheats. As permanent owners, we at Nalanda have no interest in a business owned or run by someone who defrauds customers, suppliers, employees, or shareholders. When we come across such a person, we don’t ask if the business is cheap enough for the risk to be mitigated; we don’t ask if we could persuade this individual to change; we don’t ask if their crimes are trivial enough to ignore. We simply walk away.  We are highly vigilant and do not even start assessing the business fundamentals until we have convinced ourselves that the promoters have impeccable integrity. We employ a two level process for this assessmentWe always hire a forensic diligence expert to assess if the owner or senior managers have a dubious past. During this period, we conduct our parallel diligence on the promoters and managers by scouring the media, studying past annual reports, listening to their conference call recordings, reading their interviews, and talking to people who have had personal and business dealings with them. In almost half the cases, we ask the external firm to further probe issues that have arisen during our diligence (for example, money leakage for large capex contracts or a cash payment to senior managers). This dual check provided by outsourcing and insourcing has prevented a lot of heartburn for us over the years. More importantly, it has saved a lot of money for our investors.

The fact remains that there are some well- known dodgy businesses in India whose stock prices have done reasonably well over the past few years. But we have never played this game and never will. Our philosophy of permanent ownership requires— demands— that we partner only with promoters of the highest integrity. And so that is what we do.

This is exactly what happens in the world of investing. Managements that have underperformed for long periods are able to convince investors to bet on their businesses with nothing but fancy promises and McKinsey reports. I blame neither managements nor McKinsey because optimism is not a crime. But I do get baffled at investors who, despite having access to data that amply demonstrate the incompetence of incumbent management, are willing to bet their clients’ money on the hope that this management will suddenly morph into an industry beater in the near future. More often than not, the dream scenario hyped up by the management morphs into a nightmare. [AA Note: in other words, start with people and avoid bad track records.]

P&L obsession is not limited to consultants. Read any analyst report or listen to conference call recordings that discuss quarterly results. You will be inundated with comments and questions on revenues, costs, and profit. After many years of investing, I realized that I needed to focus as much, if not more, on the company’s balance sheet. Receivables, inventory, payables, fixed assets. And most important of all, debt. Corporate finance theory has a thing for leverage.

As a long- term investor in a business, I don’t want the company ever to go bankrupt— whether the times are good or bad. I can live with a slightly lower return on equity and lower earnings-per-share (EPS) growth, but at least I will live. Not having high leverage probably makes sense to everyone. But the following may not: I am an advocate of no leverageMore than 90 percent of our portfolio companies have— and have always had— excess cash. Only three businesses out of about thirty in our portfolio have some debt. But even this debt is quite small— the maximum debt/ equity ratio among these three is 0.3.

Some of our portfolio companies do occasionally make acquisitions. Our counsel to them is to be deeply skeptical of the potential value creation in all cases. I will admit that the success rate of our advice has not been very high. Fortunately, none of our companies is addicted to M&A, and the ones that do make acquisitions have never bet the company. I am convinced that the cost of distraction— even if it was a small one— has not been worth the effort. If any of them start becoming serial acquirers, we will promptly press the exit button.

Not Predicting Where the Puck Will Be: What do mid- nineteenth-century railways and late twentieth-century dot-coms have in common? Railways transformed the United Kingdom in the early nineteenth century. The first passenger railway between Liverpool and Manchester was authorized by Parliament in 1826 and opened in 1830. Railways allowed people to travel farther at a much lower cost and in less time than the alternatives. They also spurred the growth of cities by enabling cheaper and faster transport of people and building materials. Many entrepreneurs jumped into the fray and by 1844 had opened more than 2,200 miles of railroad line. The stock market loved these companies, which promised growth forever. Between 1843 and 1850, 442 railway companies made a public offering of shares. Between January 1, 1843, and August 9, 1845, the index of railway stock prices doubled. But the bubble burst, as they inevitably do. The railway index fell over 67 percent from 1845 to 1850— many companies collapsed owing to incompetence, poor financial planning, or fraud. The common thread that binds eighteenth-century railways and twentieth- century dot-coms is the potential for enormous value destruction wrought by a fast- changing industry.

Some companies in industries that change fast ultimately do end up creating a lot of value. But very few companies. The only ones that have created truly significant value from the dot-com era are Amazon and Google (Facebook was founded in 2004). If you want to be charitable, you could add eBay and Priceline (now called Booking Holdings). But that’s it. Just step back and think about this for a moment. Only a handful of businesses from the 1995– 2000 bubble have prospered. To give you a sense of the scale of destruction, 546 IPOs successfully raised $ 69 billion in 1999 alone. What would have been the probability of finding the next winner? The path to creating wealth in rapidly evolving industries is treacherous, and we refuse to walk on it. Many investors are slaves to the famous quote of the hockey legend Wayne Gretzky: “I skate to where the puck is going to be, not where it has been.” 27 I am not one of them. I am just not that smart. In fast- changing industries, I have no idea who will win, when, or how. And to draw the parallel with hockey, since I don’t know where the puck is going to be, I refuse to play. We at Nalanda love stable, predictable, boring industries. Give us electric fans over electric vehicles, boilers over biotech, sanitaryware over semiconductors, and enzymes over e- commerce. We like industries in which the winners and losers have been largely sorted out and the rules of the game are apparent to everyone. For everything else, thanks, but no thanks.

There is a structural problem with an arrangement in which a parent also has a listed subsidiary. We have better things to do than to participate in this inherent conflict.

But You Would Have Missed Tesla! Yup. We would have. We eschew a very long list of risks. This is the core element of our investment strategy. We don’t invest in businesses run by crooks, we detest turnarounds, we stay as far away from leverage as possible, we refuse to engage with M& A addicts, we can’t figure out fast- changing industries, and we don’t align ourselves with unaligned owners. Are there any businesses left for us to invest in? In India, not many. At Nalanda, our shortlist comprises seventy- five to eighty companies out of a universe of about eight hundred with a market value of more than $ 100 million. Except for filial love, nothing in life comes free. Nalanda’s approach has a trade- off that many of you may find unacceptable. Imagine it’s late 2017, and you are impressed with all the media coverage of Tesla. The product seems like a winner based on its vast fan following. The CEO looks as impressive as the car he makes. But in 2017, Tesla had a net debt of about $ 7 billion and had suffered an operating loss of $ 1.6 billion. The company was also burning cash very fast— it had consumed $ 4.1 billion during the year. The traditional car businesses like BMW, Ford, GM, and Toyota had not yet entered the electric vehicle fray, but they had announced big plans. We abhor debt in general, but debt in a loss- making company with negative free cash flow in a fast- changing industry? One can get fired at Nalanda for proposing an investment in a business like this.

Tesla and Eicher Motors are the kinds of type II error we will inevitably commit because we reject highly indebted businesses, rapidly evolving industry landscapes, and turnarounds. But we will not change our approach. For every Tesla and Eicher, hundreds of unproven business models and turnaround stories are unceremoniously consigned to the dustbin of history. We believe our success is contingent upon our being comfortable with missing out on Teslas and Eichers because on average, avoiding type I errors works wonders over the long term. It has done so for us.

A bumblebee is a hairy insect that barely measures an inch in length.  The species— there are about three hundred of them— have been around for about thirty million years. They are preyed upon by crab spiders and birds. Their survival strategy was beautifully demonstrated in an experiment conducted by Dr. Tom Ings and Professor Lars Chittka of Queen Mary University of London, whose work was published in Science Daily in 2008. The scientists created a garden of artificial flowers that also contained some robotic crab spiders. They hid some spiders and made others visible. Whenever a bumblebee landed on a flower with a crab spider, the spider “captured” the bumblebee between its foam pincers. Within a few seconds, the robotic spider released the bee. The team found that the bumblebees soon started committing more type II errors: they started avoiding flowers even where there were no spiders, thereby reducing their foraging efficiency. In the wild, this instinct to avoid danger at the cost of going hungry must have played a significant role in the tremendous success of the species over millions of years. If the bumblebee can, why can’t we?

Evolutionary theory has taught me that . . . . . . the first and probably most important step in reimagining investing is to learn how not to invest. 1. Living things prioritize survival over everything else. In the animal world, this applies to prey and predator. Plants give up on opportunities to grow by redirecting resources when survival is at stake. 2. Millions of years of evolution have programmed the organic world to minimize errors of commission in favor of errors of omission. 3. Buffett’s two rules of investing (never lose money, and don’t forget to never lose money) are essentially a diktat for eliminating significant risks. 4. At Nalanda, we want to be permanent owners of high- quality businesses. Hence, we want to minimize risk before maximizing returns. 5. Just like the living world, we forgo potentially juicy opportunities if the risk of losing our capital is high. 6. We do this by avoiding crooks, turnarounds, high debt, serial acquirers, fast- changing industries, and unaligned owners. I believe we can be better investors only if we are better “rejectors.” 7. One downside of this approach is that we occasionally walk away from a potentially attractive investment. We are willing to live with this downside.

BUY HIGH QUALITY AT A FAIR PRICE: This section describes and, using evolutionary theory, justifies our buying philosophy at Nalanda. For many investors, identifying the right business to buy at the right time is almost all there is to investing. Switch on CNBC, open a financial newspaper, or read a blog, and you will witness a lot of time spent and ink spilled on companies to buy. This is unfortunate. As we have already seen in section I, not buying is an equally—if not more—important skill. There is also a profound conundrum about the buying strategy advocated by most fund managers. Everyone seems to spout the exact same philosophy as this section’s topic: Buy high quality at a fair price. I challenge you to find me a fund manager who professes to buy poor-quality businesses at high prices. Then why does the performance of professional investors vary so widely? One reason—not the only one, but a crucial one—is that our community has wildly different opinions on the meaning of “high,”“quality,” and “fair.” In this section, with many evolutionary theory elements as a backdrop, I will clarify the meaning of these words as they apply to Nalanda. I will discuss what we buy in chapters 2 to 4 and how we buy in chapters 5 to 7.

One alternative would be to use a two-step process that we use at NalandaIn the first step, we use one selection criterion that filters out low-quality or average- quality businesses and yields a preliminary list of high- quality companies. In the second step, we do more work on this preliminary list to further whittle it down to a final list. Our investable universe is around 800 Indian businesses (with a market value of more than $ 150 million). Of these, we have rejected close to 350 companies to minimize the risks outlined in the previous chapter. About 450 businesses remain. We then apply a single filter to cut down this list to about 150 firms. Let me call this single filter “F.” Remember that F simply gives us the preliminary list on which we need to work further to reject or choose businesses. After doing this work, our final list has only about 75 to 80 businesses. F gave us an excellent head start. And it continues to do so because we don’t spend any time analyzing a business unless F has cleared it.

A first plausible assumption would be “a great management team.”

I know that many professional investors will disagree with me. Many people in the fund management world pride themselves on their purported ability to separate the wheat from the chaff after a series of management meetings. Some of them may have this rare skill, but most are either deluded or lying. And if there is someone out there who can assess a company’s quality by meeting management, we can applaud them from the sidelines without falling into the trap ourselves.

Okay, let’s recap. We want to use a single filter, F, to select businesses for further analysis. This should hopefully save us a lot of time and effort. We wondered if we could use “quality management teams” or “fast growth” as our starting point. We rejected both as good candidates for F because it is tough to assess the former, and the latter can end up causing heartburn.

Does a high gross margin tell us anything about the quality of the business? Not really. Several internet businesses boasted a gross margin of more than 90 percent in the dot- com era, but almost all experienced huge losses because of their marketing spends.

Take this example of two real- world businesses: Business C has had an operating margin of about 3 percent over the past fifteen years. Business T has delivered an operating margin of 19 percent over the same period. Would you reject Business C and select Business T because T is “better” than C? If you did so, you would have spurned Costco, one of America’s best- run businesses. Business T is Tiffany & Co., a reasonably well-run business but not as well run as Costco. What makes Costco at a margin of 3 percent a better company than Tiffany at 19 percent?  I will get to that shortly. Suffice it to say that using margins as a starting point to narrow our list of companies may lead us astray. It fails our second and third criteria (removing most, if not all, low- quality businesses and selecting high- quality businesses).

We haven’t yet considered macro factors for building the short list. But which single piece of macro data should we consider for short-listing individual businesses? For example, if “experts” believe that inflation will rise, should we short-list only consumer goods businesses able to pass on the increased cost to their customers? If we do so, should we then completely revise the list if the inflation expectations get reversed within six months? I don’t know how to account for macro factors for short- listing high- quality businesses. I am not suggesting that it is the wrong thing to do— just that I don’t know how it can be done. And so, we avoid considering any macro factor as our preliminary filter F. What about accounting for macro factors when making our final list? More on that later.

Darwin, with his acute powers of observation, knew this. His statement at the beginning of this chapter asserts that hairless dogs have imperfect teeth and pigeons with feathered feet have skin between their outer toes. He predicted that if humans choose to select for one characteristic, they will surely also cause transformations in other characteristics owing to what he called the “mysterious laws of the correlation of growth.

At Nalanda, here is what we begin with while short-listing businesses: historical return on capital employed (ROCE). The first word first. Historical. I devote an entire chapter to this important and oft- ignored word, but for now I wanted to clarify that the ROCE number is what a business has delivered in the past. We don’t listen to stories about how ROCE will improve in the future. We want to assess a company purely on its historically delivered ROCE. Now let’s look at some definitions. ROCE is simply the operating profit of the business as a percentage of total capital employed. As defined earlier, operating profit is earnings before interest and taxes, or EBIT. Why do we not use profit after tax (PAT) instead? Remember, we want to understand a business’s operating performance, and mixing it with financial measures like tax and interest will muddy the waters. We do not ignore tax or interest charges in our overall evaluation of the business, but for calculating ROCE, we limit ourselves to operating performance. What about total capital employed? This typically comprises two factors: net working capital and net fixed assets. In the net working capital number, we like to exclude excess cash (i.e., cash minus debt if cash happens to be much greater than debt) because extra cash is not an operating asset. Also, high- ROCE companies generate a lot of cash, and incorporating cash into the capital employed number will unnecessarily reduce ROCE. If you are uncomfortable with this, you can include a portion of cash in capital employed. For an acquisitive company, we also include the capital invested in acquiring businesses, but let’s keep things simple for the moment. Thus, ROCE for nonfinancial companies can be defined as follows: EBIT ÷ (net working capital + net fixed assets)

I claimed that Costco at an operating margin of 3 percent is a better business than Tiffany at 19 percent. If we limit our definition of “better” to the level of ROCE, then I was right. This is because Costco’s average ROCE from 2014 to 2019 (pre- pandemic) was 22 percent compared to Tiffany’s 16 percent. Costco is deploying its capital much more effectively than Tiffany, and this more than compensates for Costco’s low margin. Let’s take the example of just one important part of its capital employed: inventory. Costco keeps about 31 days of inventory in its warehouses and retail stores. Guess the same number for Tiffany. It is 521 days, or almost a year and half! Tiffany’s operating margin is impressive, but Costco’s dramatically better management of its inventory and other assets ensures it earns a higher ROCE than Tiffany.

Just because we can’t measure management quality through interviews and discussions does not mean quality management teams do not exist. Of course they do. What we need is not some airyfairy impression of an investor made over a coffee (or a Zoom call) but a quantitative measure. We don’t vote for the best cricket bowler, best running back, or best marathoner based on their interviews or their ability to articulate their excellence, so why should we do it for management teams? The best bowling statistics and the best finish times determine the best bowler and the best marathoner. Similarly, in my view, an excellent— but not the only— indicator of the quality of the management team is their historical track record on the quantity of ROCE.

The median historical ROCE of our portfolio of thirty businesses— most of which are more than thirty- five to forty years old— is about 42 percent. I am obviously biased since I am an investor in these businesses, but I do think the management teams of these businesses are excellent. Are they exemplary, or am I just calling them first rate because they happen to have high ROCE? I don’t know. Does it matter? We should expect the following from a quality management team. That they deliver products and services to their customers that are superior to those of their competitors, allocate capital prudently, attract and retain quality employees, manage their cost structure (which is commensurate with their size and revenue), maintain a quality balance sheet, and continuously innovate by taking calculated risks. All this should— and does— correlate with high ROCE. A Consistently High- ROCE Business Is Likely to Have a Strong Competitive Advantage All long- term investors, mentored by more than five decades of Buffett’s letters and annual meetings, demand that companies have a “sustainable competitive advantage” (SCA). But how does one go about assessing whether a company has an SCA? If you peruse business and investment books, the sources of SCA turn out to be the usual suspects: brand, intellectual property, network effect, economies of scale, and low cost.

Once we have short-listed a company based on its sustained high ROCE, we start analyzing its competitive advantages. After weeks or months of research, we may conclude that this high ROCE is unsustainable and that the company just got lucky historically. So be it. We then choose to stay away. But taking this route— of starting our assessment of competitive advantage only for high- ROCE companies— saves us a lot of time and effort.

A company delivering high ROCE with modest revenue growth will generate excess cash. This is not an opinion— just a mathematical fact. For example, Company X growing its sales at 10 percent with ROCE of 25 percent can grow from zero cash to a cash balance of almost 18 percent of sales in five years (other assumptions: margin 15 percent, tax 30 percent). With an increasing cash cushion, X’s management team can choose to launch new products or target new geography. Even if the new business fails, X can recover given its ability to generate cash from its core business.

After the companies have been through the risk filter, as I discussed in chapter 1, we reject companies with long-term historical ROCE lower than 20 percent. Our preliminary short list of about 150 companies consists only of those that have delivered ROCE of more than 20 percent over the past five to ten years or more.

Remember this chapter is about where you start looking for great businesses, not what guarantees great investment return (spoiler alert: nothing does). The second problem with making a preliminary list of only high- ROCE businesses is that it rejects companies that may become hugely successful in the future. Take Netflix. If we had evaluated Netflix in early 2018, its median ROCE for the previous ten years (2008 to 2017) of 10 percent would have been too low for us to include it on our preliminary list. We would have missed out on a spectacular wealth- creation opportunity: Netflix’s stock price jumped 2.9 times from January 2018 to December 2021. But here is the thing. We would have looked at this lost opportunity and not regretted it one bit. I know we will lose Netflix- like businesses, and I am okay with it. Our strategy of selecting only high- ROCE companies for our initial list invariably excludes some potential winners, but it also excludes hundreds of low- quality businesses that we would never want to own. Thus, on average, I believe this approach works well for us. We will not change our approach just because others have made money with a strategy that we have chosen to avoid. C’est la vie.

Chapter Summary:  Evolutionary theory has taught me that . . . . . . to avoid getting drowned by a deluge of data and information, we can reimagine investing by initially selecting a single business trait that brings with it many favorable business qualities. 1. In nature, selection for just one trait can influence many other behavioral and physical qualities of an organism. 2. Dmitri Belyaev and Lyudmila Trut’s long-term experiment in Siberia has shown that selecting for tameness in wild silver foxes transforms them into a creature not unlike a pet dog over very few generations. The foxes become docile and crave human attention. They also develop floppy ears, a piebald coloration, and a shorter snout and can be reproductively active more than once a year. 3. Investors could benefit immensely by homing in on a business trait that, when selected, brings along many other favorable qualities with it. Some popular parameters like management quality, high growth, and high margins are inappropriate or inadequate. 4. The single business quality that correlates favorably with many other areas of business excellence is historical return on capital employed (ROCE). We start our analysis by selecting only those businesses that have historically delivered high ROCE. 5. High ROCE generally (but not necessarily) indicates that the management team is stellar, they allocate capital effectively, they have built a strong competitive advantage over their peers, and they have room to innovate and grow. 6. Selecting for ROCE is a good starting point of analysis. It helps us narrow down our choices. We do a lot more work to create a short list of attractive businesses after this initial filter. 7. However, not all businesses with high historical ROCE will necessarily continue to be considered good businesses. There are no guarantees in investing.

Living Organisms Are Highly Robust MBA degrees, management seminars, best-selling business books, and corporate titans all seem focused on ensuring that companies adapt to change and evolve into a better version of themselves. If one could bottle up corporate obsession, the label on this bottle would declare, “How do we change faster, better, and easier?” I beg to differ. The question that should be on the minds of business leaders and investors is almost the exact opposite: How do we change without changing?

Something similar has been happening at McKinsey over the past century. Today’s McKinsey bears no resemblance to Marvin Bower’s McKinsey in terms of geographical presence, organization processes, type of client work, and breadth of expertise. But at some fundamental level of culture, oneness, problem- solving, and working with CXOs, the firm has remained stubbornly Boweresque. It has changed without changing. This is what we seek as owners in our businesses: the ability to keep evolving while staying robust.

Let’s recap our journey until now. We have eliminated serious risks (chapter 1) and have shortlisted businesses based on ROCE (chapter 2). Now we need to select companies for their robustness. Here are some learnings we have internalized at Nalanda.

You will notice that, unlike in chapter 2, where we used the quantitative criterion of ROCE to select businesses, many factors contributing to robustness are qualitative.

Different investors attribute different weights to the factors listed in table 3.1. For instance, many investors do not consider customer concentration to be a problem for a business. We do. We took advantage of these contrasting opinions a few months after the inception of Nalanda.

We Assess Evolvability Indirectly by Measuring Robustness Directl.As permanent owners, we Nalanda folks are hungry for companies that can successfully implement neutral strategies to evolve and adapt to a changing environment. We want evolvability. Correction. We need evolvability. Having invested, we need the business to survive the onslaught of AI or other technologies, to upstage its increasing online and offline competition, to withstand multiple recessions, to conquer the adverse effects of climate change, to survive management turnover, and much more. We need it to be able to adapt.

Many investors contend that management interviews and discussions are an excellent way to assess the future adaptability of a business. Maybe. I consider such interviews a waste of time— but more on that later (in chapter 7). But there is an indirect— and I would argue a reasonably reliable— path of satisfying my need. It is by measuring the robustness of an organization. Robustness lays the groundwork for evolution in living things. It does the same in businesses. Robustness is a necessary— though not sufficient— requirement for businesses to evolve successfully. In a robust business, just as in a living organism, evolvability comes free.

What I have outlined is not a theoretical construct. We have seen this story play out in many of our businesses. When the pandemic began, the general opinion was that all Indian businesses would suffer. Sharp stock market declines in March and April 2020 mirrored this opinion. However, as the months passed, the differences in the impact on the business of companies with differential degrees of robustness became quite stark. During and after the COVID crisis, we have seen these divergent outcomes play out across many of our companies and industries, be it paint, innerwear, air conditioners, tires, pipes, or batteries.
There is an unbroken chain of life between us and our last universal common ancestor (scientists call it LUCA) 3.5 billion years ago. 12 Every part of this unbroken and evolving lineage has had robustness at multiple levels: genes, proteins, and body plan, to name just three. In a not dissimilar manner, I have discovered over more than two decades of investing that more levels of robustness lead to more evolvability.

We want our companies to tilt toward the left side of table 3.1 across all factors. Thus, we want the business to be robust at the level of ROCE and concentration of customer base and degree of leverage and strength of competitive advantage, and so on. Is this asking for a lot? You bet. We are permanent owners— the key word here being “permanent.” If a business can’t last permanently, we don’t want to own it. Without several levels of robustness, how will we be sure that the business will survive over the long term? I wish I could categorically answer the question, Is this a robust business? As a practicing investor, I know that assessing robustness is a matter of judgment and that there is no shortcut to this process. I have arrived at a certain heuristic after many years of investing. There clearly are black- and- white extremes to robustness (e.g., a business with just two customers) but in many, maybe even most, businesses, it is the gray zone that stares at us.

For example, is a company with a debt/ equity ratio of 2.0 robust? Probably not. What about a ratio of 0.2 or 0.5? Maybe. For me, the answer depends on the other factors of robustness. Almost all the companies in our portfolio are debt free. Still, in 2010 we invested in India’s leading plastic pipes business (used in homes and agriculture), Supreme Industries, whose debt/ EBITDA ratio was 0.6. Not high, but not zero either. Despite the company having debt— which was small to begin with— we concluded that the company was robust because it was the clear industry leader, had been gaining market share over its competitors, had a return on capital of more than 30 percent, had successfully designed and launched many products over the past decade, had thousands of distribution points across India, was able to negotiate the best terms with its suppliers, and had not wasted time and money on unnecessary acquisitions. It was not perfectly robust, but it was resilient enough. Today, the company is debt free and continues to be the industry leader by a wide margin. The more levels of robustness, the more we salivate.

There are millions of small businesses across the world that are incredibly robust but will stay small. And then there are businesses that test the limits of robustness and implode. The former risk nothing, and the latter risk everything. The Goldilocks zone between these two extremes is what I call “calculated risk.” It is the degree of risk that makes managers uncomfortable, but not too much; it compels the organization to innovate, but not too much; it forces the business to invest, but not too much; and it adds areas of potential growth, but not too many. To me, the company that best demonstrates calculated aggression and risk- taking is Walmart. Small digression. Let’s see if you know the answer to this question: What was Sam Walton’s age when he founded Walmart? If your answer begins with a one or a two, your answer was the same as mine. And like I was, you are wrong. Sam Walton was forty- four when he opened his first Wal- Mart store in 1962 in Rogers, Arkansas (Wal-Mart became Walmart in 2018).  Not all the great founders of the modern era are from Silicon Valley, not all of them were hard- charging teenagers, and not all of them wanted to “change the world.” After graduating from college, Walton started in sales at J. C. Penney in 1940, then enlisted for the war in 1942. In 1945, he started managing a franchise Ben Franklin store in Newport, Arkansas (where the population was then seven thousand). By 1950 he was running two stores in Newport and had achieved reasonable success by experimenting and innovating. One of his innovative ideas that had been a massive hit with his customers was an ice cream machine. As he writes in his autobiography, “Every crazy thing we tried hadn’t turned out as well as the ice cream machine, of course, but we hadn’t made any mistakes we couldn’t correct quickly, none so big that they threatened the business.” Could there be a better definition of calculated risk? After Walmart’s initial success in Rogers, Sam Walton opened more stores. 14 By 1967, he had opened twenty- four stores, which brought in sales of about $ 13 million. The company reached $ 1 billion in sales in 1980, by which time it had 276 stores and about 21,000 associates. Note that store openings and selling more products through the same stores propelled growth from 1967 to 1980. During these thirteen years, sales per store had increased about seven times. How had Walton done this? By continuously trying new things, expanding product offerings, and broadening the customer base.

The company did something similar—small, measured, and low risk—when it launched its web business. In 2000, Walmart joined hands with a leading Silicon Valley investment firm, Accel Partners, to launch Walmart.com. Accel, by the way, gained much fame (and a gargantuan fortune) as a result of its $12.7 million investment in an early-stage company called Facebook in 2005. In about eighteen months, in mid-2001, Walmart acquired the minority stake of Accel to own Walmart.com fully. By 2020, Walmart’s e-commerce sales had climbed to $24 billion and Walmart.com was approaching 10 percent of Walmart’s overall U.S. sales. What had started as a “neutral” strategy in 2000 is now fast becoming the centerpiece of Walmart’s approach to gaining market share. One of our largest investors is a well-known U.S. university endowment. They have been investing in funds globally for many decades. Their CFO visited our Singapore office in 2011. After he had finished grilling us on compliance and other related matters, I wanted to know if he could share any learnings with us. He said that the one industry in which their fund managers had consistently lost money across time and geographies was retailing. In this context, Walmart’s success is awe inspiring.

I don’t understand product marketing, but thankfully Page [an Indian company he owns] does.

Robustness Is a Proxy for Evolutionary and Business Success but Doesn’t Guarantee It: Dinosaurs, a diverse group of more than a thousand reptilian species, dominated our planet for 180 million years. 17 As a matter of comparison, we Homo sapiens have been around for less than 0.2 million years. Dinosaurs couldn’t have survived and thrived for so long unless they were highly robust and adaptable. Molecular evidence has shown that many modern mammalian orders— Carnivora, Primata, Proboscidea— coexisted with dinosaurs for at least 30 million years during the Cretaceous period (145 to 66 million years ago), and maybe even earlier. The mammals during the era of dinosaurs were small, squirrel sized, and probably insectivores. If aliens had landed on our planet 65 million years ago, they never could have predicted that a small offshoot of the insignificant mammalians would reign supreme one day. The cataclysmic aftermath of an asteroid strike in the Yucatan peninsula 65 million years ago wiped out the dinosaurs. But the mammals survived. No one is sure why. The extraordinary robustness of dinosaurs did not guarantee their evolvability. In general, the greater the robustness, the greater the evolvability. But sometimes, robustness ceases to help businesses adapt. We can see this in Gap’s failure to grow despite the company being very robust. Its revenue stayed flat at about $ 16 billion from 2005 to 2020, although it delivered ROCE of 20 percent or more for more than a decade and had no leverage. In general, multiple levels of robustness are better than a single level. But sometimes, even multiple levels of robustness can’t safeguard the future of a business, as has been the case for thousands of newspapers across the world. In general, highly robust businesses evolve by taking calculated risks. But sometimes, very rarely, businesses can succeed by taking huge risks, as shown by Netflix. We at Nalanda never bet against the odds. And so, despite some rare counterexamples, we have kept and will continue to keep robustness at the front and center of our investment approach. As permanent owners, we seek robustness in companies as the best available benchmark to assess if they are likely to adapt and survive over the long term. A better measure may exist, but I don’t know what it is. We invest only in highly robust companies. Many of them have stayed robust and have grown their sales and profits over decades. But our track record is not perfect. We have witnessed two key problems with this approach. First, a business can lose its robustness.

Being a permanent owner, we are tolerant of declining sales or margins or market share. But we will not risk survivability. As the company’s robustness nose- dived, we exited the business at a loss.
The second problem is too much robustness.

We have made almost forty investments to date, and in all of them, robustness was a primary— but not the only— selection criterion. We have worked on the assumption that robustness will lead to growth and evolution. This assumption has failed us on two occasions: one in which the company lost robustness and another in which the company’s excessive focus on robustness compromised growth. I am surprised at our strategy’s low failure rate. We have been quite lucky, and while robustness should continue to reward us across our portfolio, we will continue to encounter failures of the first or second kind over time.
“Confronted with a like challenge to distill the secret of sound investment into three words, we venture the motto, MARGIN OF SAFETY” (emphasis in the original text). This is the best advice for investors in the best chapter of the best investing book ever written: The Intelligent Investor by Benjamin Graham, Buffett’s actual and spiritual mentor. The chapter is titled “ ‘Margin of Safety’ as the Central Concept of Investment.” Graham knew that the corporate world is highly uncertain and that the best protection offered to an investor is the price they pay for a business.

In this chapter, I have used the word “robust” to extend the margin-of-safety concept to many other facets of a company. We have sought a margin of safety on business quality by demanding high ROCE and a wide competitive moat, on the strength of the balance sheet by requiring it to be debt free, on the bargaining power of customers and suppliers by requiring them to be fragmented, and on the sustainability of economics by insisting that the industry be slow- changing.

The COVID-19 pandemic has severely impacted what had appeared to be highly robust hotel chains; Intel’s erstwhile dominance in semiconductor chips has been upset by the likes of AMD, Nvidia, and Samsung; Amazon has already destroyed many small and large retail businesses; regulators in the United States and Europe appear to be threatening the very existence of Google and Facebook in their current form[AA Note: I disagree.  As the technology cold war heats up, Google becomes more important.]

We know that given the nature of the businesses we are after— those with very low risk and exceptional business quality— they will almost never be available cheap. The market is not an idiot; it is almost always efficient. Almost always. Not always. We wait for those few occasions to pay what we call a “fair” price. Not too low but not too high either. What is “fair”? Rather than describe it, let me state the actual number. The median trailing twelve- month (TTM) entry PE ratio for the Nalanda portfolio is 14.9. The median TTM PE for the period from 2005 to 2020 for India’s primary index, Sensex, is 19.7 and for the Midcap Index is 23.8. Thus, we are buying what we think are exceptional businesses at a 25 to 30 percent discount on the index. Over almost a quarter of a century of investing, I know I have been wrong on many occasions. The margin of safety of our entry price pays for my errors of judgment.
A Leader Is Made a Loser Lazarus of Bethany was miraculously brought back to life by Jesus in the New Testament. Charles Lazarus performed a modern capitalist miracle by making Toys “R” Us the largest and most well- respected toy business globally. Lazarus is also a beggar in a parable in the Gospel according to Luke, the third of the four Gospels of the New Testament. Once a miracle of Lazarus, Toys “R” Us suffered the same fate as Lazarus the beggar.

In 1988, the Wall Street Journal boldly predicted, “Toys ‘R’ Us, Big Kid on the Block, Won’t Stop Growing.” As if on cue, the problems started. In 1988, Walmart’s market share at 17.4 percent came marginally ahead of that of Toys “R” Us at 16.8 percent. Toys “R” Us was in second place after being the leader for fifteen years. Toys “R” Us was being squeezed at both ends: by discount chains like Walmart, Target, and Costco, which competed on low prices, and by so- called edutainment companies like Zany Brainy, Noodle Kidoodle, and Imaginarium, which offered higher- priced specialized toys and better service.
Too often, a business in trouble tries to buy its way out. Toys “R” Us was no exception. It acquired Imaginarium Toy Centers in 1998. It also tried frequent management changes— the company had three CEOs from 1994 to 2000. But its downward slide continued with Amazon, too, muscling its way into the toy segment.

Toys “R” Us was spectacularly successful for about four decades, from the late 1950s to the late 1990s. By the mid-2000s, however, it wasn’t growing, its market share was declining, and its profitability had taken a severe beating. Whatever the reasons for its trouble, there was no doubt that it was in trouble. The company’s robustness had suffered significantly. Maybe it was bad luck, maybe it was management missteps, or maybe it was a bit of both. One way to think about this situation is to picture an elite marathoner whose performance has dipped in recent months. They used to be a picture of health and vigor, but nowadays, they look exhausted and cannot run even ten kilometers at their earlier marathon pace. They are now at the starting line of the Boston Marathon. What would you expect their coach to do before the race starts? If I were the coach, I would advise them to withdraw from the race, rest and recover for a few months, and slowly build their mileage back up. Maybe your advice would be different—you might counsel them to take it easy and just finish the race without worrying about a podium finish to minimize damage to the body. I assume you would be shocked if I told you that the coach not only asked them to run at full speed but also loaded a ten-pound bag on their back!

Remember that the capitalist world is a Boston Marathon that never ends— there is no respite at the end of a punishing two- hour race. The race goes on and on and on and on and on: 24 hours × 7 days a week × 365 days a year. It’s unending, unrelenting, unforgiving.
Dear Amazon, you have opened physical book shops in Manhattan. Time for toys?

Chapter Summary: Evolutionary theory has taught me that . . . . . . we can reimagine investing by owning only robust businesses that are resilient to internal and external shocks, while continuing to evolve and grow. 1. There is a paradox in the living world: Organic life is highly complex but not fragile. Organisms have survived hundreds of millions of years despite living in constantly changing external environments and undergoing a barrage of internal mutations. This is because they are robust at multiple levels. 2. Thus, an accidental change in DNA sequence does not affect which amino acids are made; a change in amino acids or their sequence does not impact the synthesis of proteins; and a change in proteins need not affect the body plan of an organism. 3. Neutral mutations permit new functions and adaptations to arise without disrupting current functioning. 4. We want our businesses to mimic the robustness of the living world: to survive and prosper in a dynamic external environment, withstand internal strategic and organizational upheavals, and evolve by taking calculated risks. 5. Hence, we choose to invest only in businesses that are robust at multiple levels. A robust business has high ROCE, minimal or zero debt, a strong competitive advantage, fragmented customer and supplier bases, a stable management team, and is in a slow-changing industry. 6. Just because a business is robust today does not mean it will continue to be so. Our only protection against the loss of robustness of a business is to be price sensitive. We do not invest unless the market offers us an attractive valuation, which happens rarely.

You are going bald. Not because of age, but because you have been tearing your hair out. A few months ago, you committed to investing $100,000 with a money manager. This fund manager did not want to raise cash and said that he would call commitments if he saw attractive opportunities to invest in the market. He made a strong pitch with a snazzy PowerPoint presentation, and, like everyone in the industry, he promised to be long- term oriented. He also claimed that he does not like to lose money (as if everyone else does!) and that he carefully assesses the quality of businesses and buys when everyone else is selling. Very Buffettesque. Very old world. You have heard the same spiel from everyone, but he seems quite sincere. Or at least he put on a good act. A few quarters have passed, and he has called $ 40,000 from you. At the end of the quarter, the value of your $ 40,000 investment is $ 38,000. You know that the market has been a bit weak lately, so you ignore the minor loss. You are a patient sort and don’t fret over the value of your investments daily like many of your friends. But when you check your investments next quarter, you see that the value of your portfolio is now only $ 32,000. You have taken a hit of 15 percent in a single quarter. To rub salt into your wounds, the fund manager demands $ 15,000 more from you. He is entitled to; you have signed an agreement with him that commits you to keep investing until your $ 100,000 limit is reached. You invest the additional $ 15,000. One more quarter goes by, and you are aghast to discover that the value of your $ 55,000 is now only $ 39,000. You are down 30 percent within six months! The fund manager repeats the same mantra, “We are long term, patient, blah . . . blah . . . blah,” while asking for an additional $ 15,000! You talk to some of your friends who appear to be stock market experts. All of them advise you to stop investing more and to withdraw your remaining capital promptly. Unfortunately, your lawyer advises you that there is no way out. Not only can you not redeem your capital (because you have agreed to a lock- up for many years), but you must honor your commitment. With great reluctance, you invest an additional $ 15,000. Your total investment is now $70,000. You have read Buffett’s great annual letters and listened to his interviews on TV. You remember that he advises investors not to check their stock holdings obsessively. Hence, this time, you decide to wait six months before reviewing how your portfolio has performed. Kudos for your patience! The day finally arrives. You haven’t felt this nervous since the first time you used a fake ID to enter a bar. You open your account statement with increasing dread. Your worst fears are realized. Your $70,000 investment has now almost halved to $36,000. Your portfolio has lost about 40 percent during the past six months. Oh, and your money manager is threatening to ask for more money. Apart from continuing to tear at your remaining hair, what should you do? The horns of dung beetles. Maybe they have the answer.

We want the businesses we own to increase in value over the long run. And the only way for this to happen is for the company to perform well over many years, preferably decades. Investment success may not correlate with business success for a day trader or short- term investor. But for us, the ultimate success of an investment is almost entirely dependent on the ultimate success of the business. If you accept this premise, then as permanent owners, we should focus exclusively on the quality and performance of the business over the long run. And that is what we do. However, this is easier said than done. In today’s world of Facebook, Instagram, Reddit, Twitter, WhatsApp, and other soul- destroying inventions, it is not easy to escape the din of proximate noise that can drown out the desire to seek sources of ultimate success. When there is a specter of a Greek default, an announcement of reduced jobs growth in the United States, OPEC negotiations break down, the Federal Reserve hints that the days of low interest rates are over, or company revenue falls, stocks can fall. Similarly, a bullish projection by the International Monetary Fund (IMF) on world growth, the recapitalization of banks in China, the success of a new product launch, or the increased pace of vaccination for a global pandemic can lead to higher stock prices. All these are proximate causes of price movement. And all are divorced from what could ultimately lead to the business success or failure— and hence a higher or lower market value— of a company. The question we investors don’t ask often enough but should is relatively straightforward: Does this proximate cause have anything to do with the cause of ultimate business success? The interesting thing about proximate causes is that they are almost always evident in screaming newspaper headlines and hyperventilating news anchors. Ultimate causes, thankfully, are way too dull for media coverage. Why “thankfully”? I will get to it.

If you want to lash out at the fund management community for being impulsive and trigger-happy, go ahead. But please don’t blame us for being inconsistent. Whenever the markets get excited about a macroeconomic event, the fund managers behave predictably by riding the proximate bandwagon of stock prices. And then they repent at leisure. Fund managers have a Pavlovian reaction to macro or market data. Will interest rates be higher? Sell. Will inflation be lower? Buy. If the fiscal deficit shoots up? Sell. Or is it a buy? Most businesses should be (and are) relatively immune to short- term macro movements. As shown in table 4.1, their stock prices, for some inexplicable reasons, are not.

I am not cherry-picking here. Over the long run, well- run businesses create a lot of value irrespective of the macroeconomic environment. Do we seriously think Amazon, JPMorgan, Michelin, Nestlé, Siemens, Tesco, Walmart, Zara, and other excellent businesses are held hostage to inflation and fiscal deficit? If the business and stock price performance of exceptional companies is immune to macroeconomic perturbations, aren’t we, as investors in those companies, better off ignoring the economy?

The third problem with using economic data is the most obvious of all. No one knows anything. Okay, that is exaggerating a bit. But only a bit. Since even expert economists are abysmal at forecasting the economy, why should we investors squander our time giving it any importance? I assume you will agree that an economist’s most important task is to forecast a recession. This would enable the government to take the necessary steps to prevent widespread pain and suffering. In March 2018, the IMF published a working paper titled “How Well Do Economists Forecast Recessions?” 9 The authors compared real GDP forecasts with actual growth data for sixty- three countries from 1992 to 2014. They showed that while GDP contracted by an average of 2.8 percentage points during recessions, the consensus forecast from the year before the recession was a growth of 3 percent! Worse, even during the year of the recession, the average forecast was a contraction of 0.8 percent when the real contraction turned out to be 2.8 percent. Prakash Loungani, one of the authors of this IMF paper, told The Guardian in an interview that according to his analysis, economists had failed to predict 148 of the last 150 recessions! “The record of failure to predict recessions is virtually unblemished,” he said. One would have thought that with a greater volume of data, more computing power, and better algorithms, our ability to forecast would have improved over the years. Yeah, right. In the same Guardian article, Mark Pearson, the deputy director for employment, labour and social affairs at the Organisation for Economic Co- operation and Development in Paris, said, “We are getting worse at making forecasts because the world is getting more complicated.” Way to go, Mark. I know that many investors spend a lot of time poring over economic data. Maybe they have figured out a way to factor in exchange rate movement or the external debt levels of the country in their decision- making. I am unable to do so. I do not know how to translate any economic indicator into the prospects of a specific business. We ignore every piece of proximate macroeconomic information. We do not believe these data help us assess the ultimate success or failure of a business. We have no economic advisers, we do not talk to economists at banks or brokerage houses, and we do not discuss any economic indicators in our team meetings. Their weightage in our investment decision is a big zero.

I don’t fraternize with folks in the financial services industry to exchange ideas or information. However, when I started my investing career with Warburg Pincus in 1998, I spent a fair bit of time with fund managers and finance professionals connected with the Indian equity markets. I had a consulting background and wanted to understand the workings of the capital market and its participants. I thought I had a lot to learn. I was right, just not in the way I had imagined. After just a couple months at Warburg, I could predict the exact words of a finance industry professional’s greeting. It wasn’t “How are you?” or “How’s it going?” or just “Hello.” It would almost always be “Kya lagta hai?” Translated from Hindi, in stock market parlance, this means “What do you think the market will do?” I remember being confused. Here I was, a novice trying to understand how the markets work, while this “expert” was seeking my opinion? Do they not know? It took me a while to conclude that they don’t. No one does.

But there are many other occasions when the market moves because, well, it moves. Such is the nature of markets. The proximate causes of market movements are unknown, and in my view, unknowable.

Why did fund managers in the United Kingdom start piling on to BP, Rolls- Royce, and Diageo on July 25 after a huge increase in the U.S. indices on July 24? The explanation that comes to mind was offered by the great John Maynard Keynes when he opined that the stock market players were playing a complex guessing game. 11 He asked us to imagine a game in which competitors pick the six prettiest faces from one hundred photographs. The winner is not the one who picks the prettiest faces but whose choices match the average of all the competitors.

Keynes had learned from bitter experience that this market guessing game is a colossal waste of time. In the 1920s, he used a detailed economic model to predict market levels and failed to see the Great Crash of 1929. He also underperformed the market during this period. He switched to picking stocks and, like Buffett, eschewed diversification. He declared, “The right method of investment is to put fairly large sums of money into enterprises one thinks one knows something about.” No wonder he turned out to be an excellent investor. Keynes managed the endowment of King’s College, Cambridge, from 1924 to 1946. During this twenty- two- year period, he compounded the college’s wealth by almost 14 percent a year. If someone had invested £ 100 with Keynes at the start of 1924, it would have been worth about £ 1,675 at the time of his death in 1946. The same money invested in the UK stock market index would have been worth only £ 424. Astonishingly, this period included the Great Crash of 1929, the Great Depression, and the Second World War.

I have not met a single finance professional who claims that markets can be predicted. So why do industry players spend so much time obsessing over future market levels? Why do fund managers devote enormous time and effort obsessing over what other fund managers think and do? Flawed incentives, false comfort, one- upmanship, you name it. It doesn’t really matter. We ignore all market forecasts. Well, maybe not entirely. I do look at them on days when I want to have a good laugh. 

Here is my question for you: What do you think Nikola’s market value was at the end of December 2020, three months after the Hindenburg exposé? Remember that the company had no battery or fuel cell technology, had no prototype, its founder had exited ignominiously, GM had terminated the partnership, and the government had begun investigating fraud. My answer would be close to zero. Nope. It was $ 6 billion! The Hindenburg report appeared to be fact based: They backed up almost all their assertions with documents, photographs, text messages, videos, and interviews. I am in no position to double- check their research. However, the facts that Milton resigned and GM terminated the partnership seem to indicate that a reasonable portion— if not all— of the report was credible. If so,how does one explain a $ 6 billion valuation for a company like Nikola? That’s an unfair question because I doubt anyone fully understands how companies are valued. But in this case, I want to offer a two- word answer. Thematic investing.

A foolproof method of checking the interest level in a concept or theme is to analyze Google searches using Google Trends. If you do so for the term “electric vehicles” between January 2014 and January 2019 in the United States, you will see a relatively flat trend. However, between January 2019 and February 2021, interest in electric vehicles quadrupled.

Like almost every proximate theme before and since, the automotive tech theme has three properties. It hypes up total addressable market (TAM), is simple to understand, and is actionable. First, TAM. Every theme I have encountered since the start of my investing career in 1998 plays on the enormous size of the addressable market. And the size is usually so large that it dwarfs the businesses currently operating in that industry or theme. No wonder it is usually the most salient proximate cause of a theme gone wild.

In my experience, there is only one problem with chasing a proximate theme based on TAM. It’s useless. It makes astrology- based forecasts look respectable. TAM is pointless because it does not tell us whether any profits will be made, and even if a business can be profitable, TAM is silent on who will make that moolah.

The second reason for the seductiveness of a proximate theme is its simplicity. Even a casual reader of business news will be aware of themes like e-commerce, renewable energy, electric vehicles, fintech, food delivery, artificial intelligence, self- driving cars, infrastructure, and biotech. Unlike economic forecasting, which is full of jargon like “GDP” and “monetary supply,” a layperson can relate to themes.

How should we separate the proximate causes from the ultimate ones when there is euphoria or bearishness in a theme? Unfortunately, I am not aware of a foolproof method for doing so. But here is what we do. We define our unit of analysis clearly as the company. Not the economy, not the market, not a theme. We care about the fundamentals of the company— nothing else. We have never invested in a theme and never will.

As I have discussed in this chapter, we ignore proximate problems related to the economy, the market, and even the industry. But the dilemma is much trickier to address when the proximate cause of problems relates to the company itself. Suppose the sales growth and profitability of the company has declined in the past few quarters, whereas its main competitors showed no such struggle. How would you decide if the performance issues are related to proximate (and hence temporary) causes or ultimate (and hence more permanent) causes? In my experience, developing a method and an instinct to separate proximate and ultimate causes of failure or success when they relate to a company event is invaluable for a long- term investor. I have been an investor for more than two decades, and this is where I stumble most often. As usual, at the extremes, the decision is straightforward. If the share price declines owing to a downturn in one or two quarters, we ignore the decline, considering it a proximate event. But if the decline results from a loss of market share for three years in a row, we ask if there is something fundamentally wrong with the business. It is the gray area in between these extremes that creates the worst headaches for us. I do not know of any foolproof method of cracking the conundrum; the answer is almost always very company specific.

The Pain and the Gain of Headline Harassment: Let’s go back to the question I raised at the beginning of the chapter. You have sunk  70,000 in a fund that is now worth $ 36,000. Everything the fund manager touches seems to be heading down. Apart from continuing to tear your hair out, what should you do? Nothing. At the end of the anecdote, the time was March 2009. If you had done nothing and continued to hold the fund, your $ 36,000 would be worth a little more than $770,000 at the end of September 2022. Which is a multiple of 21.4 times over 13.5 years. In comparison, the main stock index grew six-fold during these years.As you may have guessed, this was not a hypothetical situation. I have described what transpired at Nalanda. What you see in the numbers I’ve given is the result of our aggressive buying during the global financial crisis of 2008 and its dramatic longer- term impact on the fund’s performance. The only change you would need to make is to switch dollars to rupees. 17 Your patience would have paid. A lot. As the Indian market started falling from March 2008, we started buying high- quality businesses, and we did not stop until early 2009. The further the market fell, the greater our buying frenzy was. In December 2008, the fund had delivered an annualized return (called the internal rate of return, or IRR, in investing parlance) of negative 55 percent (!), and we continued to invest as much as we could. The fund’s annualized rupee return as of September 2022 was 20.3 percent (after payment of all fees and expenses). What allowed us to invest when the world seemed to be coming to an end? We ignored all proximate causes of stock price decline and focused exclusively on the ultimate sources of success of a business.

It is common knowledge that lousy news attracts way more eyeballs than good news. We may blame the media for this bias, but psychologists have shown that people prefer reading bad news and remember it better. The media simply exploit an existing prejudice. In an article titled “On Wildebeests and Humans: The Preferential Detection of Negative Stimuli” in the journal Psychological Science, researchers showed that subjects remembered negative words faster and more often than positive ones.

In sharp contrast, there was no celebration of business as usual at the high-quality companies that were becoming part of our portfolio during this period. No headlines screamed, “WNS Processes Another Mortgage Application,”“Triveni’s Factory Manufactures Turbine Number 39 for the Year,”“Page Industries Adds Two More Retailers Today in the City of Aurangabad,” or “Carborundum Factory in Chennai Finishes Another Shift.” Earlier I wrote, “Ultimate causes, thankfully, are way too dull for media coverage.” Now you know why. There is one more important reason we could embrace a diametrically opposite attitude to that of many of our peers in 2008. We are fortunate to have long-term investors—primarily U.S. university endowments and U.S. and European family offices—who have supported our aggression when the world seemed to be coming to an end. Not even one investor defaulted on their commitment. No one (I hope) tore at their hair! I know that many private equity and hedge funds could not persuade their investors to commit more capital in 2008. We were very fortunate.

Chapter Summary Evolutionary theory has taught me that . . . . . . we can reimagine investing by ignoring proximate causes of stock price movements while focusing on ultimate explanations of business success. 1. Evolutionary biology explores natural phenomena by searching for proximate and ultimate causes. Proximate mechanisms explain immediate influences on a trait. The role played by natural selection explains the ultimate cause of an organism’s success or failure in an environment. 2. Thus, to understand the impressive size and variety of dung beetle horns, evolutionary biologists ask the proximate question (e.g., which network of genes was switched on?), as well as the ultimate question (e.g., what is the adaptive value of the horns?). Scientists understand that these are different types of questions with different types of answers and that both types must be asked. 3. The investing world, too, must differentiate between proximate and ultimate causes. Proximate causes of share price changes can result from the macroeconomy, the markets, the industry, or the company itself. Since proximate causes are highly salient (e.g., the Fed announcing an interest rate cut or a company announcing a slowing of sales growth), investors may erroneously overweight them in their decision-making process. 4. We ignore all proximate causes when analyzing businesses. We focus exclusively on the business fundamentals, or the ultimate causes of the success or failure of businesses. 5. We were aggressive investors during the financial crisis of 2008 and the early days of the COVID- 19 pandemic because proximate worries compelled the markets to overlook the ultimate causes of the success of many high- quality businesses.

An Overlooked Reason for the Underperformance of Fund Managers: We encountered two harsh realities in the introduction to this book: About 90 percent of fund managers cannot beat the market, and their performance has worsened over time. Why do fund managers underperform? Talk to a dozen insiders, and you will get a dozen different reasons for this sorry state. One oft-repeated complaint is the misalignment of incentives for the fund manager. The fund management company gets paid based on the size of the fund, not on its performance. But over the long term, many researchers have found that an increase in fund size can lead to declining performance. For example, in a study published in 2009 in the Journal of Financial and Quantitative Analysis, an analysis of actively managed funds in the United States from 1993 to 2002 demonstrated a “significant inverse relation between fund size and fund performance.”  Similarly, in a 1996 article in the journal Financial Services Review, the authors write, “Once large, equity funds do not outperform their peers.”  They go on to advise investors to invest in smaller funds.

What you will not find in these articles is the crux of this chapter. I believe a crucial reason for the continued underperformance of fund managers is their focus on future rewards while ignoring the treasures of the past. We at Nalanda pursue the profession of investing the same way evolutionary biologists do: We interpret the present in the context of history. Evolutionary biology does not make predictions as physics and chemistry do. Nor do we. Instead, our investment approach attempts to explain the present by interpreting what occurred in the past. In an essay on the theory of evolution, the late Harvard paleontologist Stephen Jay Gould wrote, “The present becomes relevant, and the past, therefore, becomes scientific, only if we can sum the small effects of present processes to produce observed results.” 4 He could have been writing about the way we invest.

Darwin wrote more than twenty-five books, and hundreds more have been written about him and his oeuvre. We can’t cover even a fraction of his genius here. In this chapter, I want to focus on only one aspect of his method, which is evident in his groundbreaking book On the Origin of Species: his focus on historical information to make deductions about ongoing evolutionary processes.

In my layperson’s view, the reason Darwin’s crowning achievement— the theory of natural selection— was not discovered earlier and remained unaccepted by many stalwarts during and after his lifetime was that few understood the powerful effect of small changes accumulated over very long periods of time. But for those who understood the relevance of history, the theory was so powerful and straightforward that the famous biologist Thomas Huxley remarked, “How extremely stupid not to have thought of that.”

Chapter 14 of Origin presents copious evidence to bolster his claim that most species have evolved from very few common ancestors. He called this phenomenon “descent with modification.” He starts by pointing out the obvious: Organic beings are nested within groups. 19 The hierarchy levels, in ascending order, are as follows: species, genus, family, order, class, phylum, and kingdom. Thus, dogs are the species Canis familiaris and belong to the genus Canis. When grouped with wolves and jackals, they belong to the family Canidae. When Canidae is grouped with other families like Felidae (cats), Ursidae (bears), Mustelidae (weasels), and many others, we get to the order Carnivora. Carnivora brackets along Cetacea (whales and dolphins), Perissodactyla (horses, tapirs), Sirenia (dugongs), Lagomorpha (rabbits), and others to form the class Mammalia. Mammalia, Amphibia, and other classes merge to form the phylum Chordata. Chordates, mollusks, nematodes, and numerous other phyla cluster to create the kingdom Animalia. Carolus Linnaeus, the Swedish botanist, laid the groundwork for this classification system in 1735 in Systema Naturae (The System of Nature).

Darwin was right, of course. Scientists have concluded that our last universal common ancestor (LUCA) arose somewhere between 3.5 and 4 billion years ago. LUCA then gave rise to the six significant kingdoms of life: animals, plants, fungi, protists, eubacteria, and archaea. Although Darwin wasn’t aware of four of these six kingdoms, I find it staggering that he still arrived at the correct conclusion. What a genius.

We see the same set of historical facts as everyone else. We have no interest in forecasting the future. We study the history of a business to understand its financials, assess its strategies, gauge its competitive position, and finally assign value to it. So let’s take them one by one.

[I recall] one of our portfolio companies a few years ago. We met with the CEO and CFO for about an hour or so. As we were about to depart, the CFO received a call on his mobile phone, got visibly upset at the caller, and exclaimed, “I can’t say anything; the results will be out after a few weeks.” It turns out that the caller was a well-known investor who was checking to see how the quarter was progressing on the revenue and profit front. If investors hound the company management for following quarter results, wouldn’t they do the same with research analysts? So why should the analysts bother with longer- term history? Analysts produce forecasts because their clients demand they produce forecasts. I am sure many of them know it is a futile exercise. Here is why. Let’s say I need to project the following year’s financials. I will need to forecast at least ten (if not more) numbers ranging from units sold, price per unit, cost of goods sold, sales expenses, receivables, capital expenditure, and so on. Let’s assume that I am a great guesser and that I will correctly guess each of the ten numbers with a 90 percent probability. Hence, the chance of guessing all ten numbers correctly for next year would be only 35 percent (0.9010). One may quibble that not all ten are independent variables, so we should not multiply them. True, but the number of variables is much greater than ten, and they are all at least semi- independent. Whichever way you evaluate the probability of guessing the next year’s financials correctly, it is probably worse than guessing heads or tails after tossing a coin. But this was only for next year. I also need to project for the following year and the year after that. How accurate do you think my estimates will be? The only financials we prepare are for the past decade or more. Our financial trackers have no projections. Instead, we use the same factual financial information to which everyone else has access. Not unlike Darwin.

If we don’t forecast financials, what do we do with historical numbers? A lot. As permanent owners, we are incredibly paranoid about the financial performance of our businesses. So here is the way we use historical financials to assess our portfolio companies.
Two things may be evident to you as you read these questions: They are about the company’s strategic steps in the past, and these are issues that even a first- year undergrad could raise. What’s so great about these questions? Nothing. We ask these questions not to evaluate the answers objectively but to subjectively assess if they fit our preexisting hypotheses of success or failure. Yes, we know the answer we want before we have asked the first question. If we have done a decent job over many years, it is not a result of asking these mundane questions but because our underlying bias demands the answers fit our template. We have our templates for success and failure, and we aim to assess if the company’s strategy fits a pattern. Very few do—

We avoid the automotive component space because, in general, its clients, the automotive companies, do not allow them to make money. In the United States, for example, the top five car companies controlled about two- thirds of the market in 2021.27 This concentration allows them to drive a tough bargain with their suppliers: the automotive component companies. Unsurprisingly, not many parts suppliers can consistently earn a decent profit. India is even more consolidated than the United States— the dominant car company, Maruti Suzuki, controls half the Indian market. The Indian motorcycle market is an oligopoly of just three companies. It is not unusual for a parts supplier to have a huge customer concentration— a top customer typically accounts for 30 to 50 percent of revenues. Hence, our bias is to reject almost every automotive component business. There can be an exception, though. But that exception must fit the following template. First, the parts supplier would need to manufacture a critical component requiring proprietary technology and have a low customer concentration over many years. It should have only one or two competitors, and the competitive dynamics in the industry should be stable over the long term. Finally, there should have been no new entrants to the industry for many years, and the company should have delivered good financials historically. Note that not a single criterion here is about the future.

Assessing the strategy of a business is useless unless we have a strategy to comprehend the strategy.

Darwin discovered that the success of a species is not dependent on its being the best but simply being better than the competition. This joke will make it more straightforward. When two friends hiking in a forest spot a lion, one starts putting on his running shoes. His friend says, “What are you doing that for? You can’t possibly outrun a lion.” The man replies, “I know, but I need only to run faster than you, not the lion!”

Investors, analysts, and academics have beaten the term “sustainable competitive advantage” to death. Still, as in evolutionary theory, the real question is not just about sustainable competitive advantage but about being consistently better than the competition. And what is the meaning of “better”? For us, it relates to measurable parameters like ROCE, market share, free cash flow, balance sheet strength, consistency of financials, and other such measures.

We want our businesses to gain market share over the long term, recognizing full well that the trend line may occasionally reverse in the short term.

In our diligence process, we received a lot of qualitative information from the management, customers, and even competitors about how the company’s strategy and direction had started yielding fruit in recent years. The company had been performing well recently, but, with a longer-term lens, I should have seen that the company was a chronic underperformer. Moreover, the same team of founder- managers had been running the business since its inception. So how could the following five- year result be any different from the past twenty- five? I had made a big blunder. When it comes to gauging competitive position, barring some exceptions, there is almost nothing better than measuring market share of volume, revenue, and profit over a long period. We live and learn.

Which of these three—45, 32, or 25—is the correct PE? It depends on the investor. For us, it is the backward-looking 45, and for my friend, it is the forward-looking 25. Most discussions of PE or other valuation ratios (like price/book or enterprise value/EBITDA) are forward-looking. The only PE ratio we discuss relates to the delivered earnings of the past. It may be the previous twelve months or the past three years, or, for some highly cyclical businesses, even the past ten-year average PE (i.e., current market value divided by the average earnings of the past ten years). We also use other valuation metrics, but all value the current business based on its past performance. I can understand if some investors project earnings over one or two years since that’s not too far in the future. It’s not ideal, but I get it. What I fail to fathom is why investors do something worse. Much worse. It’s called discounted cash flow (DCF) analysis. This makes academic sense. It’s true mathematically. But as a practical way to invest, it borders on being nonsensical. Let’s understand why. There are two main requirements for building a DCF spreadsheet: the discount rate and the cash flow projection.

again, isn’t the notion of measuring risk by using volatility as a proxy quite silly? As I discussed in chapter 1, how does riskiness have anything to do with volatility? For an investor, the riskiness of a business is directly proportional to the probability of capital loss of investing in that business. The higher the potential loss, the higher the risk. I don’t care about β and never will. As you can see in the formula, we need a number for the expected market return.

If investors can’t forecast cash flows even a few days or months in advance, how can they be expected to project cash flows years ahead? But this is what the DCF methodology demands. [AA Note: True, but one can use DCF models to gage the impact of different long-term outcomes.]

Investors and analysts rarely fail to build massive, complicated financial models that assess dozens of factors to project cash flows over many years in the future. Hail Excel. It’s not that the builders of these Excel models— whether analysts, bankers, consultants, or investors— are unaware of the pitfalls. But for some reason, the deep desire to look far in the future to arrive at an exact number overwhelms the rational voice admonishing the person to stop pretending they are doing anything useful. One of the best ways for you to get a sense of this future obsession is to read the transcripts of a company’s quarterly results conference calls. Most companies post such transcripts in their websites’ “Investor Relations” section. I analyzed three conference call transcripts— for Walmart (for Q2 2018), P& G (for Q4 2017), and General Motors (for Q2 2017)— and the results are stark. For Walmart, analysts and investors focused twenty-eight out of forty- nine questions on the future (e.g., “implied EBIT margin direction within the guidance”). On the P& G call, fourteen out of twenty questions asked the management to make some kind of prediction (e.g., “Do you have more initiatives hitting the market?”). At General Motors, a staggering twenty- seven out of thirty- three questions were forward- looking (e.g., “What should we think about the cadence of the expected savings from the restructuring actions?”). The tug- of- war between the analyst and the management team is occasionally painful to witness: The former tries to pin down exact forecasts for revenues and margins (so that they can populate the DCF model). Knowing full well that the future is inherently unpredictable, the latter attempts to sidestep the question with some broad generic comments. For example, on the General Motors call for Q2 2017, an analyst wanted to know the revenue projection for OnStar, an advanced communication system installed in GM cars. The answer from the CFO was, “As we have talked about before, yes, OnStar is generating revenue. We don’t disclose it separately. It continues to grow.”My sympathies lie with company management; they know that they don’t know what the future will bring. But, on the other hand, most analysts and fund managers, having never worked in a company, think that it is the management’s job to know the future; how else how will they be able to populate their DCF models? I have been on the boards of more than twenty-five companies, and over the years I have never seen a management team meet its budgets. Some exceed their projections, and some undershoot. Occasionally, the over- or underperformance is by a wide margin. If the company management can’t forecast correctly, how can investors do so? They can’t. More importantly, they shouldn’t try. We have never done a DCF analysis and never will. However, I know many— if not most— investors and analysts do. Maybe they have figured out a method to look far into the future that eludes me. In any event, our approach is straightforward.

Oh, one last point on valuation. It is always the last thing we discuss. When evaluating a business, risk comes first, quality second, and valuation last[AA Comment:  Ok.  But you only buy when the valuation is below an arbitrary level, even though you are searching for the long term winners.]

As long- term investors, we have dissociated ourselves from the “what will happen?” obsession and replaced it with “what has actually happened?” The former is a laundry list of conjectures and opinions, and the latter, to a large extent, consists of facts. Of course, facts in and of themselves are empty, and what matters is the opinions we build onto those facts, but at least they give us a foundation for a discussion. For example, if a company has had a historical ROCE over the past decade of 40 percent, two investors could have widely different opinions of this “fact.” One could assert that the company has a great future, and the other could argue that microeconomic theory demands that these returns will be competed away. Understanding that the company has had unusually high returns in the past focuses the investors’ attention on the sources of these returns and their sustainability. For example, did the company earn these returns because of regulatory protection from overseas competition, and, if so, are we comfortable backing a business that has not faced genuine competition? Or were these returns earned despite fierce competition? What has the company done relative to the competition that has made it so unique?

The once- famous company Nokia exemplifies the first category of issues. Nokia was a high- flying company in the late 1990s and dominated the mobile phone market in the same way that the iPhone does today.  Large emerging markets like China and India were severely underpenetrated in the mobile phone sector in the 1990s. Based on historical performance, it appeared that Nokia would conquer the world. Investors couldn’t buy enough Nokia stock, and, at its peak in the year 2000, Nokia’s market value was about $ 325 billion. Since then, it has lost more than 90 percent of its value. In the year 2000, all the historical signals from the company— its financial performance, competitive position, reputation, and dealer and customer feedback— would have screamed, “This is an amazing company.” But Nokia could not compete with Apple, Samsung, or tens of local Chinese and Indian competitors over the next decade, and the Nokia phone is now a museum relic. Anyone relying only on the history of Nokia would have suffered massive losses. Giving weight to a track record is a necessary condition for investment success, but it is in no way sufficient. We find this to be especially true in fast-changing industries that may or may not be technology related. Thus, in the case of Nokia, while most historical signals would have led one to conclude that the company had been truly outstanding, the very nature of the technology industry, in which rapid change is the norm, should have made any investor pause. We have avoided fast- changing industries like the plague, and many are not even in the technology space. Industries like retailing, microfinance, food delivery, and e- commerce are in the early stages of evolution in India.

I changed seven schools in twelve years because my father was in the armed forces, and they transferred him every two years. These were all government schools where the quality of teachers wasn’t usually the best, to put it mildly. However, I doubt even the best private schools had someone like the incredible Mr. Rathod. We had recently arrived in a small town called Jamnagar when I was in grade 7. I was miserable because I had to bid farewell to my friends in the previous town (called Dehu Road), and I found it hard to fit in socially at this new school. However, Mr. Rathod and his history class got me through that year. He refused to teach us history from the prescribed textbook. Instead, he ordered us to use the school library to read about ancient and modern Indian history from books and popular comics. And then he asked each student to pick a topic and educate the class on what they had learned. Of course, the rest of us were free to disagree with the presenter, and Mr. Rathod encouraged us to be methodical and logical in our arguments. I distinctly remember a group of twelve- year- olds almost coming to blows when debating the British influence on India. Before meeting Mr. Rathod, history for me was objective, undisputed, unchanging. Before him, every history teacher had drilled into me that there was only one correct answer to any question. Mr. Rathod taught us that most answers to questions in a history test should begin with, “It depends. Throughout grade 7, he showed us directly and indirectly that history can teach us less about who they were and much more about who we are. The notion that we can all be great investors just by gauging history is nonsense. It has been fundamental to our process, but it works for us because of who we are. So I bring my prejudices and biases to something as simple as a historical balance sheet. Occasionally, there are vehement disagreements on how to interpret the past. It occurs even within our small, well- knit team, which has worked together for many years. In the middle of these fiery debates, I often yearn for Mr. Rathod. Why couldn’t he be here to adjudicate this?

Evolutionary theory has taught me that . . . . . . we investors can reimagine investing by studying and understanding the history of a business and an industry instead of constantly obsessing over the future. 1. Darwin, the founder of modern evolutionary theory, understood better than anyone before him that the present was the result of the cumulative effect of the past. 2. He proposed his three groundbreaking theories— natural selection, sexual selection, and common descent— by construing history in a new light. 3. Unlike physics and chemistry, the science of evolutionary biology does not make predictions. Rather than answering the question, “What will happen to humans?” it ponders over the conundrum, “How did bipedal humans evolve from an ancestral quadruped ape?” 4. The investment world is obsessed with the future. Studying history has taken a backseat to making bold forecasts. 5. Taking a leaf out of evolutionary biology, we focus exclusively on widely and openly available historical information to analyze businesses. We spend no time building projections and forecasts. 6. We develop a point of view on company financials, strategy, competitive position, and valuation by analyzing what has already happened without bothering about what will happen. 7. However, concentrating on the past does have two main downsides. We may wrongly assume that (1) a historically successful business will continue to be so, or (2) a failed or failing business will continue to be so.

Our investment strategy has an unusual feature. We don’t invest in individual businesses. It may seem like we do, but we don’t. What in the world do we invest in then? Let’s do an evolutionary thought experiment to answer the question. Imagine another Earth- like planet that is at a similar distance from its sun- like star. This is not entirely improbable since there are a billion trillion (1021) stars in the universe. Would this planet evolve the same life forms as those on Earth? How likely is it to have honeysuckles and hornbills? Philosophers may have pondered this question for millennia, but the first modern scientist to attempt an answer was the late Harvard paleontologist and evolutionary biologist Stephen Jay Gould. In his excellent book Wonderful Life, Gould took the position that evolution was unpredictable: “Replay the tape a million times . . . and I doubt that anything like Homo sapiens would ever evolve again.”
Dolphins are mammals just like us, and sharks are fish. But their fusiform body shapes are pretty similar, and, more interestingly, they have the same coloration. Both have a light underbelly and darker back, making them harder to spot from above and below. George McGhee, a paleontologist, claims that the reason sharks, dolphins, tuna, and the extinct ichthyosaur look alike is that there is only one way for a fast- swimming animal to evolve.

Let’s move on to plants. Most of us have had coffee, tea, and chocolate (derived from cacao). The Brazilians among us will be familiar with the drink Guaraná Antarctica, made from the guaraná plant in the Amazon rainforest. All four plants produce the same chemical desired by humans: a purine alkaloid called 1,3,7- trimethylpurine- 2,6- dione— in short, caffeine. 9 These four plants may seem to be closely related, but they aren’t. The common ancestor of tea and coffee dates back a hundred million years. Cacao is more closely related to maple and eucalyptus trees than to tea and coffee. Bizarrely, the ancestor of coffee gave rise to potatoes and tomatoes but not tea! Plants have many defense mechanisms against predators, and it appears that some have converged toward the same solution: producing caffeine. [AA Note:  The book Why We Sleep shows a picture of what caffeine does to a spider’s ability to build spider webs.]

I could go on and on to fill this book with examples of convergent evolution in the natural world. But scientists now agree that convergence is the rule, not an exception, in nature. This sentiment is best expressed by the most famous advocate of convergence, the Cambridge paleontologist Simon Conway Morris, who has written two books on the subject. He has explained convergence by saying, “Certainly it’s not the case that every Earth- like planet will have life let alone humanoids. But if you want a sophisticated plant, it will look awfully like a flower. If you want a fly, there are only a few ways you can do that. If you want to swim, like a shark, there are only a few ways you can do that. If you want to invent warm- bloodedness, like birds and mammals, there are only a few ways to do that.” Convergence in nature symbolizes a profound fact: There is a pattern to success and failure. What can the Caribbean anole, the crest- tailed marsupial mouse, and caffeine teach us about investing? Convergence in business symbolizes a profound fact: There is a pattern to success and failure.

We Don’t Invest in Individual Businesses Earlier in the chapter, I made the following assertion about our investment strategy: We don’t invest in individual businesses. It may seem like we do, but we don’t. So what in the world do we invest in then? We invest in convergent patterns. We seek patterns that repeat. As we saw, “replaying the tape of life” often yields the same result. We operate on the principle that the business world is no different. There is a big difference between asserting “I love this business” and “I love this business construct.” We are fans of the latter, not the former. We don’t care about a business; we are deeply attached to a business template. Not unlike the natural world, which converges toward a small subset of answers to the same questions, we have seen that companies around the globe behave in similar ways when facing a similar environment. Not always, but often enough. We have benefited enormously by asking this simple convergence question up front: “Have we seen this pattern elsewhere?”

We detest the phrases “This time, it’s different” and “My gut yells me this will work.”  We need to see the evidence that our investment thesis has worked elsewhere. If it hasn’t, we are unlikely to touch it.  We are the antithesis of the venture capital community, which earnings its living by betting on untested and unproven businesses.  I am in awe of successful venture capital firms, but my admiration for them will never translate into a desire to emulate them.

Daniel Kahneman is one such individual.  His masterpiece Thinking, Fast and Slow should be compulsory reading for all investing 101 classes.  If you are already an investor, there is no more valuable chapter to read (and re-read_ than Chapter 23, “The Outside View.”

As I was transitioning to consulting in my early investing days, I was the biggest believer in the myth that more work produces better answers for investors.  It doesn’t.

The second benefit [of not investing in bad businesses] was the time , money, and effort saved.  Instead of conducting lengthy and unproductive management meetings, schmoozing the investor relations person, paying fat fees to consultants, and spending weeks to construct multimegabyte Excel spreadsheets, we spend a few hours on the internet, download a few reports, and make our decision within a few minutes with enough time to go home early to our families.

Buying into a business means also buying into the industry of that business.  … But how could we be sure that it would continue to be successful?  By witnessing the convergent outcomes of other businesses within the industry, all of which had been able to scale their revenues over many decades without sacrificing profitability. 

I highlight six types of businesses we avoid at all costs:  1. Those owned and run by crooks. 2. Turnaround situations. 3. Those with high levels of debt. 4. M&A junkies. 5. Those in fast changing industries. 6. Those with unaligned owners.  … The common thread through all of these was the hunger for detection patterns and seeking a convergence of outcomes.

We are price sensitive. The median trailing price/earnings ratio for our portfolio at the time of our investment is less than 15 when the Indian marlet has been about 19 to 20.  We have rarely ever paid 20 times trailing P/E. Most importantly, we have never said, “This is such a great business that even 30 P/E is justified.” … The markets are generally efficient – businesses like these are rarely6 available at a throwaway price. 

I know I that I would have missed Amazon in the past, and will miss an Amazon-like business in the future.  So be it.  The only saving grace of this failure? I doubt I will see another Bezos in my lifetime. [AA Note:  And he retired years ago.]

Zahavi’s handicap principle contends that a signal that is costly to produce is honest and therefore can be relied upon by a receiver. Strong signals:  1. Females are more attracted to males with redder or brighter hues.  2. Males with redder coloration are fitter than paler males.  3. It is costly for healthy males to produce a deep read pigment. … For a signal to strong, it must be costly.  An honest signal is not “our margins will be 15% next year” but “our average margin was 12% over the last ten years.” … A good reputation is a very costly signal – and hence an honest one.  [Cites Phil Fisher and the “scuttlebutt method” for ascertaining good reputations.]  [Doesn’t mention that a repeated willingness to go against the grain by investing in the long term when the market is myopically punishing such action, is perhaps one of the strongest and honest signals a company can send.]

Kurten demonstrated mathematically and empirically that phenotypical change (i.e., changes in the bodily characteristics of a species) could be rapid from one generation to the next, but that in contrast, evolution can be slow on long time scales. … Contrary to expectations, the pace of genetic evolution is inversely correlated with the period of measurement.  In less than a decade, a finches’ beak size increases then decreases because of natural selection.  When observed over decades, beak size did not change that much.  It fluctuates due to extreme weather events. … This realization has helped me formulated an investing principle that I call the Grant-Kurten principle of investing.  It goes as follows: When we find high-quality businesses that do not fundamentally alter their character over the long term, we should exploit the inevitable short-term fluctuations in their businesses for buying and not selling. … Since [buying opportunities] arise infrequently, we rarely ever buy.  We are lazy.  After investing, we ignore the short term fluctuations because the fundamental characteristics of stellar businesses remain stable over the long term.  We have sold only when there had been an egregiously bad capital allocation or irreparable damage to the business.

The absence of evidence is evidence of absence. [AA Note:  Nassim Taleb disagrees.]

There are a few large and successful firms in most industries.  The successful companies are becoming even more successful.  Weak companies are getting weaker.

We have a straightforward rule that is also easy to implement: Buy when the price is right.  Unfortunately, not everyone follows this rule.  A widely practiced rule is buy when the time is right.  That is also a straightforward rule, but is it easy to implement?  We follow the former because we know the price we want to pay for the business we want to own.  It may or may not be the right price, but we know it for sure.  We have no way of figuring out the right price.  Maybe some folks do.  Good for them. … Lets say we have valued a business at $100 per share.  If the stock falls to $100 and out business assessment remains unchanged, we buy as much of the business as we can at or below $100.  [After making this statement he espouses the magic of compounding and how it made investors like Buffett and Davis fabulously rich over the very long term, as measured in decades.]  … Not selling makes us better buyers.  That seems like a weird assertion. … Over the years I have heard many objections from my fund manager friends and investors who have refused to give us money because they were uncomfortable with our permanent owner approach.  .. Why should I hold on to 60 P/E stock?  We are price sensitive; we do not invest if the valuation is high.  A logical question, then is, Why aren’t we seller when the valuation is high? … It’s a logical and fair question.  It is so logical and fair that most fund managers will sell and exit at this point.  We won’t.  The reasons are three-fold.  First we have found that great businesses usually surprise to the upside. Second, valuation multiples generally don’t stay benign for great businesses. Third, why should I limit my valuation to only the next five years? 

Objection 2:  My “incremental” return will be low from now on [i.e. after the multiple expands, while I own it well beyond the entry multiple that I demand].

Objection 3: There is a better opportunity to deploy capital. … We never engage in “sell-high-to-buy-low activity. … One justification for this hamster-on-a-wheel behavior is that it is prudent to sell a business with a 50 P/E to buy a business with a 15 P/E.  Not for us.

We have been successful investors not because we are better at buying, but because we refuse to succumb to the temptation of selling.

The investment community ties itself up in knots over finding the “best” investments.  I have seen extraordinarily complex algorithms and multigigabyte spreadsheets to assess the value and quality of a business.  One the other hand, we are interested only in executing a sound investment process. … We invest only in exceptional businesses because most businesses fail, and we want to reduce uncertainty.  We buy only at attractive valuations because, while we don’t know what will go wrong, we assume that something will.  … Our algorithm … has only three steps:  1. Eliminate significant risks. 2. Invest only in stellar businesses at a fair price. 3. Own them forever.


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