Prashant Kishor is currently the most famous backroom boy in Indian politics. To put it euphemistically, he is currently India’s most famous political strategist who helps Indian politicians win elections.
He helped Narendra Modi win the 2012 Gujarat state assembly elections and the 2014 Lok Sabha elections. In 2015, he helped Nitish Kumar win the Bihar state assembly elections. Given this success, currently there is a “halo effect” that has been built around Kishor, in the media.
The Bharatiya Janata Party fought the Bihar elections under the leadership of Narendra Modi. Modi was the main campaigner for the party, in the run-up to the election. The party lost badly in Bihar. Given this, Kishor is now seen as the man who first scripted Modi’s rise and then his fall. He is seen as the man who can do nothing wrong.
As Phil Rosenzweig writes in The Halo Effect…and the Eight Other Business Delusions That Deceive Managers: “Once people…believe the outcome is good, they tend to make positive attributions about the decision process; and when they believe the outcome is poor, they tend make negative attributions.”
How does this apply in Kishor’s case? Kishor has been a part of three successful election campaigns in India. Also, more than the 2014 Lok Sabha triumph, the 2015 win in Bihar has given an aura of invincibility to him, which is nothing but the positive attribution that Rosenzweig talks about.
Nevertheless, Kishor has just been a part of three successful elections in India, up until now. The point being that the sample size is very small. And small samples do give extreme results, as has been the case with Kishor.
As Nobel Prize winning psychologist Daniel Kahneman writes in Thinking, Fast and Slow: “Large samples are more precise than small samples. Small samples yield extreme results more often than large samples.”
What this means is that Kishor needs to be a part of more election wins till the tag of invincibility is ascribed to him. Also, until now he has had to hard-sell leaders like Modi, Kumar and Lalu Yadav, who were strong brands on their own.
In fact, the Bihar election win was more about Kumar and Yadav coming together, than anything else. The BJP did very well in 2014 Lok Sabha elections in Bihar because Kumar and Yadav fought the elections separately.
Of course, Kishor deserves credit for building a coherent marketing message around Kumar and Yadav, who are very distinct personalities. Kishor’s real challenge will come in the state assembly elections in Uttar Pradesh and Punjab in 2017, where he will help the Congress party. The Congress has six members in a house of 404, in the Uttar Pradesh assembly. In Punjab it has been out of power for a while.
In these two states Kishor will have to construct a marketing message around Rahul Gandhi and the Gandhi family. And this is where his real challenge will lie. Despite having been in active politics for close to twelve years now, Gandhi remains a weak leader and weak brand, who is not taken very seriously, in large parts of the country.
Will Kishor be able to rescue Gandhi and the Congress party? It is difficult to see the Congress party reviving in Uttar Pradesh. In Punjab, perhaps they stand a chance. Predicting how politics and elections go, is a difficult job. But one thing can be said here nonetheless, Kishor’s invincibility will surely take some beating in the days to come.
As Leonhard Mlodinow writes in The Drunkard’s Walk—How Randomness Rules Our Lives in the context of corporate executives: “Executives’ winning years are attributed to their brilliance, explain retroactively through incisive hindsight. And when people don’t succeed, we often assume the failure accurately reflects the proportions which their talents and their abilities fill the urn.”
Come 2017, something along similar lines will happen with Kishor as well. The media which is currently busy talking about his invincibility, will be busy writing about his weaknesses.
(Vivek Kaul is the author of the Easy Money trilogy. He can be reached at [email protected])
When the Bhartiya Janata Party (BJP) won the Lok Sabha elections in May 2014, a lot of credit for its success went to Amit Shah. He was deemed to be a master strategist and a hard worker. A lot was written on how Shah worked round the clock to ensure a BJP victory. Explanations were offered on how Shah picked up winning candidates, engineered local alliances and so on.
The rise of Amit Shah in public consciousness is an excellent example of the halo effect. Author and strategist Michael Mauboussin defines the halo effect as “our proclivity to attach attributes to what has succeeded, solely because of the success.” The media went around looking for reasons behind the success of Amit Shah and found them. As Phil Rosenzweig writes in The Halo Effect…and the Eight Other Business Delusions that Deceive Managers “We want explanations. We want the world around us to make sense…We prefer explanations that are definitive and offer clear implications.”
This tendency to build a halo around those who are successful is not just limited to politics. It’s a very important part of success in business as well. As Jason Zweig writes in The Devil’s Financial Dictionary: “If the price of a company’s stock has gone up strongly, the people who run the company will seem almost superhuman. In early 2000, for instance, with Cisco Systems’ stock up more than 100,000 percent over the previous decade, Fortune magazine called its chief executive, John Chambers, “the world’s greatest CEO.””
If you are the kind who reads business newspapers and magazines regularly, you are unlikely to miss out on profiles of business leaders. These profiles typically look into the background of what makes these leaders so successful, at the time they are successful. These days the profiles of those who run ecommerce firms are very popular in the media. They get the kind of readership that nothing else does (at least in the business media). What none of these profiles seem to talk about is that these businesses are loss-making and are likely to continue to be loss making.
The point being that the media writes good things only up until the going is good. Getting back to Amit Shah, after the success of May 2014, the BJP lost elections first in Delhi, and then more importantly in Bihar. Both these defeats were hugely embarrassing for the party.
And not surprisingly, knives are now out for Shah. Some analysis suggest that he is a Gujarati and doesn’t have a feel of the entire country. This has been offered as one of the explanations for why the BJP lost Bihar. Then there were also some news reports that suggested that Shah will be replaced as the BJP president soon.
The same media that built a halo around Shah is now busy pulling him down. In fact, what is happening to Shah now, also happened to Chambers at Cisco.
After a 100,000 percent increase in price, the stock price of Cisco fell by 80% in a year. As Zweig writes: “A year later with the stock price down almost 80%, Fortune described Chambers as having dangerously blind to the signs of the coming collapse. The same company run by the same man seemed utterly transformed as soon as its stock price fell.”
Why does this happen? As Mauboussin told me in an interview a few years back: “The idea is that when things are going well, we attribute that success to skill—there’s a halo effect. Conversely, when things are going poorly we attribute it to poor skill…So the answer is that great success, the kind that lands you on the covers of business magazines [or other magazines], almost always includes a very large dose of luck. And we’re not very good at parsing the sources of success.”
Nevertheless, the media couldn’t have just written that luck played a large role in Shah and BJP’s success in 2014. That would have been boring.
(Vivek Kaul is the author of the Easy Money trilogy. He tweets @kaul_vivek)
A special court in Hyderabad found all the ten accused in the Satyam scam guilty of cheating, forgery, destruction of evidence and criminal breach of trust. This includes the founder and the Chairman of the company B Ramalinga Raju. The decision came more than six years after the scam first came to light. On January 7, 2009, Raju wrote a letter to the board of directors of Satyam Computer Services, in which he admitted to cooking the books of the company. A copy of the letter was sent to the stock exchanges as well as the Securities and Exchange Board of India. In this letter Raju admitted to inflating the cash and bank balances of the company by Rs 5,040 crore. The company’s total assets as on September 30, 2008, stood at Rs 8,795 crore. Of this cash and bank balances stood at Rs 5,313 crore which was nearly 60% of the total assets. This was overstated by Rs 5,040 crore. The company basically had cash and bank balances of less than Rs 300 crore. Raju also admitted to fudging the last financial result that the company had declared, for the period of three months ending September 30, 2008. The company had reported revenues of Rs 2,700 crore, with an operating margin of 24% of revenues or Rs 649 crore. These numbers were made up. The actual revenues were Rs 2,112 crore with an operating margin of Rs 61 crore or 3% of the total revenues. So, Satyam had made a profit of Rs 61 crore but was declaring a profit of Rs 649 crore. The difference was Rs 588 crore. The operating profit for the quarter was added to the cash and bank balances on the balance sheet. Hence, cash and bank balances went up by an “artificial” Rs 588 crore just for the three month period ending September 30, 2008. This was a formula that Raju had been using for a while. First Satyam over-declared its operating profit. Once this fudged operating profit was moved to the balance sheet, it ended up over-declaring its cash and bank balances. And this led to a substantially bigger balance sheet than was actually the case. The company had total assets of Rs 8,795 crore as on September 30, 2008. Once the Rs 5,040 crore of cash and bank balances that were simply not there were removed from this, the “real” total assets fell to a significantly lower Rs 3,755 crore. Raju went on to say that: “The gap in the Balance Sheet has arisen purely on account of inflated profits over a period of last several years (limited only to Satyam standalone, books of subsidiaries reflecting true performance). What started as a marginal gap between actual operating profit and the one reflected in the books of accounts continued to grow over the years.” What was Raju upto? Raju’s fraud was no Enron, where complicated derivative transactions were used to boost revenues as well as profit. He had been cooking the books since 2003 by simply over-declaring revenues and profits. In the process he ended up boosting his balance sheet as the cash and bank balances kept going up. So, how did Raju manage to boost revenues? In order to do this Raju created fictitious clients with whom Satyam had entered into business deals. This was again something akin to Enron, which essentially entered into business deals with its subsidiaries. The subsidiaries paid Enron for the deal by borrowing money. While the revenues brought in from the subsidiaries was recorded by Enron, the debt that they had taken on, wasn’t. Getting back to Raju, in order to record the fake sales he introduced 7000 fake invoices into the computer system of the company. He couldn’t stop at this. The clients were fake. Fake clients could not make real payments. Given this, the company kept inflating the money due from its clients (or what Raju called debtors position in his letter). Further, once fake sales had been recorded, fake profits were made. And fake profits brought in fake cash which needed to be invested somewhere. This led to Raju creating fake bank statements(forged fixed deposit receipts) where all the fake cash that the company was throwing up was being invested. Raju then tried to use this “fake cash” and buy out two real estate companies called Maytas Properties and Maytras Infra (opposite of Satyam and promoted by the family) for a total of $1.6 billion. But this did not work out. As Raju said in his confessional statement: “The aborted Maytas acquisition deal was the last attempt to fill the fictitious assets with real ones. Maytas’ investors were convinced that this is a good divestment opportunity and a strategic fit. Once Satyam’s problem was solved, it was hoped that Maytas’ payments can be delayed.” The idea was to have some “real” assets against all the “fake” cash that the company had managed to accumulate. But that did not happen and after this, Raju had no way out but to come clean. The question is how could Raju run such a big scam for such a long period of time. Satyam’s accounts were audited by Price Waterhouse, a member-firm of PricewaterhouseCoopers International Ltd — since the financial year 2000-2001. The auditor had no clue that Satyam’s assets were overstated by more than Rs 5,000 crore. When the scam first broke out a middle level executive from a Big 4 consulting firm told me: “All the auditor needed to ask was the bank statements of the various banks in which this (supposed) cash had been deposited or mutual funds it had been invested in. This is overstatement of Rs 5,000 odd crore we are talking about, not Rs 500.” The auditor clearly did not do that. The auditor is paid to ask questions; in this case it seems to have been paid not to ask any. The company couldn’t have hoodwinked the investors without the auditor being on its side. This was no complicated accounting fraud like Enron was. The many analysts who covered Satyam also did not have any clue about the fact that the profits as well as revenues of Satyam were fake. Brokerage analysts who follow companies need to keep companies in a good humour. Without that, they run the risk of being given limited or at times no access to the company, at all. This explains why none of the analysts caught on to what was happening at Satyam. It also explains why the number of sell recommendations on stocks put out by brokerage analysts are lower when compared to the number of buy recommendations that brokerages put out. And finally we come to the media. It had no clue of what was happening at Satyam. One reason for this lies in the fact that the Indian media over the years has been extremely taken in by the IT companies and the people who run them. The case with Satyam’s Raju was no different. Lot of magazines and newspaper wrote stories on him and painted him as a person who could do no wrong. This blinds investors, media and experts who follow a company. This comes from the need of the media to create a story around the individual. As Nassim Nicholas Taleb writes in his book Fooled by Randomness on how the Halo effect around a CEO is built up by the media:“We would get very interesting and helpful comments on his remarkable style, his incisive mind, and the influences that helped him achieve that success. Some analysts may attribute his achievement to precise elements among his childhood experiences. His biographer will dwell on the wonderful role models provided by his parents; we would be supplied with black and white pictures in the middle of the book of great mind in the making.” Something similar had happened with Satyam as well. And given this the media expected Satyam to do no wrong. The Halo effect was clearly at work in case of Satyam as well. Investors could see Raju doing no wrong. Raju even sold his shares in Satyam to fund social causes. How could such a man be a fraud? When the Halo effect is at work, the ability to ask incisive pointed questions clearly goes down, and that’s what happened in Satyam’s case as well. So, while Raju ran his fraud, the auditor slept, the analysts slept and so did the media. To be fair, the media did an excellent job of exposing Raju and his many other ‘shenanigans’ after he had confessed. Now more than six years later, the first decision in the Satyam scam has been made. Of course, we haven’t seen the last of this case, given the slow pace at which our judicial system works. Stay tuned.
(Vivek Kaul is the author of the Easy Money trilogy. He tweets @kaul_vivek)
Michael J. Mauboussin is Chief Investment Strategist at Legg Mason Capital Management in the United States. He is also the author of bestselling books on investing like Think Twice: Harnessing the Power of Counterintuition and More Than You Know: Finding Financial Wisdom in Unconventional Places. His latest book The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing is due later this year. In this interview he speaks to Vivek Kaul on the various aspects of luck, skill and randomness and the impact they have on business, life and investing. Excerpts:
How do you define luck? The way I think about it, luck has three features. It happens to a person or organization; can be good or bad; and it is reasonable to believe that another outcome was possible. By this definition, if you win the lottery you are lucky, but if you are born to a wealthy family you are not lucky—because it is not reasonable to believe that any other outcome was possible—but rather fortunate. How is randomness different from luck? I like to distinguish, too, between randomness and luck. I like to think of randomness as something that works at a system level and luck on a lower level. So, for example, if you gather a large group of people and ask them to guess the results of five coin tosses, randomness tells you that some in the group will get them all correct. But if you get them all correct, you are lucky. And what is skill? For skill, the dictionary says the “ability to use one’s knowledge effectively and readily in execution or performance.” I think that’s a good definition. The key is that when there is little luck involved, skill can be honed through deliberate practice. When there’s an element of luck, skill is best considered as a process. You have often spoken about the paradox of skill. What is that? The paradox of skill says that as competitors in a field become more skillful, luck becomes more important in determining results. The key to this idea is what happens when skill improves in a field. There are two effects. First, the absolute level of ability rises. And second, the variance of ability declines. Could you give us an example? One famous example of this is batting average in the sport of baseball. Batting average is the ratio of hits to at-bats. It’s somewhat related to the same term in cricket. In 1941, a player named Ted Williams hit .406 for a season, a feat that no other player has been able to match in 70 years. The reason, it turns out, is not that no players today are as good as Williams was in his day—they are undoubtedly much better. The reason is that the variance in skill has gone down. Because the league draws from a deeper pool of talent, including great players from around the world, and because training techniques are vastly improved and more uniform, the difference between the best players and the average players within the pro ranks has narrowed. Even if you assume that luck hasn’t changed, the variance in batting averages should have come down. And that’s exactly what we see. The paradox of skill makes a very specific prediction. In realms where there is no luck, you should see absolute performance improve and relative performance shrink. That’s exactly what we see. Any other example? Take Olympic marathon times as an example. Men today run the race about 26 minutes faster than they did 80 years ago. But in 1932, the time difference between the man who won and the man who came in 20th was close to 40 minutes. Today that difference is well under 10 minutes. What is the application to investing? The application to investing is straightforward. As the market is filled with participants who are smart and have access to information and computing power, the variance of skill will decline. That means that stock price changes will be random—a random walk down Wall Street, as Burton Malkiel wrote—and those investors who beat the market can chalk up their success to luck. And the evidence shows that the variance in mutual fund returns has shrunk over the past 60 years, just as the paradox of skill would suggest. I want to be clear that I believe that differential skill in investing remains, and that I don’t believe that all results are from randomness. But there’s little doubt that markets are highly competitive and that the basic sketch of the paradox of skill applies. How do you determine in the success of something be it a song, book or a business for that matter, how much of it is luck, how much of it is skill? This is a fascinating question. In some fields, including sports and facets of business, we can answer that question reasonably well when the results are independent of one another. When the results depend on what happened before, the answer is much more complex because it’s very difficult to predict how events will unfold. Could you explain through an example? Let me try to give a concrete example with the popularity of music. A number of years ago, there was a wonderful experiment called MusicLab. The subjects thought the experiment was about musical taste, but it was really about understanding how hits happen. The subjects who came into the site saw 48 songs by unknown bands. They could listen to any song, rate it, and download it if they wanted to. Unbeknownst to the subjects, they were funneled into one of two conditions. Twenty percent went to the control condition, where they could listen, rate, and download but had no access to what anyone else did. This provided an objective measure of the quality of songs as social interaction was absent. What about the other 80%? The other 80 percent went into one of 8 social worlds. Initially, the conditions were the same as the control group, but in these cases the subjects could see what others before them had done. So social interaction was present, and by having eight social worlds the experiment effectively set up alternate universes. The results showed that social interaction had a huge influence on the outcomes. One song, for instance, was in the middle of the pack in the control condition, the #1 hit on one of the social worlds, and #40 in another social world. The researchers found that poorly rated songs in the control group rarely did well in the social worlds—failure was not hard to predict—but songs that were average or good had a wide range of outcomes. There was an inherent lack of predictability. I think I can make the statement ever more general: whenever you can assess a product or service across multiple dimensions, there is no objective way to say which is “best.” What is the takeaway for investors? The leap to investing is a small one. Investing, too, is an inherently social exercise. From time to time, investors get uniformly optimistic or pessimistic, pushing prices to extremes. Was a book like Harry Potter inevitable as has often been suggested after the success of the book? This is very related to our discussion before about hit songs. When what happens next depends on what happened before, which is often the case when social interaction is involved, predicting outcomes is inherently difficult. The MusicLab experiment, and even simpler simulations, indicate that Harry Potter’s success was not inevitable. This is very difficult to accept because now that we know that Harry Potter is wildly popular, we can conjure up many explanations for that success. But if you re-played the tape of the world, we would see a very different list of best sellers. The success of Harry Potter, or Star Wars, or the Mona Lisa, can best be explained as the result of a social process similar to any fad or fashion. In fact, one way to think about it is the process of disease spreading. Most diseases don’t spread widely because of a lack of interaction or virulence. But if the network is right and the interaction and virulence are sufficient, disease will propagate. The same is true for a product that is deemed successful through a social process. And this applies to investing as well? This applies to investing, too. Instead of considering how the popularity of Harry Potter, or an illness, spreads across a network you can think of investment ideas. Tops in markets are put in place when most investors are infected with bullishness, and bottoms are created by uniform bearishness. The common theme is the role of social process. What about someone like Warren Buffett or for that matter Bill Miller were they just lucky, or was there a lot of skill as well? Extreme success is, almost by definition, the combination of good skill and good luck. I think that applies to Buffett and Miller, and I think each man would concede as much. The important point is that neither skill nor luck, alone, is sufficient to launch anyone to the very top if it’s a field where luck helps shape outcomes. The problem is that our minds equate success with skill so we underestimate the role of randomness. This was one of Nassim Taleb’s points in Fooled by Randomness. All of that said, it is important to recognize that results in the short-term reflect a lot of randomness. Even skillful managers will slump, and unskillful managers will shine. But over the long haul, good process wins. How do you explain the success of Facebook in lieu of the other social media sites like Orkut, Myspace, which did not survive? Brian Arthur, an economist long affiliated with the Santa Fe Institute, likes to say, “of networks there shall be few.” His point is that there are battles for networks and standards, and predicting the winners from those battles is notoriously difficult. We saw a heated battle for search engines, including AltaVista, Yahoo, and Google. But the market tends to settle on one network, and the others drop to a very distant second. I’d say Facebook’s success is a combination of good skill, good timing, and good luck. I’d say the same for almost every successful company. The question is if we played the world over and over, would Facebook always be the obvious winner. I doubt that. Would you say that when a CEO’s face is all over the newspapers and magazines like is the case with the CEO of Facebook , he has enjoyed good luck? One of the most important business books ever written is The Halo Effect by Phil Rosenzweig. The idea is that when things are going well, we attribute that success to skill—there’s a halo effect. Conversely, when things are going poorly we attribute it to poor skill. This is often true for the same management of the same company over time. Rosenzweig offers Cisco as a specific example. So the answer is that great success, the kind that lands you on the covers of business magazines, almost always includes a very large dose of luck. And we’re not very good at parsing the sources of success. You have also suggested that trying to understand the stock market by tuning into so called market experts is not the best way of understanding it. Why do you say that? The best way to answer this is to argue that the stock market is a great example of a complex adaptive system. These systems have three features. First, they are made up of heterogeneous agents. In the stock market, these are investors with different information, analytical approaches, time horizons, etc. And these agents learn, which is why we call them adaptive. Second, the agents interact with one another, leading to a process called emergence. The interaction in the stock market is typically through an exchange. And, finally, we get a global system—the market itself. So what’s the point you are trying to make? Here’s a key point: There is no additivity in these systems. You can’t understand the whole simply by looking at the behaviors of the parts. Now this is in sharp contrast to other systems, where reductionism works. For example, an artisan could take apart my mechanical wristwatch and understand how each part contributes to the working of the watch. The same approach doesn’t work in complex adaptive systems. Could you explain through an example? Let me give you one of my favorite examples, that of an ant colony. If you study ants on the colony level, you’ll see that it’s robust, adaptive, follows a life cycle, etc. It’s arguably an organism on the colony level. But if you ask any individual ant what’s going on with the colony, they will have no clue. They operate solely with local information and local interaction. The behavior of the colony emerges from the interaction between the ants. Now it’s not hard to see that the stock market is similar. No individual has much of a clue of what’s going on at the market level. But this lack of understanding smacks right against our desire to have experts tell us what’s going on. The record of market forecasters has been studied, and the jury is in: they are very bad at it. So I recommend people listen to market experts for entertainment, not for elucidation. How does the media influence investment decisions? The media has a natural, and understandable, desire to find people who have views that are toward the extremes. Having someone on television explaining that this could happen, but then again it may be that, does not make for exciting viewing. Better is a market boomster, who says the market will skyrocket, or a market doomster, who sees the market plummeting. Any example? Phil Tetlock, a professor of psychology at the University of Pennsylvania, has done the best work I know of on expert prediction. He has found that experts are poor predictors in the realms of economic and political outcomes. But he makes two additional points worth mentioning. The first is that he found that hedgehogs, those people who tend to know one big thing, are worse predictors than foxes, those who know a little about a lot of things. So, strongly held views that are unyielding tend not to make for quality predictions in complex realms. Second, he found that the more media mentions a pundit had, the worse his or her predictions. This makes sense in the context of what the media are trying to achieve—interesting viewing. So the people you hear and see the most in the media are among the worst predictors. Why do most people make poor investment decisions? I say that because most investors aren’t able to earn even the market rate of return? People make poor investment decisions because they are human. We all come with mental software that tends to encourage us to buy after results have been good and to sell after results have been poor. So we are wired to buy high and sell low instead of buy low and sell high. We see this starkly in the analysis of time-weighted versus dollar-weighted returns for funds. The time-weighted return, which is what is typically reported, is simply the return for the fund over time. The dollar-weighted return calculates the return on each of the dollars invested. These two calculations can yield very different results for the same fund. Could you explain that in some detail? Say, for example, a fund starts with $100 and goes up 20% in year 1. The next year, it loses 10%. So the $100 invested at the beginning is worth $108 after two years and the time-weighted return is 3.9%. Now let’s say we start with the same $100 and first year results of 20%. Investors see this very good result, and pour an additional $200 into the fund. Now it is running $320—the original $120 plus the $200 invested. The fund then goes down 10%, causing $32 of losses. So the fund will still have the same time-weighted return, 3.9%. But now the fund will be worth $288, which means that in the aggregate investors put in $300—the original $100 plus $200 after year one—and lost $12. So the fund has positive time-weighted returns but negative dollar-weighted returns. The proclivity to buy high and sell low means that investors earn, on average, a dollar-weighted return that is only about 60% of the market’s return. Bad timing is very costly. What is reversion to the mean? Reversion to the mean occurs when an extreme outcome is followed by an outcome that has an expected value closer to the average. Let’s say you are a student whose true skill suggests you should score 80% on a test. If you are particularly lucky one day, you might score 90%. How are you expected to do for your next test? Closer to 80%. You are expected to revert to your mean, which means that your good luck is not expected to persist. This is a huge topic in investing, for precisely the reason we just discussed. Investors, rather than constantly considering reversion to the mean, tend to extrapolate. Good results are expected to lead to more good results. This is at the core of the dichotomy between time-weighted and dollar-weighted returns. I should add quickly that this phenomenon is not unique to individual investors. Institutional investors, people who are trained to think of such things, fall into the same trap. In one of your papers you talk about a guy who manages his and his wife’s money. With his wife’s money he is very cautious and listens to what the experts have to say. With his own money his puts it in some investments and forgets about it. As you put it, threw it in the coffee can. And forgot about it. It so turned out that the coffee can approach did better. Can you take us through that example? This was a case, told by Robert Kirby at Capital Guardian, from the 1950s. The husband managed his wife’s fund and followed closely the advice from the investment firm. The firm had a research department and did their best to preserve, and build, capital. It turns out that unbeknownst to anyone, the man used $5,000 of his own money to invest in the firm’s buy recommendations. He never sold anything and never traded, he just plopped the securities into a proverbial coffee can(those were the days of paper). The husband died suddenly and the wife then came back to the investment firm to combine their accounts. Everyone was surprised to see that the husband’s account was a good deal larger than his wife’s. The neglected portfolio fared much better than the tended one. It turns out that a large part of the portfolio’s success was attributable to an investment in Xerox. So what was the lesson drawn? Kirby drew a more basic lesson from the experience. Sometimes doing nothing is better than doing something. In most businesses, there is some relationship between activity and results. The more active you are, the better your results. Investing is one field where this isn’t true. Sometimes, doing nothing is the best thing. As Warren Buffett has said, “Inactivity strikes us as intelligent behavior.” As I mentioned before, there is lots of evidence that the decisions to buy and sell by individuals and institutions does as much, if not more, harm than good. I’m not saying you should buy and hold forever, but I am saying that buying cheap and holding for a long time tends to do better than guessing what asset class or manager is hot. (The interview was originally published in the Daily News and Analysis(DNA) on June 4,2012. http://www.dnaindia.com/money/interview_people-should-listen-to-market-experts-for-entertainment-not-elucidation_1697709) (Interviewer Kaul is a writer and can be reached at [email protected])