Naturally, everyone expects to be above average. And those helpers – bless their hearts – will certainly encourage their clients in this belief. But, as a class, the helper-aided group must be below average. The reason is simple: 1) Investors, overall, will necessarily earn an average return, minus costs they incur; 2) Passive and index investors, through their very inactivity, will earn that average minus costs that are very low; 3) With that group earning average returns, so must the remaining group – the active investors. But this group will incur high transaction, management, and advisory costs. Therefore, the active investors will have their returns diminished by a far greater percentage than will their inactive brethren. That means that the passive group – the “know-nothings” – must win.

Source:   Berkshire Hathaway Shareholder Letters

If you instead focus on the prospective price change of a contemplated purchase, you are speculating. There is nothing improper about that. I know, however, that I am unable to speculate successfully, and I am skeptical of those who claim sustained success at doing so. Half of all coin-flippers will win their first toss; none of those winners has an expectation of profit if he continues to play the game. And the fact that a given asset has appreciated in the recent past is never a reason to buy it.

Source:   Berkshire Hathaway Shareholder Letters

Stock prices will always be far more volatile than cash-equivalent holdings. Over the long term, however, currency-denominated instruments are riskier investments – far riskier investments – than widely-diversified stock portfolios that are bought over time and that are owned in a manner invoking only token fees and commissions. That lesson has not customarily been taught in business schools, where volatility is almost universally used as a proxy for risk. Though this pedagogic assumption makes for easy teaching, it is dead wrong: Volatility is far from synonymous with risk. Popular formulas that equate the two terms lead students, investors and CEOs astray.

Source:   Berkshire Hathaway Shareholder Letters

Market forecasters will fill your ear but will never fill your wallet.

Source:   Berkshire Hathaway Shareholder Letters

Thaler pokes fun at much that is holy at the University of Chicago. Indeed, Thaler believes, with me, that people are often massively irrational in ways predicted by psychology that must be taken into account in microeconomics.

Source:   Poor Charlie's Almanack

Is the stock market so efficient that people can't beat it? Well, the efficient market theory is obviously roughly right - meaning that markets are quite efficient and it's quite hard for anybody to beat the market by significant margins as a stock picker by just being intelligent and working in a disciplined way. Indeed the average result has to be the average result. By definition, everybody can't beat the market. As I always say, the iron rule of life is that only twenty percent of the people can be in the top fifth. That's just the way it is. So the answer is that it's partly efficient and partly inefficient. And, by the way, I have a name for people who went to the extreme efficient market theory - which is "bonkers". It was an intellectually consistent theory that enabled them to do pretty mathematics. So I understand its seductiveness to people with large mathematical gifts. It just had difficulty in that the fundamental assumption did not tie properly to reality.

Source:  

Well, Berkshire's whole record has been achieved without paying one ounce of attention to the efficient market theory in its hard form. And not one ounce of attention to the descendants of that idea, which came out of academic economics and went into corporate finance and morphed into such obscenities as the capital asset pricing model, which we also paid no attention to. I think you'd have to believe in the tooth fairy to believe that you could easily outperform the market by seven percentage points per annum just by investing in high-volatility stocks.

Source:   Poor Charlie's Almanack

It is true that every established trend has a certain momentum, so that it is more likely to continue for at least a while longer than it is to reverse itself at the moment of observation. But this is far from saying that any trend may be relied upon to continue long enough to create a profit for those who “get aboard”. Rather extensive studies which we have made of the subject lead us to conclude that reversals of trend in every part of the financial picture occur so frequently as to make reliance on a trend a particularly dangerous matter. There must be strong independent reasons for investing money on the expectation of a continuance of past tendencies, and the investor must beware his weighing of future probabilities be unduly influenced by the trend line of the past.

Source:   The Intelligent Investor

It is a mistake to use, as journalists and some economists do, statistics without logic but the reverse does not hold: It is not a mistake to use logic without statistics. (Roundtrip Fallacy).

Source:   Fooled By Randomness

Mathematics is principally a tool to meditate, rather than to compute.

Source:   Fooled By Randomness

The science of econometrics consists of the application of statistics to samples taken at different periods of time, which we called time-series. It is based on studying the time series of economic variables, data, and other matters. In the beginning, when I knew close to nothing, I wondered whether time-series reflecting the activity of people now dead or retired should matter for predicting the future. Econometricians who knew a lot more than I did about these matters asked no such questions; this hinted that it was in all likelihood a stupid inquiry...I am now convinced that, perhaps, most of econometrics could be useless - much of what financial statisticians know would not be worth knowing.

Source:   Fooled By Randomness

It was confidently believed that the scientific success of the industrial revolution could be carried through into the social sciences, particularly with such movements as Marxism. Pseudoscience came with a collection of idealistic nerds who tried to create a tailor-made society, the epitome of which is the central planner. Economics was the most likely candidate for such a science; you can disguise charlatanism under the weight of equations, and nobody can catch you since there is no such thing as a controlled experiment. Now the spirit of such methods, called "scientism" by its detractors, continued past Marxism, into the discipline of finance as a few technicians thought that mathematical knowledge could lead them to understand markets. The practice of "financial engineering" came along with massive doses of pseudoscience. Practitioners of these methods measure risks, using the tool of past history as an indication of the future. We will just say at this point that the mere possibility of distributions [of data] not being stationary makes the entire concept seem like a costly (perhaps very costly) mistake. [presciently written before the Great Recession of 2008-09]

Source:   Fooled By Randomness

What has gone wrong with the development of economics as a science? Answer: There was a bunch of intelligent people who felt compelled to use mathematics just to tell themselves that they were rigorous in their thinking, that theirs was a science. Someone in a great rush decided to introduce mathematical modeling techniques without considering the fact that either the class of mathematics they were using was too restrictive for the class of problems they were dealing with, or that perhaps they should be aware that the precision of the language of mathematics could lead people to believe that they had solutions when in fact they had solutions when in fact they had none. Indeed the mathematics they dealt with did not work in the real world, possibly because we needed richer classes of processes - and they refused to accept the fact that no mathematics at all was probably better.

Source:   Fooled By Randomness

Ben Graham and David Dodd put it this way more than sixty years ago in the second edition of Security Analysis, the bible of value investors: “the relation between different kinds of investments and the risk of loss is entirely too indefinite, and too variable with changing conditions, to permit of sound mathematical formulation.”

Source:   The Most Important Thing

The bottom line is that, looked at prospectively, much of risk is subjective, hidden and unquantifiable.

Source:   The Most Important Thing

Where does that leave us? If the risk of loss can’t be measured, quantified or even observed—and if it’s consigned to subjectivity—how can it be dealt with? Skillful investors can get a sense for the risk present in a given situation. They make that judgment primarily based on (a) the stability and dependability of value and (b) the relationship between price and value. Other things will enter into their thinking, but most will be subsumed under these two.

Source:   The Most Important Thing

Bruce [Greenwald] has put it admirably into words: “There’s a big difference between probability and outcome. Probable things fail to happen—and improbable things happen—all the time.” That’s one of the most important things you can know about investment risk.

Source:   The Most Important Thing

Projections tend to cluster around historic norms and call for only small changes. … The point is, people usually expect the future to be like the past and underestimate the potential for change.

Source:   The Most Important Thing

The greatest risk doesn’t come from low quality or high volatility. It comes from paying prices that are too high. This isn’t a theoretical risk; it’s very real.

Source:   The Most Important Thing

Controlling the risk in your portfolio is a very important and worthwhile pursuit. The fruits, however, come only in the form of losses that don’t happen. Such what-if calculations are difficult in placid times.

Source:   The Most Important Thing

When things are going well, extrapolation introduces great risk. Whether it’s company profitability, capital availability, price gains, or market liquidity, things that inevitably are bound to regress toward the mean are often counted on to improve forever.

Source:   The Most Important Thing

I’m firmly convinced that (a) it’s hard to know what the macro future holds and (b) few people possess superior knowledge of these matters that can regularly be turned into an investing advantage. There are two caveats, however: 1) The more we concentrate on smaller-picture things, the more it’s possible to gain a knowledge advantage. 2) With hard work and skill, we can consistently know more than the next person about individual companies and securities, but that’s much less likely with regard to markets and economies. Thus, I suggest people try to “know the knowable.”

Source:   The Most Important Thing

Whatever limitations are imposed on us in the investment world, it’s a heck of a lot better to acknowledge them and accommodate than to deny them and forge ahead. Oh yes; one other thing: the biggest problems tend to arise when investors forget about the difference between probability and outcome—that is, when they forget about the limits on foreknowledge: when they believe the shape of the probability distribution is knowable with certainty (and that they know it), when they assume the most likely outcome is the one that will happen, when they assume the expected result accurately represents the actual result, or perhaps most important, when they ignore the possibility of improbable outcomes.

Source:   The Most Important Thing

The achievements of the natural sciences were so impressive that the social sciences sought to imitate them. Not all of the social sciences - for instance, anthropology insists on telling stories rather than establishing universally valid theories - but economic theory in particular has made a valiant effort to imitate natural science.

Source:   The Alchemy of Finance

The thinking of the active participant is very different from that of the outside observer. We may call it "organic" as distinct from rational. Scientists are interested in timeless generalizations and statistical probabilities; participants need to focus on the one particular case in which they are participating. Probabilities and generalizations can be useful, but they are misleading if they are based on the viewpoint of the outside observer. That is what happened in economic theory.

Source:   The Alchemy of Finance

If participants labor under the misapprehension that the market is always right, the feedback they get is misleading. Indeed, the belief in efficient markets renders markets more unstable by short-circuiting the corrective process that would occur if participants recognized that markets are always biased. The more the theory of efficient markets is believed, the less efficient the markets become.

Source:   The Alchemy of Finance

My objection to [macro forecasting] is not that it is unreasonable in theory. It is that in the current state of human knowledge about the economics which deal with forecasting future business trends, it is impossible to apply this method in practice. The chances of being right are not good enough to warrant such methods being used as a basis for risking the investment of savings. This may not always be the case. It might not even be the case five or ten years from now. At present, able men are attempting to harness electronic computers to establish “input-output” series of sufficient intricacy that perhaps at some future date it may be possible to know with a fair degree of precision what the coming business trends will be. When, if ever, such developments occur, the art of common stock investment may have to be radically revised. Until they occur, however, I believe that the economics which deal with forecasting business trends may be considered to be about as far along as was the science of chemistry during the days of alchemy in the Middle Ages. In chemistry then, as in business forecasting now, basic principles were just beginning to emerge from a mysterious mass of mumbo-jumbo. However, chemistry had not reached a point where such principles could be safely used as a basis for choosing a course of action.

Source:   Common Stocks and Uncommon Profits

The amount of mental effort the financial community puts into this constant attempt to guess the economic future from a random and probably incomplete series of facts makes one wonder what might have been accomplished if only a fraction of such mental effort had been applied to something with a better chance of proving useful.

Source:   Common Stocks and Uncommon Profits

In contrast to guessing which way general business or the stock market may go, he should be able to judge with only a small probability of error what the company into which he wants to buy is going to do in relation to business in general. Therefore he starts off with two advantages. He is making his bet upon something which he knows to be the case, rather than upon something about which he is largely guessing.

Source:   Common Stocks and Uncommon Profits

In the last few years, too much attention has been paid to a concept that I believe is quite fallacious. I refer to the notion that the market is perfectly efficient. Like other false beliefs in other periods, a contrary view may open up opportunities for the discerning.

Source:   Common Stocks and Uncommon Profits

If the market is efficient in prospect, then the nexus of analysis that leads to this efficiency must be collectively poor.

Source:   Common Stocks and Uncommon Profits

Efficient market theory grew out of the academic School of Random Walkers. These people found that it was difficult to identify technical trading strategies that worked well enough after transactions costs to provide an attractive profit relative to the risks taken. I don't disagree with this. As you have seen, I believe that it is very, very tough to make money with in and out trading based on short-term market forecasts. Perhaps the market is efficient in this narrow sense of the word.

Source:   Common Stocks and Uncommon Profits

We have all heard this mantra of diversification for so long that we have become intellectually numb to its inevitable consequence: mediocre results. Both active and index funds do offer diversification, but, in general, neither strategy will give you exceptional returns.

Source:   The Warren Buffett Way

What strategies lend themselves best to low turnover rates? One possible approach is a low-turnover index fund. Another is a focus portfolio. “It sounds like premarital counseling advice,” say Jeffrey and Arnott, “namely, to try to build a portfolio that you can live with for a long, long time.”

Source:   The Warren Buffett Way

Risk, for Buffett, is inextricably linked to an investor’s time horizon. This alone is the single greatest difference between how Warren Buffett thinks about risk and how modern portfolio theory frames risk. If you buy a stock today with the intention of selling it tomorrow, Buffett explains, then you have entered into a risky transaction. The odds are no better than the toss of a coin—you will lose about half the time. However, says Buffett, if you extend your time horizon out to several years, the probability of its being a risky transaction declines meaningfully, assuming of course that you have made a sensible purchase.

Source:   The Warren Buffett Way

...you can easily see how applying the Buffett approach will put you in conflict with its proponents. Not only are you intellectually at odds with modern portfolio theorists, but you are also vastly outnumbered, both in the classroom and the workspace. Embracing the Warren Buffett Way makes you a rebel looking out across the field at a much larger army of individuals who invest totally differently. As you will learn, being an outcast has its own emotional challenges.

Source:   The Warren Buffett Way

...the success of some who continually beat the major indexes—most notably Warren Buffett—suggests that the efficient market theory is flawed. Others, Buffett included, argue that the reason most money managers underperform the market is not because it is efficient, but because their methods are faulty.

Source:   The Warren Buffett Way

Probabilities alone are insufficient when payoffs are skewed.

Source:   More Than You Know

Many models in standard finance theory assume that stock price changes are normally distributed around the well-known bell curve. A normal distribution is a powerful analytical tool, because you can specify the distribution with only two variables, the mean and standard deviation. The model, despite its elegance, has a problem: it doesn’t describe real world results very well. In particular, the model is remiss in capturing “fat tails”: infrequent but very large price changes. The failure of risk-management models to fully account for fat tails has led to some high-profile debacles, including the 1998 demise of the hedge fund Long Term Capital Management.

Source:   More Than You Know

Humans have a deep-seated desire to link cause and effect. Unfortunately, markets do not easily satisfy this desire. Unlike some mechanical systems, you can’t understand markets by looking at the parts. Reductionism doesn’t work.

Source:   More Than You Know

[Gary] Gladstein, who has worked closely with Soros for fifteen years, describes his boss as operating in almost mystical terms, tying Soros’s expertise to his ability to visualize the entire world’s money and credit flows. “He has the macro vision of the entire world. He consumes all this information, digests it all, and from there he can come out with his opinion as to how this is going to be sorted out. He’ll look at charts, but most of the information he’s processing is verbal, not statistical.”

Source:   More Than You Know

Much of the real world is controlled as much by the “tails” of distributions as by means or averages: by the exceptional, not the mean; by the catastrophe, not the steady drip; by the very rich, not the “middle class.” We need to free ourselves from “average” thinking. —Philip Anderson, Nobel Prize recipient in physics, “Some Thoughts About Distribution in Economics”

Source:   More Than You Know

In a triumph of modeling convenience over empirical results, finance theory treats price changes as independent, identically distributed variables and generally assumes that the distribution of returns is normal, or lognormal. The virtue of these assumptions is that investors can use probability calculus to understand the distribution’s mean and variance and can therefore anticipate various percentage price changes with statistical accuracy. The good news is that these assumptions are reasonable for the most part. The bad news, as physicist Phil Anderson notes above, is that the tails of the distribution often control the world.

Source:   More Than You Know

Is there a mechanism that can help explain these episodic lunges? I think so. As I have noted in other essays, markets tend to function well when a sufficient number of diverse investors interact. Conversely, markets tend to become fragile when this diversity breaks down and investors act in a similar way (this can also result from some investors withdrawing). A burgeoning literature on herding addresses this phenomenon. Herding is when many investors make the same choice based on the observations of others, independent of their own knowledge. Information cascades, another good illustration of a self-organized critical system, are closely linked to herding.

Source:   More Than You Know

The standard model for assessing risk, the capital-asset-pricing model, assumes a linear relationship between risk and reward. In contrast, nonlinearity is endogenous to self-organized critical systems like the stock market. Investors must bear in mind that finance theory stylizes real world data. That the academic and investment communities so frequently talk about events five or more standard deviations from the mean should be a sufficient indication that the widely used statistical measures are inappropriate for the markets.

Source:   More Than You Know

The risk-reducing formulas behind portfolio theory rely on a number of demanding and ultimately unfounded premises. First, they suggest that price changes are statistically independent from one another. . . . The second assumption is that price changes are distributed in a pattern that conforms to a standard bell curve.

Source:   More Than You Know

Do financial data neatly conform to such assumptions? Of course, they never do. —Benoit B. Mandelbrot, “A Multifractal Walk down Wall Street”

Source:   More Than You Know

In an important and fascinating book, Why Stock Markets Crash, geophysicist Didier Sornette argues that stock market distributions comprise two different populations, the body (which you can model with standard theory) and the tail (which relies on completely different mechanisms). Sornette’s analysis of market drawdowns convincingly dismisses the assumption that stock returns are independent, a key pillar of classical finance theory. His work provides fresh and thorough evidence of finance theory’s shortcomings.

Source:   More Than You Know

Complex adaptive systems include governments, many corporations, and capital markets. Efforts to assert top-down control of these systems generally lead to failure, as happened in the former Soviet Union. Thinking about the market as a complex adaptive system is in stark contrast to classical economic and finance theory, which depicts the world in Newtonian terms. Economists treat agents as if they are homogenous and build linear models—supply and demand, risk and reward, price and quantity. None of this, of course, much resembles the real world.

Source:   More Than You Know

The stock market has all of the characteristics of a complex adaptive system. Investors with different investment styles and time horizons (adaptive decision rules) trade with one another (aggregation), and we see fat-tail price distributions (nonlinearity) and imitation (feedback loops). An agent-based approach to understanding markets is gaining broader acceptance. But this better descriptive framework does not offer the neat solutions that the current economic models do.

Source:   More Than You Know

When describing markets, financial economists generally assume a definable tradeoff between risk and reward. Unfortunately, the empirical record defies a simple risk-reward relationship. As Benoit Mandelbrot has argued, failure to explain is caused by failure to describe.

Source:   More Than You Know