described earlier, replication begins with the science, the descriptive model of underlier behavior. Modern portfolio theory rests on the efficient market hypothesis (EMH), a framework that has come under renewed and very severe attack since the onset of the great financial crisis of 2007–2008. Let's try to understand what it proposes.
The Efficient Market Hypothesis
Empirically, no one is very good at stock price prediction, whether using magical thinking or deep fundamental analysis. To be sure, there have been a few investors who have significantly outperformed the market in the past. Whether you believe their performance was due to luck or to skill, to significantly outperform the market you do not need to be very good at stock price prediction. Being right just 55 % to 60 % of the time, consistently, over many trades, is remarkable and can lead to great profit.
In the 1960s, faced with this failure at price prediction, a group of academics associated with Eugene Fama at the University of Chicago developed what has become known as the efficient market hypothesis. Over the years, many formulations of the theory have evolved, some more mathematical and rigorous, and some less so. Economists have defined strong, weak, and other kinds of “efficiency.” No matter how we define it, though, at its core the EMH acknowledges the following more or less true fact of life:
It is difficult or well-nigh impossible to successfully and consistently predict what is going to happen to a stock's price tomorrow based on all the information you have today.
The EMH formalizes this concept by stating that it is impossible to beat the market in the long run, because current prices reflect all current economic and market information.
Converting the experience of failed attempts at systematic stock price prediction into a hypothesis was a fiendishly clever jiu-jitsu response on the part of economists. It was an attempt to turn weakness into strength: “I can't figure out how things work, so I'll make the inability to do that a principle.”
Uncertainty, Risk, and Return
It might seem as though the efficient market hypothesis claims that the stock's price and value are identical, and that nothing more can be said. That's not the case. Let's proceed to understand how the assumption of efficient markets can lead to a model for valuing securities. The elephant in the room of finance, as in the realm of all things human, is the unknown future. Uncertainty implies risk; risk means danger; danger means the possibility of loss.
In economics, thoughtful people have come to distinguish between quantifiable and unquantifiable uncertainty. Examples of unquantifiable uncertainty include the likelihood of a revolution in Russia within two years, the probability of a terrorist attack in midtown Manhattan this year, or the chance of finding intelligent life on another planet. Not only are all of these events highly uncertain, but any model that we would develop to try to predict these events is likely to be highly subjective. There is no way of honestly estimating these probabilities. This type of probability is often referred to as uncertainty or Knightian uncertainty. We can say that these events are likely or unlikely or very unlikely, but not much more.
In some rare and somewhat idealized cases, uncertainty is quantifiable. Some economists like to define risk as quantifiable uncertainty. A good example is the uncertainty involved in tossing an unbiased coin: will it come up heads or tails? The probability that an unbiased coin lands on heads is equal to the probability that it lands on tails, 1/2. Similarly, one can determine the probability for three successive heads followed by two successive tails to be (1/2)5, or 1/32. This is the frequentist definition of probability that defines the concept in terms of expected frequency of occurrences, in the limit, for an infinite number of tosses.
You might argue that quantifiable uncertainty is unrealistic. On the one hand, a perfect coin is a Platonic ideal and no coin is perfectly fair. On the other hand, a coin toss is, in some theoretical sense, predictable. If we knew the velocity and angle of the flick, how the air was moving around the coin as it spun, and the irregularities of the floor upon which the coin was bouncing, we could predict the outcome of the coin flip with a high degree of accuracy. If we are willing to ignore quantum mechanics, we could argue that there are no truly random events, only pseudo-random ones. From a practical standpoint, though, outside of a laboratory, even without quantum mechanical effects, there are so many factors that might impact the result of a coin toss that we may as well consider it to be a random event.
In human affairs, frequentist probabilities are rare. The world is constantly changing, and experiments with humans cannot easily be repeated with the same initial conditions. Importantly, human beings learn from experience. For example, credit markets after the great financial crisis won't behave like credit markets before the crisis, because we have all learned a lesson, at least temporarily.
Put another way, human institutions display hysteresis: Their current state depends on their entire history. Though the history of the world doesn't affect a coin toss, the history of the world does have a bearing on the likelihood of a political revolution or the next change in a stock's price. The uncertainty in the behavior of a stock's price is qualitatively different from the uncertainty of a coin flip, because the behavior of people is very different from the behavior of coins. The likelihood of a stock market crash is not like the likelihood of throwing five tails in succession, because market crashes are societal events, and society remembers the last crash and fears the next. A coin doesn't fear a sequence of five tails, and isn't affected by the other coins in your pocket.
The Behavior of a Share of Stock
A company – take Apple Inc., for example – is a tremendously complex and structured endeavor. Apple has tens of thousands of employees, owns or leases buildings in many countries, designs products ranging from power plugs and cables to desktop and laptop computers through iPhones, iPads, and the Apple Watch. It manufactures some of them on its own, and outsources the manufacturing of others. It distributes its products through Apple's website and stores as well as via third parties, and sells music, videos, and books over the Internet. Apple advertises, provides product support, maintains websites, and carries out research and development.
Amazingly, the entire economic value of this organization can, in theory, be summed up in just one number, the quoted price of a share of Apple's stock.2 The quoted stock price is the amount of money that was required to buy or sell just one incremental share of the company the last time the stock traded. Financial modeling is an attempt to project the value of the entire enterprise into that single number that symbolizes its value. It aims to tell you what you should pay today for a share of the company's future performance.
The task of a would-be forecaster sounds impossibly difficult, and it gets worse. In order to predict the movement of stock prices, it is not enough to understand all of the complexities of a corporation and its place in the economy. In addition, we need to understand how all the other participants in the market view the company as well. Predicting the direction of stock prices, as Keynes wrote, is a lot like predicting the winner of a traditional beauty contest; you are not trying to figure out who is the most attractive, but who the judges think is the most attractive (Keynes 1936). In the long run, fundamentals, the state of the economy, and the state of the company count. Sentiment can maintain its influence for only so long. In the short run, though, people's opinions and passions count for a lot. But then again, the short run influences the long run. Short-term changes in the price of a company's stock will affect the behavior of the company, its customers, and its creditors; psychological reality and economic reality interact, and are in fact indivisible.3 When people thought Lehman Brothers might go bankrupt in late 2008, they wouldn't continue to lend it money, so it went bankrupt.
The more you think about it, if you are honest and introspective, the more you realize that valuation is a vastly complex problem involving
To be clear, the total value of a firm, what financial analysts refer to as a company's enterprise value, includes the value of both the company's stock and its debt, and Apple, like most large firms, does issue debt. In fact, in 2013 Apple issued what was, at the time, the largest corporate debt issue in history. The value of a company's debt is generally fixed and largely predictable, except perhaps when it enters a credit crisis. The interesting part of determining the value of a company is, in most cases, almost entirely concerned with determining the value of its stock. This is what we focus on here, though more advanced models do treat the enterprise value as the fundamental underlier.
Ole Bjerg, a philosopher working in the framework of Slavoj Žižek, sees the corporation as “the real” and the stock price as its “symbol,” and this seems right. What interests Bjerg is the way fantasy and ideology fill the gap between reality and symbol, as discussed in his book
