[MUSIC] Albert Einstein described the challenge of translating a theory into practice succinctly. I quote, In theory, theory and practice are the same. In practice, they are not. Unquote. Using theory and models to explain complicated real world phenomena can work brilliantly in the natural sciences but less so in the social and behavioral sciences, which deal with the relationships between human beings. Take a relatively young subject of finance, which many think is about math and numbers. Quantitative evidence should convince people about financial matters, right? Wrong. As we explore this specialization together, you can see finance, which is actually a branch of economics, is firmly rooted in the social sciences. And while the study of finance and related subjects is full of robust and sophisticated theories that are awarded Nobel prizes annually, some of which we will explore, there are also increasingly strong lines of criticism around how these theories have little use for practitioners. Take for example the growing disaffection for a famous theory in finance called the Efficient Market Hypothesis or EMH. An idea developed in the 1960's and credited to the American economist, Eugene Fama, which has searched that market prices incorporate all types of information available to market participants. And as a result, prices are neither over nor undervalued. This is why, as we mentioned in an earlier video, it's often considered impossible to beat the market. Financial markets in particular, and specially currency markets, are considered so efficient that they are set to almost achieve perfection in terms of determining the true value of any given product or service in the market place. This ideal of perfection in the market place comes from the most basic model in economics, a situation in which everyone has free access to information that are zero taxes and transactions cost, and supply equals demand. Altogether, these conditions create an equilibrium price or the true value making the marketplace perfect. In other words, participants find themselves in a level playing field, and nobody can extract abnormal profits that reveal true values that were previously hidden. This hypothesis maintains that financial markets are pretty much sufficient, and this has dominated academic thinking about finance for decades. In fact, there are three variants of the efficient market hypothesis, which include the analysis of past information, also called the weak form, current information called the semi strong form, and privileged or inside information known as the strong form. Note that as represented by the concentric circles in the figure, moving into a circle of stronger information implies that you also know the weaker information, all the information represented in the smaller circles. In the context of these information sets, thousands of tests have been conducted to examine the effect of information on price. These tests have generally reinforced the claim that prices on traded assets, like stocks and bonds, automatically factor in all available information. The implications of these are huge. For example, if prices are neither over or undervalued, expert stock selection and market timing, meaning when to buy or sell, is useless since there is no way that an expert can have some extra insight into what the market will do. Thus, the significant implications of the efficient market hypothesis is that superior investment performances either mostly a function of luck or results from taking on a lot of risk. Let's explore these ideas a little further. An important assumption driving the EMH is that investors have rational expectations. This does not mean that everyone is rational, but it does assume that people investing in markets make decisions that factor in all available information. Furthermore, if an investor uses a particular model to understand the market, they assume that the predictions of the model are valid. There are a couple of assumptions made here, and what they ultimately come down to is that the various investors are using various models, all of which are using all available information. Then on average, financial model predictions about prices converge to reach particular true value. Let's unpack these points about the EMH through a basic example of the stock market to better understand the effects of convergence. First, applying the rational expectations assumptions to stock prices suggest that the market will reflect them equilibrium price. Or in other words, most people, when given the same information, will arrive at the same assessment of value and conclude that the price is right. This is convergence. Then, because the prices in the market reflect the correct value, you cannot profit from identifying mispriced stocks because these, by definition, cannot exist in an efficient market. This also means that novices will do just as well as professionals since neither can beat the market, which just means to outperform it. Again, the assumption is that any informational advantage in an efficient market is already reflected in the prices since market participants will have factored in these pieces of information when buying and selling. Indeed, many financial advisers who believe in the efficient market hypothesis recommend that their client simply buy a market index, which mimics or represents the value of the stock market. Instead of paying fees to bankers and analysts who recommend customized portfolios consisting of the their own unique stock picks. In other words, it is unlikely that one individual is likely to know more than hundreds of thousands of investment managers worldwide who have acted on the same available information. Now, let's consider another implication of the efficient market hypothesis. This is based on prices moving in what is called a random walk, which simply means that they cannot be predicted. This suggests that by using trends in technical analysis that identify patterns to gain a profit advantage is, again, basically a waste of time. Altogether, these are the effects of convergence. But is the efficient market hypothesis and convergence a reality? Are individuals equally as capable of making good investment choices without bowing to the market? Professor Malkiel from Princeton University claimed in his bestselling book, A Random Walk Down Wall Street, that quote, blindfolded monkey throwing darts at a newspaper's financial pages could select a portfolio that would do just as well as one carefully selected by experts, unquote. Sounds like fun. But before we adopt a dart-throwing monkey, let's consider evidence of some assets consistently outperforming others. For example, take a look at this chart. What's interesting here is that over long periods, the stocks of small companies, indicated with the red line, outperform those of big companies shown by the blue line. There are several reasons for this, including that smaller companies have more growth opportunities and are also often implicated in a more volatile business environment that exposes them to more risk, which should be rewarded with more return over time. So a portfolio of, say, 30 stocks randomly selected by our pet monkey is likely to outperform any professionally-managed portfolio, because it has a small number of very large companies that account for most of the total value. And a large number of small companies, which as we saw in the chart, do better than large companies. Because of the presence of small company stocks, the portfolio will therefore do better, which has nothing to do with the monkey or shooting darts. In addition, other controlled studies show experienced traders significantly outperforming novices, which again supports the intuition that experience and strategy are likely important factors when selecting stocks and not just random selection. As mentioned, economic models suggest price convergence. However, when it comes down to how risk is understood, human behavior points towards divergence. Let's explore this notion of divergence with a quick thought experiment. For this thought experiment, I'm going to ask you to respond immediately to a question. Please don't think of more than a few seconds before you respond. I'd like you to write down or key the first word or two that comes to your mind as you hear me say one word. Are you ready? That word is risk. What word or words would you choose to describe risk? Repeating this experiment in the classroom always results in many different responses. The most common words to describe risk that I've heard in the past can be seen in this chart here. Clearly, risk is a multi-dimensional concept. From the previous video on cash flows, if cash flow is insufficient for a company, not only do the operations become riskier, other serious repercussions begin to surface. On the other hand, many see risk as both danger and opportunity, which are opposing ideas. But as we emphasized in Course 2 on markets in the video on turbulence, that markets both destroy world and present opportunities for correction and world creation. Looking, again, at the list of responses to the word risk, the word that is closest to what you'd find in the finance book is volatility. One reason to define risk is volatility, let's say, of future cash flows or prices or returns, is that it lends itself well to measurement. We'll come back to how to quantify risk in the next video segment. But for now, let's return to the issue of market efficiency as it relates to risk. Most critics of the efficient market hypothesis, on the idea of convergence, challenge the underlying assumptions of the model that all market participants have equal information on stock investment properties. For them, the actions of buyers and sellers are better explained by psychological, social, and emotional factors as opposed to solely relying on rationally interpreting available information. A lot of recent literature has explored whether these psychological, social, and emotional factors are better predictors of price movements. Take for example prospect theory, which suggests that investors get more stressed out about prospective losses than they are happy from gains. Imagine your own reaction to losing $10,000 compared to winning the same amount. This might explain why investors hold on to a stock that has lost a lot, hoping it will bounce back, compared to realizing gains on the same stock. Another is herding. Here, money tends to flow to stocks that are performing well, I'm investing because others are, rather than taking money out from stocks that are underperforming. Regret theory is based on the realization that you've made an error. So you avoiding selling, avoiding the embarrassment of reporting a loss, which would be admitting to making a mistake. In addition, investors are known to be overoptimistic when the market goes up and extremely pessimistic when the market goes down. All of these examples are irrational that result in mispricing and inefficient markets. However, trying to outguess the market is very hard, and it's not clear how to use these behavioral theories to consistently identify opportunities to make money. Despite the opposing views of efficient markets and behavioral theory, it can be helpful to further understand the different sources of risk that make distinctions that help us to mitigate some aspects of risk. For example, systematic or systemic risk factors affect the entire market and are far-reaching in that they can impact commodity prices, interest rates, and the like. Whereas business risk is more closely associated with unique factors concerning, say, unions, changes in technology, customer relationships, etc., and are likely to impact the price of a particular company's stock. A much smaller scale affect than that of systematic risk. If we take our responses to the risk thought experiment, and we develop it further, we can see that there are a multitude of types of risks. Understanding the various sources of risks helps us to better manage it. A crucial distinction is the difference between risk and uncertainty. Economist Frank Knight was the first to address this distinction in detail in his 1921 book, Risk, Uncertainty and Profit. He defined risk as a situation where decision-makers are faced with informed forecast of unknown outcomes. For example, one can use all available information to predict the level of interest rates, oil prices, stock and bond prices, and so on. But ultimately, they still have unknown outcomes. With uncertainty, we have no available information to guide decision-making. In other words, uncertainty refers to completely unforeseen or random events. They are sometimes referred to as black swan events, because although black swans exist, nobody ever expects to see one. Catastrophic events caused by, say terrorism, infectious disease, unexpected government collapse, and so on, are examples of uncertainty. So in summary, we know how to assess risk as much as we know how to use information to determine the price of any given thing, whereas we do not know how to assess uncertainty. This video segment has examined both sides of efficient and inefficient markets to help us understand the complexities of how participants in the financial marketplace influence price. On the one hand, by assuming market perfection, we can understand that market prices absorb and adjust information rapidly. But there are no money-making machines out there that consistently produce excess profit as well as the fundamental positive relationship between risk and return. Market efficiency also rules out market illusions and bubbles such as inflated values of stocks sustained for long periods of time. While these insights are extremely valuable as a simplified, easy to grasp representation of complex phenomenon, they come with lots of assumptions that can be challenged. And this is where market corrections are rooted in reality that bring us full circle in terms of the theory versus practice challenge that we began with. Whereas the theory of pricing simplifies reality, the practice of pricing implicates a complex set of human behaviors that can accompany panics resulting from unanticipated events. Given the history and recurrence of turbulence and severe corrections in the marketplace, and given our propensity to be conservative in these situations, perhaps we need to know how to brace ourselves for the next inevitable correction. We address this important idea right after a quick overview of how risk is measured. Measurement attempts to incorporate risk and how prices are determined. As we'll see, these measurement tools are necessary but insufficient for efficient pricing, which again leads to market corrections. This suggests we need to think beyond the measurement box and generate other alternatives to predicting, preparing, and protecting ourselves against financial turbulence, which after all is what finance for everyone is all about.
