We want to conclude this chapter by taking a look at theresearch methodsthat are used in financial economics. After all, we want to know where the results we are studying come from and how we can possibly add new results.
Albert Einstein is known to have said that “there is nothing more practical than a good theory.” But what is a good theory? First of all, a good theory is based on observable assumptions. Moreover, a good theory should have testable implications – otherwise it is a religion which cannot be falsified. This falsification aspect cannot be stressed enough.1Finally, a good theory is a broad generalization of reality that captures its essential features. Note that a theory does not become better if it becomes more complicated.
But what are our observations and implications? There are essentially two ways to gather empirical evidence to support (or falsify) a theory on financial markets:
one way is to study financial market data. Some of this data (e.g., stock prices) is readily available, some is difficult to obtain for reasons such as privacy issues or time constraints. The second way is to conduct surveys and laboratory experiments, i.e., to expose subjects to controlled conditions under which they have to perform financial decisions.
Both approaches have their advantages and limitations: market data is often noisy, depends on many uncontrollable factors and might not be available for a spe- cific purpose, but by definition always comes from real life situations. Experimental
1Steve Ross, the founder of the econometric Arbitrage Pricing Theory (APT ), for example, claims that “every financial disaster begins with a theory!” By saying this, he means that those who start trading based on a theory are less likely to react to disturbing facts because they are typically in love with their ideas. Falsification of their beloved theory is certainly not their goal!
data often suffers from a small number of subjects, necessarily unrealistic settings, but can be collected under controlled conditions. Today, both methods are frequently used together (typically, experiments for the more fundamental questions, like decision theory, and data analysis for more applied questions, like asset pricing), and we will see many applications of these approaches throughout this book.
So, what is a typical route that research in financial economics is taking?
Often a research question is born by looking at data and finding empirically robust deviations from random behavior of asset prices. The next step is then to try to explain these effects with testable hypotheses. Such hypotheses can rely on classical concepts or on behavioral or evolutionary approaches. In the latter cases, laboratory tests have often been performed before in order to test these approaches under controlled conditions.
The role of empirical findings and its interplay with theoretical research in finance cannot be overstressed. To quote Hal Varian[Var93b]:
Financial economics has been so successful because of this fruitful relationship between theory and data. Many of the same people who formulated the theories also collected and analyzed the data. This is a model that the rest of the economic profession would do well to emulate.
In any case, if you want to discover interesting effects in the stock market, the main requirement is that you understand the “Null Hypothesis”. In this case, it is what a rational market looks like. Therefore a big part of this book will deal with traditional finance that explains the rational point of view.
We have now concluded our bird’s eye view on financial economics and on the contents of this book. Before we dive into financial markets with their manifold interactions, we start with a more basic situation: in the next chapter we will study the individual decisions a person makes with financial problems. This leads us to the general field of decision theory which will later serve us as a building block for understanding more complex interactions on the market that involve not only one, but many persons.
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Decision Theory
As soon as questions of will or decision or reason or choice of action arise, human science is at a loss.
NOAMCHOMSKY
How should we decide? And howdowe decide? These are the two central questions of Decision Theory: in theprescriptive (rational)approach we ask how rational decisions should be made, and in thedescriptive (behavioral)approach we model the actual decisions made by individuals. Whereas the study of rational decisions is classical, behavioral theories have been introduced only in the late 1970s, and the presentation of some very recent results in this area will be the main topic for us.
In later chapters we will see that both approaches can sometimes be used hand in hand, for instance, market anomalies can be explained by a descriptive, behavioral approach, and these anomalies can then be exploited by hedge fund strategies which are based on rational decision criteria.
In this book we focus on the part of Decision Theory which studies choices between alternatives involving risk and uncertainty.Riskmeans here that a decision leads to consequences that are not precisely predictable, but follow a known probability distribution. A classical example would be the decision to buy a lottery ticket.Uncertaintyorambiguitymeans that this probability distribution is at least partially unknown to the decision maker.
In the following sections we will discuss several decision theories connected to risk. When deciding about risk, rational decision theory is largely synonymous with Expected Utility Theory, the standard theory in economics. The second widely used decision theory is Mean-Variance Theory, whose simplicity allows for manifold applications in finance, but is also a limit to its validity. In recent years, Prospect Theory has gained attention as a descriptive theory that explains actual decisions of persons with high accuracy. At the end of this chapter, we discuss time-preferences and the concept of “time-discounting”.
© Springer-Verlag Berlin Heidelberg 2016
T. Hens, M.O. Rieger,Financial Economics, Springer Texts in Business and Economics, DOI 10.1007/978-3-662-49688-6_2
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Before we discuss different approaches to decisions under risk and how they are connected with each other, let us first have a look at their common underlying structure.