What we know today as behavioral finance was initiated some three decades ago by a small number of people who asked questions seldom asked before and offered answers not offered before. Today, many people are engaged in behavioral finance, and there Is wide disagreement about its boundaries and frontiers. Many see behavioral finance mainly as a refutation of the efficient market hypothesis and as a tool to beat the market. Behavioral finance is an attempt to understand investors and he reflection of their interactions in financial markets.
Such understanding can, for example, help investment professionals tamp down the overconfidence of investors in their ability to beat the market. Or it can help investment professionals cater to this overconfidence. Standard approach to Flanagan economics or Traditional finance. Standard finance, also known as modern portfolio theory, has four foundation blocks: (1) investors are rational; (2) markets are efficient; (3) investors should design their portfolios according to the rules of mean-variance oratorio theory and, In reality; and (4) expected returns are a function of risk and risk alone.
Modern portfolio theory is no longer very modern, dating back to the late sass and early sass. Morton Miller and Franco Modeling described investors as rational in 1961. Eugene Fame described markets as efficient in 1965. Harry Margarita prescribed mean-variance portfolio theory in its early form in 1952 and in its full form In 1959. William Sharpe adopted mean-variance portfolio theory as a description of Investor behavior and In 1964 Introduced the capital asset pricing theory (CAMP). According to this theory, differences in expected returns are determined only by differences in risk, and beta is the measure of risk.
Behavioral finance contradiction to these four basic foundation blocks. Investors are normal : According to traditional finance theory, rational decision makers evaluate all available information and possible outcomes, and make rational choices that lead to maximizing expected utility. That is, they act in an unbiased fashion and make decisions that maximize their self-interests. Those who make suboptimal decisions would be punished through poor outcomes. The basis of this notion is classical decision theory. According to finance theorists, investors are risk averse and have to take on risk.
Thus, classical decision theory, rationality, utility, and risk aversion serve as cornerstones of the traditional finance paradigm. But unreality, investors are normal people, who will try to obtain highest possible utility given: Budget constraints Available Information. Normal investor will consider personal utility. Normal investor depending on their requirements adjust the risk, I. E they are not necessarily risk averse. Friedman and Savage (1948) observed that people buy lottery sickest because they aspire to reach higher social classes, whereas they buy insurance as protection against falling into lower social classes.
Margarita (Bibb) clarified the observation of Friedman and Savage by noting that people aspire to move up from their current social class or “customary wealth. ” So, people with $10,000 might accept lottery-like odds in the hope of winning $1 million, and people with $1 million might accept lottery-like odds in the hope of winning $100 million. Markets are efficient: The rationality of market participants feeds into another dominant paradigm in instance – the efficient markets hypothesis (MME), which asserts that financial markets are rational and “informational efficient. According to the MME, stock prices are always in equilibrium and hence their market-based prices are correct. Thus, a stocks market price is equal to its intrinsic value. As a result, investors cannot consistently achieve returns in excess of average market returns on a risk-adjusted basis, given the information available at the time they make the investment. We will briefly discuss four areas in which behavior in the real world seems most at odds with the above theory: Volume: Standard models of asset markets predict that participants will trade very little.
The reason is that in a world where everyone knows that traders are rational (l know that you are rational, you know that I am rational, and I know that you know that I am rational), if I am offering to buy some shares of IBM Corporation and you are offering to sell them, I have to wonder what information you have that I do not. Of course, pinning down exactly how little volume should be expected in this world is difficult, because in the real world people have liquidity and refinancing needs, but it seems fee to say that 700 million shares a day on the NYSE is much more trading than standard market models would expect.
Similarly, the standard approach would not expect mutual fund managers to turn over their portfolios once a year. Volatility. In a rational world, prices change only when news arrives. Since Robert Chiller’s early work was published in 1981, economists have realized that aggregate stock prices appear to move much more than can be Justified by changes in intrinsic value (as measured by, say, the present value of future dividends). The Equity Premium Puzzle.
Historically, the equity premium in the United States and elsewhere has been huge. For example, a dollar invested in U. S. T-bills on January 1, 1926, would now be worth about $14; a dollar invested in large-cap U. S. Stocks on the same date would now be worth more than $2,000. Although one would expect returns on equities to be higher, because they are riskier than T-bills, the return differential of 7 percent a year is future returns cannot be predicted on the basis of existing information.
Thirty years ago, financial economists thought this most basic assumption of the efficient market hypothesis was true (Fame 1970). Now, everyone agrees that stock prices are at least partly predictable (see, for example, Fame 1991) on the basis of past returns, such measures of value as price-to-earnings or price-to-book ratios, company announcements of earnings, dividend changes, and share repurchases and seasoned equity offerings. What should one conclude from these and other empirical facts?
On one side of the coin is my own conclusion: In many important ways, real financial markets do not resemble the ones we would imagine if we only read finance textbooks. Mean variance portfolio theory versus Behavioral portfolio theory: Mean variance portfolio is a theory of finance that attempts to maximize portfolio expenditure for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets.
But in behavioral portfolio theory is the observation that investors view their portfolios not as a whole, as prescribed by mean-variance portfolio theory, but as distinct mental account layers in a pyramid of assets, where mental account layers are associated tit particular goals and where attitudes toward risk vary across layers. One mental account layer might be a “downside protection” layer, designed to protect investors from being poor.
Another might be an “upside potential” layer, designed to give investors a chance at being rich. Investors might behave as if they hate risk in the downside protection layer, while they behave as if they love risk in the upside potential layer. These are normal, familiar investors, investors who are animated by aspirations, not attitudes toward risk. Mean-variance portfolio theory and behavioral oratorio theory were combined recently as mental accounting portfolio theory by Dads, Margarita, Shied, and Eastman (2010).
The overall portfolio is the sum of the mental account sub-portfolios, and it, like the mental account sub-portfolios, lies on the mean-variance efficient frontier. Expected returns are a function of risk and risk alone: CAMP v/s BEAM The asset pricing model of standard finance is moving away from the capital asset pricing model (CAMP)-?in which beta is the only characteristic that determines expected stock returns-?toward a model that is similar to the BEAM.
The CAMP is expressed as an equation where: Expected return of a stock = f (market factor). Whereas BEAM is expressed as momentum, affect factor, social responsibility factor, status factor, and more). Emerging Study: Adaptive Markets Hypothesis Adaptive Markets Hypothesis can be viewed as a new version of the MME, derived from evolutionary principles. Prices reflect as much information as dictated by the combination of environmental conditions and the number and nature of market participants, each behaving in the common manner in the economy.
If multiple artisans are competing for rather scarce resources within a single market, that market is likely to be highly efficient, e. G. , the market for 10-Year US Treasury Notes. Whereas number of participants are competing for rather abundant resources in a given market, that market will be less efficient, e. G. , the market for oil paintings from the Italian Renaissance. Market efficiency cannot be evaluated in a vacuum, but is highly context-dependent and dynamic. Under the AMAH, behavioral biases abound.
The origins of such biases are heuristics that are adapted to non-financial contexts, ND their impact is determined by the size of the population with such biases versus the size of competing populations with more effective heuristics. Under the AMAH, investment strategies undergo cycles of probability and loss in response to changing business conditions, the number of competitors entering and exiting the industry, and the type and magnitude of profit opportunities available.
Implications: The first implication is that to the extent that a relation between risk and reward exists, it is unlikely to be stable over time. Such a relation is determined by the elated sizes and preferences of various populations in the market ecology, as well as institutional aspects such as the regulatory environment and tax laws. As these factors shift over time, any risk/reward relation is likely to be affected. A second implication is that contrary to the classical MME, arbitrage opportunities do exist from time to time in the AMAH.
As Grossman and Stilling (1980) observed, without such opportunities, there will be no incentive to gather information, and the price- discovery aspect of financial markets will collapse. From an evolutionary perspective, he existence of active liquid financial markets implies that profit opportunities must be present. As they are exploited, they disappear. A third implication is that investment strategies will also wax and wane, performing well in certain environments and performing poorly in other environments. Monetary to the classical MME in which arbitrage opportunities are competed away, eventually eliminating the profitability of the strategy designed to exploit the arbitrage, the AMAH implies that such strategies may decline for a time, and then return to profitability when environmental conditions become more conducive to such trades. Conclusion Both standard finance and behavioral finance provide valuable contributions and should be viewed as complementary rather than mutually exclusive.
While traditional finance assumes that investors always behave rationally, behavioral finance does not. Yet, both viewpoints can help improve understanding and the ability to make better such as during market bubbles and financial crises. Although traditional finance cannot provide definitive explanations for market bubbles, the field of behavioral finance offers some possible reasons, including overconfidence, anchoring bias (the unman tendency to “anchor” too closely on recent events when predicting future events), and herding (the tendency of investors to follow the crowd).
