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Tuesday, April 22, 2014

Behavioral finance: The inefficiency of human financial decision making

People are slow to react and over optimistic when it comes to financial decision making according to behavioral finance. They also like to pay more in trading commissions and capital gains taxes in addition to insufficiently diversifying their assets. Whether or not that is actually true depends on who is being included in academic research and how accurately the numbers represent the population of investors. Nevertheless, that fact these kinds of financial behaviors exist at all does indicate that perfect market efficiency is a myth so long as humans are directly involved.

Behaviour finance from saurabh chauhan

Behavioral finance studies how people's decision making is influenced by psychology. Since everyone is different, no single financial heuristic applies to everyone, but as the presentation shows, stock market data makes identifiable patterns of financial decision making evident for companies of varying sizes. For example, if earnings results are higher than those forecasted, historical data shows the market overreacts by generating abnormal returns.

Some of the aforementioned data is restated and corroborated by another slide show presented by Alok Kumar at the Yale School of Management. Moreover, according to Kumar, "Prospect theory", as defined by Daniel Kahneman and Amos Tversky, states that a $1,000 loss is perceived more heavily than a $1,000 gain. This means investors are more likely to behave differently to capital gains and losses if they take the presumed perception spread in to account when making financial decisions.

Another factor that influences individual monetary decision making is financial data. For example, also according to the Kumar presentation, there are three kinds of market efficiency including weak, semi-strong and strong. The strongest market efficiency is said to be based on access to public and private financial information. Since not everyone has the same market or corporate data, market efficiency is partially influenced by how much data investors have access to. 

In addition to the impact of financial data on investment decisions is use of analytical tools and evaluative capacity. Since not every investor will have the same analytical approach to financial information, not all assessments will be accurate. In effect, this has the potential to slow down or limit market efficiency to the most informed and aware of participants.

Further elaboration about how behavioral finance affects stock market efficiency is discussed in an additional presentation published by the University of Berkeley Haas School of Business. This slide show is the most thorough of the three as it mentions and provides supporting information for numerous aspects of behavioral finance. For instance, the beginning of the slide show cites Nobel Laureate Robert Shiller in reference to the topic at hand. Specifically, Shiller is quoted as stating “The aggregate stock market in the United States in the last century has been driven primarily by psychology and fads, that it has shown massive excessive volatility.”

The Berkeley slides also discuss the predictability of irrational financial decisions and different kinds of systemic irrationality such as simplification of information and even "magical thinking". Further supporting studies are also cited including one that finds investors fixate on corporate earnings and make decisions based on that instead of also including important cash flow factors such as unpaid accounts receivable. The concept of behavioral finance is also explained in terms of how active managers or asset managers can use the theory to their advantage i.e. by knowing market psychology, making buy or sell decisions that capitalize on the behavioral trends is possible.

Every individual financial decision maker is likely to make use of  his or her own heuristics or models with which monetary actions are taken. Organizations such as investment banks, mutual funds and other money managers are also likely to make use of models such as credit data, economic indicators and corporate reports when implementing transactions. These latter models, such as the Capital Asset Pricing Model and Black-Scholes Model, are logically and deductively arrived at, but are also themselves subject to trader biases. Perhaps only mathematical algorithms used in high frequency trading are the only truly efficient traders.