Behavioral Finance

Lukas Setia Atmaja

Traditional assumption:

• Rational investors (expected utility theory and arbitrage assumptions – Von Neumann & Morgenstern) and EMH (Fama).

Behavioral Finance:

• Cognitive psychology (how people think) and Limits to arbitrage (when market will be inefficient)

• Investors are not fully rational (due to preferences or mistaken beliefs). E.g., gain $2 = loss $ 1

• In some circumstances, financial markets are informationally inefficient (due to psychological bias)

Daniel Kahneman (Hebrew: כהנמן (דניאל(born 5 March 1934) is an Israeli psychologist and Nobel laureate, notable for his work on behavioral finance and hedonic psychology.

With Amos Tversky and others, Kahneman established a cognitive basis for common human errors using heuristics and biases (Kahneman & Tversky, 1973, Kahneman, Slovic & Tversky, 1982), and developed Prospect theory (Kahneman & Tversky, 1979).

He was awarded the 2002 Nobel Memorial Prize in Economics for his work in Prospect theory. Currently, he is professor emeritus of psychology and public affairs at Princeton University's Woodrow Wilson School.Source: Wikipedia

Prospect theory was developed by Daniel Kahneman, professor at Princeton University's Department of Psychology, and Amos Tversky in 1979 as a psychologically realistic alternative to expected utility theory. It allows one to describe how people make choices in situations where they have to decide between alternatives that involve risk, e.g. in financial decisions. Starting from empirical evidence, the theory describes how individuals evaluate potential losses and gains. In the original formulation the term prospect referred to a lottery.

Source: Wikipedia

In prospect theory, loss aversion refers to people's tendency to strongly prefer avoiding losses to acquiring gains. Some studies suggest that losses are twice as powerful, psychologically, as gains. Loss aversion was first convincingly demonstrated by Amos Tversky and Daniel Kahneman.

This leads to risk aversion when people evaluate a possible gain; since people prefer avoiding losses to making gains. This explains the curvilinear shape of the prospect theory utility graph in the positive domain. Conversely people strongly prefer risks that might possibly mitigate a loss (called risk seeking behavior).

Source: Wikipedia

Cognitive Biases (How People Behave)

• Heuristics: Rules of thumb, make decision-making easier.

E.g., 1/N rule in investing. Can lead to biases, especially when things change.

• Over-confidence: Over estimate their abilities. E.g., active investment management rather than passive, men (especially single-men) trade far more actively than women, there is negative correlation b/w trading activities and return. “Trading [overconfidence] is dangerous for your wealth”

• Confirmation bias: “After the love has gone, what used to be right is wrong”. People are looking for confirmation for what their believe.

• Status Quo bias: people tend not to change an established behavior unless the incentive to change is compelling.

• Anchoring bias: the common human tendency to rely too heavily, or "anchor," on one trait or piece of information when making decisions.

• Hindsight bias: “I told you so”. The inclination to see events that have occurred as more predictable than they in fact were before they took place. “Easier to predict past than future”.

• Mental accounting: people separate decisions that should, in principle, be combined. E.g., eat fish rather than lobster at home, but order expensive lobster rather than fish in restaurant (if the other way around, they could save money), have one risk investment account and another conservative account, investors are more willing to take risk with their “winning account”.

• Framing: How a concept is presented to individual matters. E.g., survival probability of 60% sound better than mortality rates of 40%. Different framing leads to different attitude.

• Representativeness (extrapolation): people underweight long-term averages, tend to put too much weight on recent experience. E.g., when IDX return in 2007 was 55%, many people begin to believe that high equity returns are “normal”.

• Regret avoidance: people who make decisions that turn out badly have more regret (blame themselves more) when that decision was more unconventional. E.g., loss from buying blue-chip stocks is not as painful as the same losses on un-known start-up company.

• Conservatism: when things change, people tend to be slow to pick up on the changes.

• Disposition effect: people avoid realizing paper losses and seek to realize paper gains. E.g., A buys stock at $30 that then drops to $22 before rising to $28. Most people do not want to sell until the stock gets to above $30. During a bull market, trading volume tends to grow. If market is bearish, trading volume falls.

Limits to Arbitrage

• Cognitive or behavioral biases will not matter if rational arbitrageurs could fully exploit the mistakes of behavioral investors.

• But, behavioral finance argues that in practice, several factors limit the ability to profit from mispricing.

• Fundamental risk: Example, IBM share is undervalued. But market price can even get worse. It takes a longer time to move market price into intrinsic value. The investors may be mutual fund manager who could lose clients if ST performance is poor. See also LTCM case.

• Implementation costs: Exploiting overpricing can be difficult. Short-selling a stocks entails costs. Short-sellers may have to return the borrowed stocks on little notice.

• Model risk: Is your model (and your intrinsic value) right?

Case Study of the Limits to Arbitrage

Long Term Capital Management (LTCM) is a giant hedge funds founded in 1994.

John Meriwether (the former vice-chairman and head of bond trading at Salomon Brothers). Board of directors members included Myron Scholes and Robert Merton (1997 Nobel Prize in Economics).

Initially enormously successful with annualized returns of over 40% in its first years, in 1998 it lost $4.6 billion in less than four months and became a prominent example of the risk potential in the hedge fund industry. The fund folded in early 2000.

• Violation of the law of one price.

• In 1907, Royal Dutch Petroleum and Shell merged their operations into one firm. Both, which continued to trade separately, agreed to split all profits from the joint company on a 60/40 basis (Royal receive 60% of cash flows, Shell 40%). So, Royal stock price should be 60/40 = 1.5 x Shell price.

• See Figure 1 (1980 – 2001).

• In 1998, LTCM shorted the expensive stock and bought the cheap one. They lost money when prices diverged further from their theoretical values during the third quarter of 1998. To meet liquidity needs, LTCM and other hedge funds were forced to sell out their positions, this made markets more inefficient (cheap stocks become cheaper due to more supply).

• Irony: they were right in the long-run!!!

• High frequency events (which occur often) : support market efficiency

• Low frequency events (which occur only infrequently): may take a long time to recover, does not support market efficiency.

• Bubbles:

• The undervaluation of world-wide stock market from 1974 – 1982

• The Japanese stock price and land price bubble of 1980s

• The Taiwanese stock price bubble that peaked in February 1990

• The October 1987 stock market crash• The technology, media, and telecom (TMT)

bubble of 1999-2000