An Introduction to Empirical Support of Efficient Market Hypothesis - - PowerPoint PPT Presentation

An Introduction to Behavioral Finance

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Nattawut Jenwittayaroje, Ph.D, CFA NIDA Business School National Institute of Development Administration Master of Arts program in Applied Finance

Topics

• Efficient Market Hypothesis
• Empirical Support of Efficient Market Hypothesis
• Empirical Challenges to the Efficient Market Hypothesis
• Theoretical Challenges of the Efficient Market Hypothesis
• What is Behavioral Finance?
• Applications of Behavioral Finance
• Why Behavioral Finance matters?

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Efficient Market Hypothesis (EMH)

• Fama (1970): an efficient financial market is one in which

security prices always fully reflect all available information.

• Weak-form: current prices reflect all stock market information (e.g., past

returns and past trading volume).

• Trading rules based on past stock market returns and trading volume are

useless.

• Semi-strong-form: current prices reflect all public information (e.g.,

earnings announcement, P/E and P/BV ratios).

• Trading rules based on public information are useless.
• Strong-form: current prices reflect all public and nonpublic information
• All trading rules are useless.

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Example of Stock Price Reaction to New Information in Efficient and Inefficient Markets

• Suppose IBM announces it has invented a microprocessor that will make its

computer 100 times faster than existing computers. The IBM share price should increase immediately to a new equilibrium price. Efficient market response

Delayed Response /Underreaction with Slow Adjustment Inefficient market response Overreaction and Reversion Inefficient market response

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EMH Theory: Theoretical Arguments

• Investors are rational and value securities rationally (i.e.,

value securities for their fundamental value).

• Some irrational investors are random and cancel each other
• ut without affecting prices.
• If investors are irrational in similar ways, rational arbitrageurs

will exploit the actions of the irrational investors and thus eliminate their influence on prices.

• Rational investors quickly respond to new information by

bidding up (down) prices following good (bad) news.

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EMH Theory: Theoretical Arguments

• Samuelson (1965) and Mandelbrot (1966) show that in

competitive markets with rational investors,

• Security values and prices follow random walks
• Returns (i.e., price changes) are thus unpredictable

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EMH Theory: Theoretical Arguments: Random Walk

• The reasoning behind the random walk concept as it applies to the

stock market is as follows;

• Securities markets are flooded with thousands of intelligent, well

paid and well educated professional investors and analysts.

• The more these professional investors, the faster the dissemination of

relevant information and thus the more efficient the market becomes.

• When information arises, news spreads very rapidly and tends to be

quickly reflected in security prices.

• If the flow of information is unimpeded, all of today’s news is

reflected in today’s stock prices.

• Then neither buyers nor sellers have an informational advantage. In

an efficient market, both buyers and sellers have the same information

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EMH Theory: Theoretical Arguments: Random Walk

• The reasoning behind the random walk concept as it applies to the

stock market is as follows (con’t)

• Tomorrow’s price changes reflect only tomorrow’s news. By definition,

new information arrives at the marketplace in an independent and random fashion (i.e., news is unpredictable and random).

• Price changes that result when news is released must also be

unpredictable and random.

• Random walk theory assets that stock price movements do not follow

any pattern or trend. Thus, past price action cannot be used to predict future price movements.

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EMH Theory: Implication

• Implication
• Investor should not make money by trading on stale information,

since stock prices accurately reflect everything that is known and is expected to occur.

• Buying and selling in an attempt to outperform the market will effectively

be a game of chance rather than skill.

• Stock prices instantaneously and fully change when new

information comes to the market, but new information cannot be anticipated and there is no way for investors to gain an edge (i.e.,

• utperform a benchmark).
• When new information about the value of a security hits the

market, stock price should neither under-react nor over-react.

• Price should not change in demand or supply of a security that is

not accompanied by news about its fundamental value.

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This response is consistent with the semi-strong-form EMH.

Empirical Support of EMH

Dow Jones reaction to the Federal Reserve’s (unexpectedly large rate cut of 3Jan2001) announcement

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Empirical Support of EMH

CARs to shareholders of targets of takeover attempts around the announcement date: Keown and Pinkerton (1981) Journal of Finance

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Are Stock Returns Predictable?

• Statistical Tests of Independence
• Autocorrelation Tests are the tests of the correlations between the current

return (rt) and the returns on day t-1, t-2,…..,t-n (rt-1 ,rt-2,….rt-n).

• Under EMH, insignificant correlations for all such combinations would be

expected.

• A positive correlation (between rt and rt-1) indicates price continuation

(i.e., momentum).

• A negative correlation (between rt and rt-1) indicates price reversal (i.e.,

contrarian).

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US Evidence International Evidence

Fama (1965)

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Empirical Challenges to the EMH

• Overreaction  Contrarian Strategy
• Underreaction  Momentum Strategy
• Use of stale/known public information
• Small firm effects
• Price-Earnings ratio and Market-to-Book ratio effects
• Index addition/deletion effects
• etc.
• De Bondt and Thaler (1985) argue that investors’ behaviour is

far from being rational. Investors tend to overreact to new information.

• In the long run, overreaction causes return reversal.
• Their results show that past winners become losers and past

losers become winners.

• The difference in returns cannot be explained by differences

in risk (i.e., beta) using CAPM.

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Overreaction: Contrarian Strategy

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Overreaction: Contrarian Strategy

Cumulative Average Residuals for Winner and Loser Portfolios Debondt and Thaler 1985 (Journal of Finance)

• Subsequently, Jegadeesh and Titman (Journal of Finance,

1993) find evidence of momentum in stock returns.

• They show that movements in individual stock prices over a

period of 3-12 months predict future price movements in the same direction.

• Recent winners keep winning, and recent losers keep losing
• ver the next 3-12 months.
• Following this evidence, arguments from the psychology

literature were increasingly employed to justify potential biases on the part of investors.

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Underreaction: Momentum Strategy

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Main Results:

• Returns of all zero-cost (i.e.,

buy minus sell) portfolios are positive and almost all are significant.

• Most successful zero-cost

strategy is to selects stocks based on their returns over the previous 12 months and then holds them for 3 months

• > 1.31% per month.

Underreaction: Momentum Strategy

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Chan et al (1991) Fundamentals and stock returns in Japan, Journal of Finance

Size, Price to Earnings, and Market to Book effects

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Size, Price to Earnings, and Market to Book effects

• Small stocks achieve much higher return than large stocks.
• High E/P outperforms low E/P
• Firms with large, positive B/M ratios earn a premium over firms

with low, positive B/M ratios.

• High cash-flow-yield firms earn substantially higher return than

low cash-flow-yield firms.

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Nattawut (2014) Index Effects: A Review and Comments, Chulalongkorn Business Review

Index effects – International evidence

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Counterargument from EMH

• Data snooping (data mining)
• The process of examining data affect the likelihood of finding empirical

results that are inconsistent with EMH.

• Authors in search of an interesting research paper are likely to focus

attention on ‘surprising’ results.

• Solution:
• Test on an independent sample: other countries, prior time periods,

(if sufficient time elapses after the discovery of an anomaly) subsequent data can also be used.

• Improper risk adjustment
• Survivorship bias (sample selection bias)

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Theoretical Challenges of EMH

• Are investors rational?
• Evidence also suggests investors are not rational in the way

suggested by neoclassical economic theory.

• There are psychological biases in people’s assessment of

probable outcomes.

• Investors’ psychological biases play an important role in the

mispricing

• Overconfidence, representativeness, anchoring, and so on
• Prospect theory

Theoretical Challenges of EMH

• If the trading strategies of irrational investors are random (i.e.,

unsystematic), then prices can remain close to fundamental value.

• However, there is considerable evidence to suggest that

deviations from the rational view are not random, and therefore trades by irrational investors have an effect on prices.

• Rational arbitrageurs, however, can eliminate the influence of

irrational investors on prices.

• A simple example of an arbitrage trade would be the simultaneous

purchase and sale of the “same” security at different prices

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Theoretical Challenges of EMH

• When arbitrage is needed to make market efficient, individual

stocks must have close substitutes (i.e., “same” security) for such arbitrage to work well.

• Consider a situation where a stock becomes overpriced relative to its

fundamental value.

• Rational investors (i.e., arbitrageurs) would sell (or short sell) this

stock and at the same time buy other securities which are essentially the same.

• The effect of the selling by arbitrageurs brings the price of the
• verpriced security back down to its fundamental value.
• A similar argument applies to an undervalued stock.
• The activities of arbitrageurs result in irrational traders losing out.
• The problem is that substitute securities are rarely perfect, and
• ften highly imperfect, making it impossible to remove all

fundamental risk.

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Theoretical Challenges of EMH

• Arbitrageurs face noise trader (i.e., irrational trader) risk,

where mispricing become worse before it disappears.

• Noise trader risk matters because it can force arbitrageurs to

liquidate their positions early, bringing them potentially steep losses.

• Implementation costs (e.g., short selling constraint) also make

arbitrage difficult.

• Therefore, it is argued that there are severe limitations to the

effectiveness of arbitrage  “Limits to Arbitrage”

• Thus, all of the theoretical foundations on which market

efficiency was based have been called into question.

• Behavioral finance has emerged as a major challenge to EMH.

What is Behavioral Finance?

• Behavioral finance is concerned with the way in which

psychological principles of decision making affect investor market behaviour.

• It explicitly takes account of the interaction between

behavioral cognitive psychology and financial markets.

• Unlike the traditional approach, it does not assume that

investors always behave in a rational way.

• Behavioral finance is expected to explains the evidence that

appears anomalous from the efficient markets perspective

• Behavioral finance models generate new predictions that are

consistent with the empirical evidence

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• “At the most general level, behavioral finance is the study of

human fallibility in competitive markets. It does not simply deal with an observation that some people are stupid, confused or biased”….

• “Behavioral finance goes beyond this uncontroversial observation

by placing the biased, the stupid, and the confused into competitive financial markets, in which at least some arbitrageurs are fully

• rational. It then examines what happens to prices and other

dimensions of market performance when the different types of investors trade with each other”…

• “Market efficiency only emerges as an extreme special case,

unlikely to hold under plausible circumstances.”

• Shleifer, Inefficient Markets, 2000, pages 23-24.

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What is Behavioral Finance?

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• The basic premise of behavioral finance is that conventional

financial theory ignores how real people make decisions and that people make a difference.

• Behavioral finance consists of two building blocks
• The anomalies can be interpreted as consistent with several

irrationalities/psychological biases that seem to characterize individuals making complicated decisions.

• Such irrationalities are systematic and affect prices, and it is

difficult for rational traders to undo the mispricing caused by irrational (i.e., noise) traders -> Limits to arbitrage

Behavioral Finance: Building Blocks

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Behavioral Finance: Building Blocks

• Psychological Biases: Types of various deviations from full

rationality.

• Overconfidence: Extensive evidence shows that people are
• verconfident in their judgements. For example, overconfidence

relates to the confidence intervals people assign to their estimates of

• quantities. Generally, the intervals are far too narrow.
• Representativeness: Kahneman and Tversky (1974): People judge

probabilities “by the degree to which A is representative of B, that is, by the degree to which A resembles or reflects the essential characteristics of B.”

• sample size neglect, “hot hand”, gambler’s fallacy, and base rate

neglect.

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Behavioral Finance: Building Blocks

• Psychological Biases: Types of various deviations from full

rationality.

• Anchoring: people “anchor” too much on the initial value.

People tend to make estimates by starting from an initial value and adjusting it to generate a final estimate  (People are initially anchored on their prior belief.) However, often the adjustment is insufficient.

• Prospect Theory  An alternative to Expected Utility Theory

(EUT)

• Risk aversion and risk-seeking behavior
• Frame-dependent – using Reference Point
• Loss Aversion

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Behavioral Finance: Building Blocks: Prospect Theory

• Value function in prospect theory replaces the utility function in

expected utility theory.

• Value is in terms of gains or losses, relative to a reference point

(usually the status quo).

• The value function exhibits diminishing sensitivity
• Concave for gains, and therefore risk averse
• Convex for losses, and therefore risk seeking
• The value function has a kink at the origin, indicating a greater

sensitivity to losses than to gains.

• So, the loss of x is more painful (has a bigger effect on utility

value) than a gain of x is pleasurable  a feature known as loss aversion.

• Loss aversion  people dislike losses, so the value function is

steeper for losses than for gains.

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Utility Value

Losses($)

Gains ($)

The Prospect Theory Utility Value Function

$x $x

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Behavioral Finance: Building Blocks

• Limit to Arbitrage
• Fundamental risk: imperfect substitute
• Noise trader risk:
• Price do not converge to fundamental values instantaneously
• Mispricing can worsen in the short run or for a longer time period.
• Implementation costs: commissions, bid-ask spreads, price

impact, legal constraints

• Short sales constraints
• Fee charged for borrowing a stock
• Legal constraints  many funds are not allowed to do short selling.

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Limits to Arbitrage: Motivating Evidence

• Froot and Dabora (JFE, 1999) cast doubt on the idea that arbitrage

works and the law of one price holds in financial markets.

• Royal

Dutch and Shell are separate legal entities and are independently incorporated.

• But they signed a charter in 1907 to merge their interests on a 60:40

basis (while remaining separate and distinct entities).

• Cash flows (adjusted for tax and control rights) are effectively split

in the proportion of 60 : 40.

• If stocks are valued on the basis of cash flows, and the market is

efficient  the law of one price holds  Market Value of Royal Dutch = 1.5 x Market Value of Shell.

Limits to Arbitrage: Motivating Evidence

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As the figure shows, the deviations from the theoretical parity are so massive, ranging from -30% to +10%. The deviation from the parity relationship ranges from +10% up to -42% (from 1980 to 1995) and it took more than 4 years for the mispricing to be corrected.

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Applications of Behavioral Finance

• Underreaction in stock markets:
• Put forward by Jegadeesh and Titman (1993, Journal of

Finance).

• Underreaction may result from anchoring (Barberis,

Shleifer, and Vishny 1998).

• Anchoring involves selecting an initial reference point and

slowly adjusting to the correct answer as we receive additional information.

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• Anchoring could lead to stocks being mispriced.
• Consider a company that suddenly reports significantly higher/lower

earnings.

• Investors may assume the change in earnings is only temporary (i.e.,

anchoring effect), and thus underreact to this news.

• They are anchored to their previous view of the company’s likely

profitability.

• As more information becomes available over time, investors adjust

their view accordingly.

• Initial underreaction and later correction cause positive serial

correlation in stock returns (predictability).

Applications of Behavioral Finance

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Price time t0

Underreaction

Level at news announcement

New equilibrium level Good news comes in

Applications of Behavioral Finance

Applications of Behavioral Finance

• Barber and Odean (2000) provide empirical evidence supporting

the view that overconfidence leads to excessive trading.

• Almost no difference in the gross performance of households that

trade frequently (with monthly turnover in excess of 22%) and those that trade infrequently.

• However, households that trade frequently earn a net annualized

mean return of 11.4%, and those that trade infrequently earn 18.5%.

• These results are consistent with models where trading emanates

from investor confidence.

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• Disposition effect - the empirical fact that investors tend to

sell winners too early and ride losers too long.

Applications of Behavioral Finance

Odean’s 1998 results relating to 10,000 households with accounts at a large discount brokerage firm, 1987-1993. PLR is significantly less than PGR.

PLR PGR

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Odean (1998) also examines excess returns for winners sold and losers held and finds the stocks investors hold onto do worse than the stocks they sell.

Applications of Behavioral Finance

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Applications of Behavioral Finance

• Suppose a stock was originally bought at $50.
• But now it sells for $55.
• Alternatively, the investor can wait another period, whereupon

the stock could go to $50 or $60 with equal probability….

• On the other hand, assume the stock is now trading at $45. so

the current loss is $5.

• Assume that if the investor wait another period, the stock

could go to $40 and $50 with equal probability……

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Why Behavioral Finance matters?

• Understanding behavioral finance helps us to avoid emotion-

driven speculation leading to losses, and thus devise an appropriate investment strategy.

• Recognizing our own behavioral biases is crucial to long-term

investing success.

• For example, these behavioral pitfalls cause most investors

to concentrate their portfolios too heavily in one asset class

• r stock, trade too frequently (Odean 1999, AER), and

underestimate the risks that are present in a particular strategy.

• One of the best ways to combat these biases is to simply be

aware of them.

References

• Barberis, N, Shleifer, A., and Vishny, R. (1998), “A model of investor sentiment”, Journal of

Financial Economics 49: 307-343.

• Chan, L., Y. Hamao, and J. Lakonishok, (1991), “Fundamentals and stock returns in Japan”,

Journal of Finance 46: 1739−1764.

• DeBondt, W.F.M., and R. Thaler (1985), “Does the stock market overreact?” Journal of Finance

40: 793−805.

• Fama, E (1970), “Efficient Capital Markets: A review of theory and empirical work,” Journal of

Finance 25: 383-417.

• Keown, A. and Pinkerton, J. (1981), “Merger announcements and insider trading activity: An

empirical investigation”, Journal of Finance 36: 855-869

• Jegadeesh, N., and S. Titman (1993), “Returns to buying winners and selling losers: implications

for stock market efficiency”, Journal of Finance 48:65−91.

• Jegadeesh, Narasimhan and Sheridan Titman, 2001, “Profitability of momentum strategies: An

evaluation of alternative explanations,” Journal of Finance .56, 699-720.

• Jenwittayaroje, Nattawut (2014). “Index Effects: A Review and Comments,” Chulalongkorn

Business Review 140, 1-15.

• Odean, T. (1998), “Are investors reluctant to realize their losses?”, Journal of Finance

53:1775−1798.

• Odean, T. (1999), “Do investors trade too much?”, American Economic Review 89:1279−1298
• Shleifer, A. (2000), Inefficient Markets, Oxford: New York: Oxford University Press.
• Wurgler, J., and K. Zhuravskaya (2002), “Does arbitrage flatten demand curves for stocks?”,

Journal of Business