Efficiency vs. Behavioral Finance

8/10/2019 Efficiency vs. Behavioral Finance

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FINA 363 FALL 2014

Efficient Markets

Behavioral Finance


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Efficient Market Hypothesis (EMH)

Capital markets are huge computing devices

Inputs: information

Outputs: security prices

EMH Capital markets compute correct

prices on a timely basis

Why do we care?


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Random Walk and The EMH

Observable time series of stock priceslook like a Random Walk (Bachelier,

1900)Random Walk

DPt= a+ et

a = trendet= random noise


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AT&T Daily Chart


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Price Changes

Past price changes do not predict futureprice changes

Cov(et, et+1) = 0

Why?

Prices change only due to new information

New information by definition is notpredictable by past information


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Why Are Capital Markets Efficient

Markets are efficient when twoconditions are met:

Prices react quickly to new information Prices react correctly to new information

Financial markets Low barriers to entry, many participants

Most participants have similar preferences(care about risk and return)


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Why Are Capital Markets Efficient

Competition Quick price adjustments New information usually becomes available to

more than one investor

If an informed investor waits, somebody else willtrade on the information

Competition between investors assures that

prices quickly adjust to the information


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Types of Information

Historicaltrading info

All public non-trading info

private info

All public info All info


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Relationship between EMH Forms

Semi-strong form includes weak formas special case

Strong form includes semi-strong andweak form as special cases


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Security Analysis and MarketEfficiency

Technical Analysis Use historical trading (prices, volume, short

interest) information to predict future prices

Weak form efficiency is not consistent withsuccessful technical analysis

Fundamental Analysis Use publicly available economic and accounting

information to predict stock prices Semi strong form efficiency is not consistent with

successful fundamental analysis


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EMH and Portfolio Management

If markets are efficient, then small (pricetaker) investors cannot outperform themarket Compute Optimal Risky Portfolio using portfolio

optimization

Hold mix of Optimal Risky Portfolio and risk-freeasset

Minimize tax burdenWhat about large (non-price-taker)investors?


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EMH and the Finance Industry

Financial analyst recommendations

Technical analysis data

Actively managed mutual funds

Discretionary brokerage accounts


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Empirical Evidence on EMH

Quick price adjustments Event studies

Correct price adjustments Event studies

Return from trading strategies using historicaltrading info

Returns from trading strategies using publicly

available info Insider trading returns

Performance of professional money managers


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Event Study Methodology

Examine speed and accuracy of the marketsresponse to new information. Does themarket under-react or over-react to new

information?Detect abnormal returns around a narrowevent window Test if cumulative abnormal returns are different

than zero

Make an assumption about normal returns Use market model


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Event Study ChartM&A Example


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Return to Trading StrategiesAnomalies

Market bubbles and crashesSmall Firm EffectCalendar Effects January Effect October Effect Day of the week effects

Post-Earnings Announcement Drift /

MomentumLong-term Reversals

Value Line Enigma


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Speculative bubbles

Historical perspective The Tulip-Bulb Craze: Holland, 1634-1637

The South Sea Bubble: Early 1700 Florida Real Estate Bubble: 1920s

Wall street: 1928-1929

Internet companies: 1996-2000

Recent turmoil

Speculative bubbles as naturallyoccurring Ponzi schemes


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January Effect - Gultekin &Gultekin (1983)


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Mutual Fund and ProfessionalManager Performance

Overall mutual funds underperform on a risk-adjusted basis

Potential errors in the risk-adjustment?

Some evidence of persistent positive andnegative performance

Incentives for creating superstars

Incubators for young mutual funds


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Conclusion

The market for US large-cap liquid stocks isapproximately semi-strong form efficient

No reason to believe that other markets areIt is not easy to outperform the market on arisk-adjusted basis

Have to be better informed

Larger

Faster


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The Explosion of Challenger(Mahoney & Mulherin, 2002)

11:39 a.m., January 28, 1986The space shuttle

Challenger crashes

11:47 a.m.The news is announced on the Dow Jones

News Wire

Possible culprits

Rockwellshuttle maker

Lockheedground support

Martin Mariettashuttle fuel tanks

Morton Thiokolsolid fuel booster rocket


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The Reason for the Explosion

Special Commission ReportJune 1986 Reason for the crashlack of resiliency at low

temperature of the seals of the booster rocket

(extremely cold that day in Florida)

Morton Thiokol is the guilty party

NASA and MT aware of the problem a year

agoThis is NOT public information for most stock

market participants


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Stock Price Reaction

12 noon R = -6.12%, L = -5.05%, MM = -2.83%

MTtrading halt (sell order imbalance)

Around 1 p.m. R -4%, L -4%, MM -5%

MT -10%

End of day R = -2.48%, L = -2.14%, MM = -3.25%

MT = -11.86%


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Evidence for Strong form MarketEfficiency

Quick price adjustments Market needed less than an hour

Correct price adjustments? 200 M market value loss

Compare to 7 M settlement with astronauts families

10 M settlement with NASA

40 M foregone profits in repair work

150 M loss of NASA contract


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How Did the Market Figure It Out

Informed trading

Selling in MT (large sell order imbalance)

Buying in other threeNYSE Specialists observe abnormaltrading and adjust prices


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Behavioral Finance

Relaxes one of the most important setof assumptions in classic finance

theory: Investor Rationality


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Investor Rationality

Consistent and homogeneous beliefs Use all available information

No cognitive biases

No framing effects

Unbiased by emotions, herd effects

Consistent preferences Tangible and measurable objectives (maximize

wealth, minimize risk, etc.)

Time consistent preferences

No regret


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Real Investors

Triunal Brain

Cognitive biases

Time-inconsistent preferences

Influenced by hot states


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Triunal Brain

The human brain can be decomposed inthree parts

Neocortexreason Limbic systememotions

Reptilian brainactions

Most decisions are made by all threeparts of the brain


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Cognitive Biases

Biases of Judgment

Overconfidence/Optimism

Hindsight bias Quick Judgments

Anchoring

Representativeness

Confusing cause and effect


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Overconfidence

Misjudge probability of downside riskPlace large weight on personal abilityConsequences Frequent trading Large positions Undiversification

Odean and Barber (2001) Men trade more and have a smaller number of

larger positions Inferior performance compared to women


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Cognitive Biases

Heuristics for reducing complexity

Humans are cognitive misers

Simplifying the facts Mental accounting

Ignoring information/Selective perception


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Mental Accounts

Example

$150 concert ticket

Scenario 1: Ticket was purchased inadvance. On the way to the theater youlose it.

Scenario 2: Ticket was reserved and has tobe picked up at the theater. On the way tothe theater you lose $150 dollars.


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Selective Perception

Bruner&Postman Experiment
http://www.amazon.com/gp/reader/0471497843/ref=sib_dp_pt/102-0686640-4346544http://www.amazon.com/gp/reader/0471497843/ref=sib_dp_pt/102-0686640-4346544http://www.amazon.com/gp/reader/0471497843/ref=sib_dp_pt/102-0686640-4346544http://www.amazon.com/gp/reader/0471497843/ref=sib_dp_pt/102-0686640-4346544


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You can see 6 different cards.

Think on one.

Just think on it.

Are you thinking intently?I will find the card on your mind.


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Now, look straight into my eyes

and think about your card

I cant see the card you havechosen

but I know exactly the card

that is on your mind


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Look!Your card is gone!


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Do it again?

You can see 6 different cards.

Think on one.

Just think on it.

Are you thinking intently?I will find the card on your mind.


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Now, look straight into my eyes

and think about your card

I cant see the card you havechosen

but I know exactly the card

that is on your mind


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Look!Your card is gone!


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Cognitive Biases

Errors of preferences (Kahneman &Tversky)

Prospect TheoryValuing Changes, not States

Asymmetric Value Function

Consequences Disposition effect

Framing bias


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Prospect Theory

Choose one option

100% chance of $1,000

50% chance of $2,500

Most people choose the sure thing,

which can be explained by risk aversionBut


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Prospect Theory

Choose one option

100% chance of losing $1,000

50% chance of losing $2,500

Most people choose the latter option,

which is actually risk seeking behaviorPeople treat gains and losses differently


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Disposition Effect

Investors are reluctant to sell losers

Consequences Sell winners

Keep losers Documented both in individual accounts (Barber and Odean)

and in some mutual funds (Cici and Gibson)

Suboptimal strategy Tax losses

Does not take advantage of short-term momentum Wall St. Rule Cut your losses and let your winners ride


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Cognitive Biases

Evaluating Consequences of Decisions

Regrets of commission and omission

Regret and risk-taking

R t f C i i


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Regret of Commission orOmission

State your biggest mistake in pastinvestments

Is it because you did something? Or because you did not do something?


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Time-Inconsistent Preferences

Large weight placed on current benefitsor costs compared to all future ones

Consequences Correct decisions about future actions,

incorrect decisions about the currentperiod

Procrastination

Addiction


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Procrastination

Nave investors

Decision to sell now or postpone for tomorrow

Ignore the fact that tomorrow they will face thesame decision

Result postpone infinitely

More sophisticated investors

Recognize the procrastination effect Usually behave almost optimally


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Hot States and Projection Bias

Current emotional state affects forecasts ofbehavior in the future

Food shopping when hungry

Use of anesthesia when in pain

Bullish after a current trading profit

Projection bias

Probability of being in a hot state in the future Duration of hot state


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Behavioral Trading

Professional investors are also subjectto biases like individuals

Biases may lead to suboptimalperformance

Disposition bias

Paralysis Explosive risk taking

Currency trader story (Goldberg


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Currency trader story (Goldberg& von Nitzsch)

Stage 1. Lucky Success Several days of large profits Become complacent Viewed as expert by peers Start ignoring/filtering information Take large exposure based on overconfidence

Stage 2. Spiraling Loss Position starts losing money Reactionmarket is temporarily crazy, confirmed by peers Filter all negative information

A series of hope states Doubling down (more on strategy) Exposure becomes huge Position is finally liquidated at a large loss


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Behavioral Trading Controls

Most investment companies managethe behavioral biases of traders

Disposition bias/Sellers Remorse Maximum Drawdown Rules

Procrastination/Paralysis Team decision making

Short decision times

Daily balancing of inventory

Investor Behavior and Market


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Investor Behavior and MarketEfficiency

Many of the behavioral biases do not affect marketefficiency For example, overconfidence leads to more trading and

collection of information and may actually improve efficiency

Others, may cancel out or be alleviated by a small numberof unbiased traders

Still, some of the biases may lead to systematic andpredictable mispricing or inflated volatility in themarket

The aggregate outcome of such biases is measuredby Sentiment Indicators


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Market Sentiment

Sentiment Indicators

Technical

Consumer Sentiment Market Participant Sentiment


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Technical Indicators

Odd-lot trading

Put/Call Ratio

Amount of Put options Compared to Calloptions

Short interest

VIX/VXNAverage implied volatility of a set of CBOE-

traded options


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Consumer Sentiment Indicators

Consumer Sentiment Index (CSI) Since 1952

University of Michigan

Phone survey of 500-800 households Preliminary results, the second Friday of each

month

Consuming Confidence Index (CCI) Since 1967 5000 households

Last Thursday of the month


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Market Participants Sentiment

Investor Intelligence Indicator Based on text from 140 business newspapers Weekly since 1950

Market Vane Daily sentiment Polls of brokerage houses, tip hotlines Used by commodity traders

American Association of Individual Investors(AAII) Weekly sentiment generated by electronic voting

by its members

Evidence on the Value of


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Evidence on the Value ofSentiment Indicators

Some predictive power

CSI predicts the future equity premium

Charoenrook (2002) Fisher and Statman (2002)

Professional sentiment

Semi-professional sentiment

Individuals

A mix of the three predicts SP500


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References

Dorsey, W., 2004, Behavioral Trading,Thomson/Texere

Goldberg, J., and R. von Nitzsch, 2001, Behavioral

Finance, Wiley & SonsKahneman, D., and M. Riepe, 1998, Aspects ofInvestor Psychology, JPM, Summer, 52-65

Kiev, A., 2002, Trading in the Zone, Wiley & Sons

Mahoney, P., and H. Mulherin, 2002, The Stock PriceReaction to the Challenger Crash: InformationDisclosure in an Efficient Market, Journal ofCorporate Finance,

Efficiency vs. Behavioral Finance

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