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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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Selective Perception
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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Selective Perception
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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Selective Perception
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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Selective Perception
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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Selective Perception
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,