mba8 480 - behavioral finance topics

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Behavioral Finance Concepts* Professor Mike Pagano [email protected] * - adapted from slides developed by Prof. B. Scheick, Villanova U.

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Page 1: MBA8 480 - Behavioral Finance Topics

Behavioral Finance Concepts*

Professor Mike [email protected]* - adapted from slides developed by Prof. B. Scheick, Villanova U.

Page 2: MBA8 480 - Behavioral Finance Topics

Where we are headed . . .

Efficient Market Hypothesis: Review

A Simple Example of Irrationality

Building Blocks of Behavioral Finance

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Efficient Market Hypothesis (EMH)

Efficient Market Hypothesis: 3 Forms

Weak Form: Past Prices & Volume is reflected in current prices

Semi-Strong: All publicly available information is reflected in current prices

Strong: All publicly information (public and private) is reflected in current prices

Page 4: MBA8 480 - Behavioral Finance Topics

Efficient Market Hypothesis

Efficient Markets Hypothesis: Evidence– Stock prices fully and accurately reflect

publicly available information (Semi-strong EMH)

• Price = “fundamental value” of the firm– Investors (or at least markets) are “rational”

What do we mean by “rational” decision making?– Investors process information correctly and

make choices to maximize their utility function

Page 5: MBA8 480 - Behavioral Finance Topics

Suppose today is Valentine’s Day and I have the following choice to make:

What should I choose?

A Simple Example of Irrationality

Choice ABuy my wife a dozen Roses for $40

Choice BGive my wife $40

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Efficient Market Hypothesis

Some Empirical Challenges to EMH:– Factors such as firm Size and Book-to-Market

Ratio can help predict stock returns– Short-term trends in stock returns (Momentum)

can persist for extended periods (6-12 months)– Stock prices may over-(under-) react to new

information, therefore leading to long-run price reversals

Are there behavioral explanations for this?

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Efficient Market HypothesisSome Anecdotal Challenges to EMH:1999-2000 DotCom Bubble 2003-2007

Housing Bubble

Page 8: MBA8 480 - Behavioral Finance Topics

Building Blocks of Behavioral Finance

Behavioral Finance: Defined– Study of the manner in which investor

psychology (e.g., cognitive biases, emotions, etc.) influences the behavior of financial market participants and its subsequent effect on asset prices, investment decisions, and financial market conditions

Two Building Blocks:– Psychology– Limits to Arbitrage

Page 9: MBA8 480 - Behavioral Finance Topics

Building Blocks: Psychology

Daniel Kahneman and Amos Tversky (Psychologists)

Kahneman and Tversky (1979)– Considered seminal paper in behavioral

economics– Kahneman received 2002 Nobel Prize in

Economics for his work on Prospect Theory• Modeling real life choices rather than optimal decision

making• Models investment decisions based on gains and losses

rather than just final outcome (e.g., Loss Aversion)

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Building Blocks: Psychology

Behavioral Exercise (Case 1): – Suppose that you currently have $300 in

your investment portfolio and are given a choice between the following two scenarios:

• A) A sure (100% chance) gain of $100• B) A 50% chance to gain $200 and a 50%

chance to gain $0

– Which choice would you prefer?

Page 11: MBA8 480 - Behavioral Finance Topics

Building Blocks: Psychology

Behavioral Exercise (Case 2): – Suppose that you currently have $500 in

your investment portfolio and are given a choice between the following two scenarios:

• A) A sure (100% chance) loss of $100• B) A 50% chance to lose $200 and a 50%

chance to lose $0

– Which choice would you prefer?

Page 12: MBA8 480 - Behavioral Finance Topics

Building Blocks: Psychology

This is a classic example of “Framing”:– The way a problem is posed to a decision maker

impacts their decision• May result in a reversal of choice

Actual Results:– Case 1: 72% chose option 1, 28% chose option 2

• Problem framed as gain: decision maker is risk averse– Case 2: 36% chose option 1, 64% chose option 2

• Problem framed as loss: decision maker is risk seeking

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Building Blocks: Psychology

Kahneman and Tversky (1979) – Value Function

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Building Blocks: Psychology

Prospect Theory can be used to explain:– Equity Premium Puzzle

• Return on equity stocks versus government bonds has historically been very high

– Volatility Puzzle• Prices move around more than movements in

firm earnings would dictate– Predictability Puzzle

• Stock returns are forecastable by factors such as price to earnings ratio and dividend yield

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Building Blocks: Psychology

Since time and cognitive resources are limited, investors make decisions based on simple “heuristics”– Mental shortcuts or rules of thumb that typically

focus on a single piece of a complex problem

Cognitive psychologists have documented patterns of individual and group behavior that exhibit predictable errors (cognitive biases)– Framing, Loss Aversion, & Mental Accounting– Representativeness / Availability– Anchoring– Overconfidence & Optimism– Herding

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Building Blocks: PsychologyRepresentativeness

– People make judgments based on patterns in random sequences

• E.g., Think of a roulette wheel that has landed on Black for the past 10 turns. Next outcome must be red—right?

Anchoring– When forming estimates, people may be influenced

by known (often arbitrary) information that serves as a reference point (or anchor)

• E.g., A car dealer sets the sticker price of a used car and expects buyer to base bid around that reference point

– Any price less than this appears to be a deal!

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Building Blocks: PsychologyOverconfidence / Optimism

– People tend to display unrealistically optimistic views of their own abilities

• E.g., Over 90% of people surveyed think they are above average drivers, have above average senses of humor, and have above average ability to get along with people

Herding– People make decisions based on a specific piece

of information or “follow the crowd,” and, in the process, ignore other important information

• e.g., Tulip mania of 1600’s in Holland

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Building Blocks: Limits to ArbitrageIrrational Investors and EMH:

– The presence of “irrational” investors does not necessarily disprove EMH (if arbitrage can occur)

The Role of Arbitrage– Pure Arbitrage: Defined

• An investment strategy that offers riskless profit at no cost

– Implication for EMH:• If irrational investors cause prices to deviate from

fundamental value in the short-run, arbitrageurs should act quickly to correct any mispricing

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Building Blocks: Limits to Arbitrage

Limits to Arbitrage: Example– Royal Dutch / Shell Transport Twin

Shares• Royal Dutch and Shell Transport merged their

interests on a 60:40 basis while remaining separate entities

• In an efficient market, the market value of

Royal Dutch equity shares should always be 1.5 times greater than the market value of Shell equity

– Any deviations from this relation should be quickly arbitraged away in the absence of arbitrage constraints

Page 20: MBA8 480 - Behavioral Finance Topics

Building Blocks: Limits to Arbitrage• Price Ratio of Royal Dutch / Shell Twin Shares

• Evidence of persistent deviations from fundamental value

Page 21: MBA8 480 - Behavioral Finance Topics

Building Blocks: Limits to Arbitrage

Limits to Arbitrage:– “Noise Trader” Risk (De Long, Shleifer,

Summers and Waldmann 1990)• If noise traders continue to trade in the direction of

mispricing, this creates additional risk for the arbitrageur

– Short-selling constraints / limitations• Financing constraints may arise for arbitrageur

(Shleifer and Vishny, 1997)• Certain groups of investors may be prohibited from

shorting a stock (e.g., many pension fund and mutual fund managers)

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The bottom line on behavioral issues . . .

Growing evidence of behavioral biases and investment anomalies (size, value, momentum, earnings, etc.)

Limits to arbitrage and/or “hidden” / latent risk factors must be present to allow these anomalies to persist

Hard to exploit these anomalies given:– Decreasing transaction costs / lower “frictions”– Easier access to information– Growing awareness / knowledge of investors to these anomalies– Strong incentives to “beat the market” by exploiting (and thus

arbitraging away) these anomalies

Andrew Lo (MIT professor) suggests markets are “adaptive” and thus are “evolving” to greater levels of efficiency (moving towards EMH).