BehFin Overhead

7/29/2019 BehFin Overhead

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Beh Finance 1

BEHAVIORAL FINANCE

Introduction

behavioral finance is an alternative to the EMH

this material taken mostly from the 2000 book by

Andrei Shleifer of Harvard:

Inefficient Markets: An Introduction To Behavioral

Finance

EMH has been the central tenet of finance for almost30 years

power of the EMH assumption is remarkable


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Beh Finance 2

EMH started in the 1960s

immediate success in theory and empirically

early empircal work gave overwhelming support to

EMH

EMH invented at Chicago and Chicago became a

world center of research in finance

Jensen (1978) no other proposition in economics

. . . has more solid empirical support


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Beh Finance 3

verdict is changing efficiency of arbitrage is much weaker than

expected

true arbitrage possibilities are rare

near arbitrage is riskier than expected

Markets can remain irrational longer than you

can remain solvent John Maynard Keyes

quoted by Roger Lowenstein in When Genius

Failed: The Rise and Fall of Long-Term Capital

Management


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Beh Finance 4

Defense of EMH

three lines of defense of the EMH:

investors are rational

trading of irrational investors is random and theirtrades cancel each other

even if a herd of irrational investors trade in

similar ways, rational arbitrageurs will eliminate

their influence on market price

each of these defenses is weaker that had been thought


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Beh Finance 5

rational investing = value a security by its

fundamental value

fundamental value = net present worth of all

future cash flows

rational investing prices are (geometric) random

walks

but prices being random walks (or nearly so) does

not imply rational investing

there is good evidence that irrational trading is

correlated

look at the Internet stock bubble


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initial tests of the semi-strong form of efficiency

supported that theory

event studies showed that the market did react

immediately to news and then stopping reactin so reaction to news, as EMH predictos

also no reaction to stale news, again as EMH

predicts

Scholes (1972) found little reaction to non news block sales had little effect on prices


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Beh Finance 7

Challenges to the EMH

it is difficult to maintain that all investors arerational.

many investors react to irrelevant information

Black calls them noise traders

investors act irrationally when they

fail to diversify

purchase actively and expensively managed mutual

funds

churn their portfolios

investors do not look at final levels of wealth when

assessing risky situations (prospect theory)


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Beh Finance 8

there is a serious loss aversion

people do not follow Bayes rule for evaluating new

information

too much attention is paid to recent history

overreaction is commonplace

these deviations from fully rational behavior are not

random

moreover, noise traders will follow each othersmistakes

thus, noise trading will be correlated across investors


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Beh Finance 9

managers of funds are themselves human and will

make these errors too

managers also have their own types of errors

buying portfolios excessively close to a benchmark

buying the same stocks as other fund managers (soas not to look bad)

window dressing adding stocks to the portfolio

that have been performing well recently

on average, pension and mutual fund managersunderperform passive investment strategies

these managers might be noise traders too


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Beh Finance 10

Can arbitrageurs save the day?

the last defense of the EMH depends on arbitrage

even if investor sentiment is correlated and noise

traders create incorrectly priced assets arbitrageurs are expected to take the other side of

these traders and drive prices back to fundamental

values

a fundamental assumption of behavioral finance is

that real-world arbitrage is risky and limited


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arbitrage depends on the existence of close

substitutes for assets whose prices have been driven

to incorrect levels by noise traders

many securities do not have true substitutes

often there are no risk-less hedges for arbitrageurs

mispricing can get even worse, as the managers of

LTCM learned this is called noise trader risk


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Beh Finance 12

What do the data say?

Shiller (1981), Do stock prices move too much to be

justified by subsequent changes in dividends:

market prices are too volatile more volatile than explained by a model where

prices are expected net present values

this work has been criticized by Merton who said

that Shiller did not correctly specify fundamentalvalue


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Beh Finance 13

De Bondt and Thaler (1985), Does the stock market

overreact?:

frequently cited and reprinted paper

work done at Cornell

compare extreme winners and losers

find strong evidence of overreaction for every year starting at 1933 they formed

portfolios of the best performing stocks over the

previous three years

winner portfolios they also formed portfolios of the worse performing

stocks

loser portfolios


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Beh Finance 14

then examined returns on these portfolios over the

next five years

losers consistently outperformed winners

difference is difficult to explain as due to differences

in risk, at least according to standard models such asCAPM

De Bondt and Thaler claim that investors overreact

extreme losers are too cheap

so they bounce back

the opposite is true of extreme winners


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Beh Finance 15

historically, small stocks have earned higher returns

than large stocks no evidence that the difference is due to higher risk

superior returns of small stocks have been

concentrated in January

small firm effect and January effect seem to havedisappeared over the last 15 years

market to book value is a measure of cheapness

high market to book value firms are growth stock they tend to underperform

also they tend to be riskier, especially in severe

down markets


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October 19, 1987 Dow Jones index dropped 22.6%

there was no apparent news that day

Cutler et al (1991): looked at 50 largest one-day

market changes

many came on days with no major news

announcements


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Roll (1988) tried to predict the share of return

variation that could be explained by

economic influences

returns on other stocks in the same industry public firm-specific news

Rolls findings:

R2 = .35 for monthly data

R2 = .2 for daily data


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Rolls study also shows that there are no close

substitutes for stocks

this lack of close substitutes limits arbitrage

stocks rise if the company is put on the S&P 500

index

this is reaction to non news America Online rose 18% when included on the

S&P

In summary, there is now considerable evidence

against the EMH This evidence was not found during early testing of

the EMH

Researchers needed to know what to look for


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Beh Finance 19

Market Volatility and Irrational Exuberance

Two books by Robert J. Shiller:

1989 Market Volatility

2000 Irrational Exuberance


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Beh Finance 20

What is a stock worth?

Let Vt be intrinsic value at time t. By definition

Vt =Ct+1

1 + k+

Ct+2

(1 + k)2+ =

i=1

Ct+i

(1 + k)i.

Ck is the cash flow at time k

k is the discount rate

A little bit of algebra shows that

Vt =T

i=1

Ct+i

(1 + k)i+

VT

(1 + k)T.

(exercise: check)


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Beh Finance 21

From previous page:

Vt =T

i=1

Ct+i(1 + k)i

+ VT(1 + k)T

. (1)

Shillers idea:

T = now

t < T is the past

Use past data to compute Vt as an approximation use PT in place of VT then all quantities in (1) are known at time T

compare Vt with Pt = actual stock price

EMH says that Pt is the optimal forecast at

time t of Vt.


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How do Vt

and Pt

compare?

Rough freehand sketch of Shillers plot.


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We see that Pt is more volatile than Vt.

Does this agree with the hypothesis that Pt is the

optimal forecast of Vt?

NO!


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Beh Finance 24

Best prediction

If X is the best predictor of X, then X and X X are uncorrelated

Var(X

) = Var(X) + E(X X)2

. Var(X) Var(X).(Exercise: prove these results if X is the best linearpredictor of X.)

An optimal predictor is less variable than the

quantity being predicted.


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Example: random walk

Suppose that Wt = 1 + + t, where 1, . . . are IID

N(, 2).

at time t, 0 t < T, the best predictor of WT is

WT = Wt + (T t) In other words, at time t

WT = {1 + 2 + + t} + {t+1 + + T}

is predicted by

WT = {1 + 2 + + t} + { + + }


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Beh Finance 26

From last page:

WT = {1 + 2 + + t} + {t+1 + + T}is predicted by

WT = {1 + 2 + + t} + { + + } Var(WT) = T

2

Var(WT) = Var(Wt) = t2. WT WT = (t+1 ) + + (T ) Var(WT WT) = (T t)

2

.Therefore, in this example,

Var(WT) = Var(WT) + Var(WT WT)


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As t T we cumulate more information about WT and

Var(WT) = Var(Wt) = t2

increases Var(WT WT) = (T t)2 decreases Var(WT) = T

2 stays the same (of course)

The main point, however, is simply that

an optimal forecast is less variable that what is being

forecasted.

stock prices are more variable that the present values

of future discounted dividends.Therefore, price cannot be optimal forecasts of the

present value of discounted future dividends.


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At least, this is Shillers argument.


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Beh Finance 29

Irrational Exuberance is a very interesting

discussion of market psychology, bubbles, naturally

occurring Ponzi schemes, and other possible

explanations of why the market seemed overpriced in

2000.

Shillers analysis suggests that the market may be

still overpriced in 2002, despite two years of declining

prices.

Shiller presents fascinating evidence that periodswhere the market is either over or under priced have

occurred, often several times, in many countries.

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