chapter 4 behavioural finance[1]

7/27/2019 Chapter 4 Behavioural Finance[1]

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Chapter 4

Behavioural Finance


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Behavioral psychology

Behavioral psychology tries to bring

answers to certain anomalies observed in

financial markets/investor behaviours and

which cannot be explained by using

financial theory.

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Behavioural finance

Behavioural finance integrates aspects of

social science; mainly psychology and

sociology to explain the behaviour of

investors and the evolution of financial

markets.

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Explain why individuals do not always take

decisions that maximize their expected

utility OR why the evolution of stock prices

cannot be explained by EMH

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Behavioural finance


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ec on : xpec e yTheory and prospect theory

Expected utility theory assumes that

investors always act in a rational manner,

by respecting the axioms of cardinal utility

(comparability, transitivity, independence,measurability, and ranking) and by

maximising expected utility.

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To verify whether the maximisation of

expected utility criteria is always applied

when individuals take decisions insituations of uncertainty, experiments

have been carried out. These experiments

consist in asking a sample of individuals tochoose between several lotteries. Through

the experiments, it was proved that

individuals do not always apply themaximisation of expected utility criteria.

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Choose between L1 and L2

L1 L2

3000

0

1

00

40000.8

0.2


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Results of the experiment show that:

80% of the individuals choose L1; i.e. L1

> L2

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L3 L4

3000

0

0.25

0.750

40000.2

0.8


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Experimental results

Choose between L1 & L2

80% of the individuals participating to

experiment choose L1; i.e. L1 > L2

Choose between L3 & L4

65% of the individuals participating toexperiment choose L4; i.e. L4 > L3


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L3 = 0.25L1 + 0.75L5L

4

= 0.25L2

+ 0.75L5

With L5 = (0,1)

According axiom independence:If L1 > L2 then L3 > L4

From experiments: L4 > L3


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Conclusion

Individuals do not alwaysbehave in a rational manner.

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How to choose between lotteries: L1 & L2 OR L3

& L4 ?? Use expected utility theory

E.g. assume utility function: U (X) = X

Concave

EU (L1) = 1*3000 + 0 = 54.7723

EU (L2) = 0.8*4000 + 0 = 50.5964

EU (L3) = 0.25*3000 + 0 = 13.6931

EU (L4) = 0.2*4000 + 0 = 12.6491

L1 > L2

L3 > L4


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E.g. assume utility function: U (X) = X2

Convex

U (L1) = 1*30002 + 0 = 9 000 000

U (L2) = 0.8*40002 + 0 = 12 800 000

U (L3) = 0.25*30002 + 0 = 2 250 000

U (L4) = 0.2*40002 + 0 = 3 200 000

Maximisation of expected utility criteria not

respected

L4 > L3

L2 > L1


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Attitude of the majority of individuals

participating to the experiment:

- L1 > L2

Same decision as experiment is obtained from

utility function U (X) = X (concave);

therefore individuals are averse towards risk.

- L4 > L3

Same decision as experiment is obtained from

utility function U (X) = X2 (convex); therefore

individuals are attracted towards risk.


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According to expected utility theory, an individual

will have only one utility function, either:- Concave or Convex or Linear

However from the experiment, it is demonstratedthat depending upon the lotteries, the same

individual/s can adopt different attitudes towards

risk.Therefore the same individual/s will have

different utility functions.


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High probability associated with gains and

low probability to losses:

averse towards risk

Low probability associated with gains and

high probability associated to losses:Attracted towards risk

Shows that individuals are less willing togamble will gains (averse to risk) than with

losses (attracted to risk).


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Prospect theory

Because of contradictions in the expected

utility theory, other methods were

developed to enable individuals take

decisions in situations of uncertainty.Among these methods, one of the most

popular is prospect theory, which was

developed by Kahneman and Tversky in1979.

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It is a theory developed by psychologists,

which is being applied in finance. In

expected utility theory, there is only one

utility function to evaluate the utility of alloutcomes (gains/losses).

In prospect theory, there are two utility

functions, one function for gains andanother for losses.

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Prospect theory


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Utility functions under prospect

theory The utility function for gains will be

concave and the utility function for losses

will be convex. This shows that individuals

are averse towards risk when gains areconsidered and attracted towards risk

when losses are considered.

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or osses n v ua s are


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or osses n v ua s areattracted towards risk.

E.g. two individuals must choose from a setof risky investments/lotteries. One

individual has just undergone a loss and

the other a gain; the individual who hasmade the loss would normally be more risk

averse as compared to the other individual

who has made a gain. The risk averse

individual could allocate more importanceto the losses as compared to the gains

when analysing the investments21


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E (L) = 900000

CE = 850000 (accepts)

Risk averse

1000000

0

0.9

0.1

E (L) = -900000

CE = -850000 (refuses)

Risk attracted

-1000000

0

0.9

0.1


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Prospect Theory (Kahneman and Tversky)

Initial wealth/amount paid to participate to lottery

= Rs1000 (reference point)

Lottery = (500, 1500; 0.5, 0.5)

Therefore Rs500 would be a loss and Rs1500

would be a gain.


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Two utility functions:

- One utility function for gains [U+(xi)];

Defined as a concave utility function;

showing aversion towards risk.

- Another utility function for losses [U (xi)]Defined as a convex utility function,

attracted towards risk.

Probabilities converted to weights: w(p1), w(p2)

...


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Evaluating lottery with expected utility theory:

E U(L) = p1U(x1) + p2U(x2) + p3U(x3) + ...

Lottery = (500, 1500; 0.5, 0.5)

E U(L) = 0.5U(500) + 0.5U(1500)

Evaluating lottery with prospect theory:

V (L) =

V (L) = w(0.5) U (500) + w(0.5) U+ (1500)

)()()()(11

i

m

niii

n

ii xUpwxUpw


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By applying prospect theory it can be proved

that for the sets of lotteries in the example:

V (L1) > V (L2) L1 > L2 and

V (L4) > V (L3) L4 > L3

Therefore prospect theory is able to explain the

choice of investors under uncertainty.


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Section 2: Efficient Market

Hypothesis and anomalies


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According to Market efficiency, stock prices

should move in a random manner over

time; as all information available is being

integrated in stock prices at all instants.

Information relative to past events or

anticipated future events (e.g. in the past

company had good financial results and itis anticipated that the future performance

will be good: increase in stock price) and

actual situation within the company oreconomy are integrated in the stocks

prices

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There are also unanticipated future events,

which are integrated in stock prices andmake them move in a random manner.

Market efficiency also implies that stocks

are fairly priced (stock value = market

price of stock) and no investor can make

gains in a consistent manner.

To test whether market are really efficient,

3 forms of market efficiency have been

defined29


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Weak form

Weak form: If past stock prices could beused to predict future stock prices, then

there should be a pattern in the evolution of

the stock prices.

Serial correlation of stock/security prices:

Studies (Fama 1965) have shown that

there is a very small positive correlation

(approaching zero) between daily stockprices. That is the stock price at time (t+1)

is not related to the stock price prevailing at

time t. 30


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Semi-strong form

Semi-strong form: public information, (e.g.company publishes earnings figures or

dividend payments) should be integrated

in the stock price at the moment it isreleased.

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Semi-strong form

Event study: researches have taken

similar events (e.g. merger

announcements; dividend payments)

and studied how the stock prices of thecompanies had been affected by the

events. If public information is instantly

reflected in stock prices, then around theannouncement date of the information an

impact should be noted on stock price.

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Strong-form

Strong-form: public and private information

(internal information which is possessed by

those who manage the company) must be

reflected in stock prices.

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Efficacy of professional investors (e.g.

mutual funds who are in possession of

internal information on the company):

researches have shown that they do notgenerate superior returns to market

indices. As private information is already

integrated in stock and cannot be used byprofessional investors to make gains.

(Consistent manner)

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Strong-form


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Over the years, many researches have

studied the efficiency of stock markets

and through the tests performed (asdetailed above); they have been able to

prove in many cases that markets are

really efficient. However there exist a fewevents in financial markets which cannot

be interpreted by using the efficient

market hypothesis.

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Crashes and Anomalies

ras an e o com


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ras an e o .comBubble

On 19th October 1987, the Dow JonesIndustrial Average (DJIA index) fell by %in one day.

Other indexes in the world also had sharp

decreases.

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Why sharp fall in the indexes?

EMH: an unfavorable information beingintegrated in the stock prices would cause

decrease in stock price and index.

However on the 19th of October, there was

no few fundamental information to justify the

sharp falls in the indexes.

EMH not able to justify the market crash.


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Dot.com Bubble

Between 1995 and 2000, the NASDAQ

Composite Index rose by 580%.

In November 2001 the index fell by 64%.

What caused this sharp increase???Behavioural finance:

- It is the optimism of individuals on the future

of the stocks that caused the index to rise.

- As the individuals generated profits, their

optimism was further increased.


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The sharp rises and subsequent sharp

decrease in indexes could not be

explained by efficient market hypothesis.The economic and financial conditions

prevailing when the crashes occurred (no

consequent rise in gross domestic

product or corporate profits) could not be

used to explain the evolution of the stock

prices within the indexes.

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NASDAQ is composed of technology

stocks. It has been put forward as

argument that it is the enthusiasm of theinvestors about the future of the

technology stocks, which could have

caused the relatively high increases in the

index. As potential investors observed

other investors who had made profits from

technology stocks, they were motivated

into investing in these stocks, thus drivingtheir prices up.

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Anomalies

According to EHM stock prices follow a

random walk; therefore it should be

impossible to predict changes in stock

prices based on publicly availableinformation and on past price behaviour.

According to market efficiency (weak

form), there should be no pattern in theevolution of stock prices. However in

reality some patterns have been noted.42


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January stock returns higher than in any

other month.

Returns

Monday effect: stock prices tended to go

down on Mondays.

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Anomalies

J ff t


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January effect

Studies carried out in several stockmarkets have shown that for no particular

reason, the return offered by stocks during

the month of January is superior as

compared to the other months. For

example a study carried out on the NYSE

over several years has shown that the

average return for January is equal to3.5% and the average return for the other

months is equal to 0.5%.44

J ff t


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Investors who are aware of this fact would

be willing to buy and hold stocks e.g. in

December, so as to benefit from the high

returns in January.

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January effect

M d Eff t


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Monday Effect

As stock prices move in a random manner,that is there is an equal probability of

increase and decrease in stock price on

any particular day of the week. The

expected return (obtained from the change

in stock prices; e.g. stock bought today (at

S0), its return over period t would be [(st -

S0)/ S0]) on a given stock should be thesame for Monday as it is for the other days

of the week.46

M d Eff t


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Studies have shown that the average

return on Monday was much lower than

the average return on other days. A study

done on the NYSE of a certain number ofyears have shown that on Mondays the

average return was equal to -0.2% and on

the other days the average return waspositive (e.g. Tuesday = 0.02%;

Wednesday = 0;1%...).

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Monday Effect

Holida effect


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Holiday effect

A study found that average stock returns

on trading days before public holidays are

abnormally high (9/14 times higher than

daily average return).

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ew exp ana ons rom


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ew exp ana ons romBehavioural finance

Investors are not identical and would notrespond in the same manner to the same

set of information, unlike what is assumed

by efficient market hypothesis. Several

theories have been developed, mainly

based upon human psychology, to explain

anomalies observed in financial theory.

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Examples of a few theories:


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Examples of a few theories:

Individuals usually give more weight to

recent events and give less importance toother information, when taking investment

decisions.

Biased expectations: people tend to be

overconfident in their predictions of future

stock prices.

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People also tend to hold stocks that are

generating losses for a longer time,

expecting that the stock prices may

improve. They also have a tendency to sellstocks that are generating gains too early

out of fear that stock prices may start to

decrease.

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chapter 4 behavioural finance[1]

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