Gulnur Muradoglu 1

Experimental Finance

Behavioral FinanceWeek 5

Read Muradoglu, 2001

Muradoglu et.al. 2005

Gulnur Muradoglu 2

Why Experimental Methodology?

Limitations of Share Price DataControlled Design

Gulnur Muradoglu 3

Muradoglu,2001

Motivation Efficient Markets Hypothesis, Fama Overreaction Hypothesis, DeBondt and Thaler

Experimental work by DeBondt, 1993 If investors are positive feedback traders, they will

expect past trends to continue in the future Anchors used will be determined by past price

changes and past price levels Confidence interval assessments will not be

symmetric

Gulnur Muradoglu 4

Limitations of DeBondt,1993

DeBondt experiments conducted by student subjects

“… an acceptable proxy for the typical investor?”

quasi experimental design“…does not control for other factors than past

price”

forecasts of various stock indexes and FXreal time forecast of specific stocks?

Short term forecast horizons

Gulnur Muradoglu 5

Purpose of Muradoglu,2001To investigate if return expectations

and risk perceptions of investors are adoptive? If so, what is the expectation formation

process and hedging behaviour? Is it similar for

stock market professionals versus novicesreal-time stock price forecasts versus

• real-time stock index forecasts• unknown calendar time, unnamed stock forecasts

different forecast horizons

Gulnur Muradoglu 6

Research Design and Procedure

Subjects Student subjects, 45

19 MBA, 26 undergraduatesexposed to EMH and financial forecasting

Professionals in stock market, 35all licensed brokersworking for brokerage houses15 prepare research reports20 managing funds and giving advice

Gulnur Muradoglu 7

Research Design and Procedure

Folder for response forms Info about the study Price series for unnamed stocks

in graphical and tabular form

Response sheets for unnamed stocks Response sheets for real-time forecasts

stock indexeight stocks of respondents’ choice

Questionnaire

Gulnur Muradoglu 8

Research Design and Procedure

Task Give point and interval forecasts

I estimate the Friday closing price, one week from now as...............................................pence The probability that the Friday closing price one week from now is greater than..........pence is 10%. The probability that the Friday closing price one week from now is less than...............pence is 10%.

For forecasting prices ofunnamed stocks, stock index,specific stocks

For forecast horizons ofone, two, four and twelve weeks (Long Term?)

Gulnur Muradoglu 9

Measurement

Expected price changeEPCi is the difference between the subject's

(k) point forecast of a stock (j) for a forecast horizon of (i=1,2,4,12) weeks (Fijk) and the last known price level (P0)

EPCi = Fijk- P0

The average EPCi is calculated as EPCi =jkEPCijk

DeBondt findings indicated• EPC i, bull 0• EPC i, bear < 0

• EPC i, bull EPC i, bear

Gulnur Muradoglu 10

Measurement

Risk Perceptions Confidence intervals

UCIijk = Hijk – FijkLCIijk = Lijk – Fijk

Mean SkewnessSi = jk (UCIijk - LCIijk)

DeBondt Findings indicatedS i, bull <0, S i, bear >0S i, bull < S i, bear

Gulnur Muradoglu 11

Tests for differencesExpected price changes and skewness

coefficients are normalised by dividing to matching standard deviations

t-statistics used for differences in meanscomparisons of

bull versus bear markets unnamed stocks, versus index, actual

stocks experts versus novices LT versus ST forecast horizons

Gulnur Muradoglu 12

Results

Extrapolate the series and hedge forecasts

EPC i, bull 0, EPC i, bear < 0 , EPC i, bull EPC i, bear

S i, bull <0, S i, bear >0, S i, bull < S i, bear

Experts behave like this for• unnamed stocks and unknown calendar time

– short forecast horizons of 1,2,4 weeks• real time index forecasts

– short horizons of 1, 2 weeksExperts are optimistic otherwise!Novices are optimistic!

Gulnur Muradoglu 13

Muradoglu,2001

Bull Market Bear Market

For unknown stocks and short forecast horizons

Gulnur Muradoglu 14

Results

Immaculate OptimismEPC i, bull 0, EPC i, bear >0 , EPC i, bull EPC i,

bear

S i, bull >0, S i, bear >0, S i, bull > S i, bear

Experts are optimistic for• Long horizons in forecasts of

– unnamed stocks, Index, Specific stocks

Novices are optimistic for• All forecast horizons for

– real time forecasts of Index and specific stocks– unknown stocks - insignificant (?)

Gulnur Muradoglu 15

Muradoglu, 2001

Bull Market Bear Market

Immaculate Optimism!!!

Gulnur Muradoglu 16

Results

Hedging SpeculationsTrend followers in bull markets have positive but

smaller skewness coefficients than contrarians• for short horizons of

– 2 weeks for index - experts– 1 week for specific stocks - novices

Trend followers in bear markets have positive and larger skewness coefficients than contrarians

• for long horisons of– 4, 12 weeks for unnamed stocks - experts– 12 weeks for index - novices

Gulnur Muradoglu 17

Muradoglu, 2001

Bull Market Bear Market

Trend Followers versus Contrarians

Gulnur Muradoglu 18

Results

Experts versus novices stocks traded at the stock exchange

Bear market EPC of experts < EPC novicesBull market skewness of experts > skewness novices

• Experts more optimistic in price reversals in bear markets• and hedge better on the continuation of a bullish trend

May be one reason for high volatility in the market ?Maybe anchor for adjustment is the last price, NOT

the price change ?

Gulnur Muradoglu 19

Muradoglu, 2001

Bull Market Bear Market

Novices versus Experts

NovicesExperts

Gulnur Muradoglu 20

ResultsDifferent forecast horizons

For unknown stocksEPC is higher for longer horizonsS is higher for longer horizons

For indexIn bull market EPC is lower for longer horizonsIn bear market EPC is higher for longer horizonsIn bear market S is lower for longer horizons

For stocks traded at the exchange EPC is higher for longer horizons S is higher for longer horizons

Gulnur Muradoglu 21

Discussions

Results are different from DeBondt mainly due to the presence of contextual information the trends in the stock market participants level of expertise forecast horizon

Gulnur Muradoglu 22

Discussions

Real-time, real-task forecasting behaviour is different! Many factors involved Task complexity increases exponentially Sometimes not possible to duplicate in

experimental setting

Gulnur Muradoglu 23

Discussions

Immaculate optimism Subjects extrapolate bullish trends and

expect price reversals in bearish trends Optimists exaggerate their talents! Underestimate likelihood of bad outcomes! Optimism accompanied by overconfidence! Source of high volatility (?) Source of various inefficiencies (?) Due to selection bias? - Optimism again!

Gulnur Muradoglu 24

Discussions

Different decision-making processes may be at work at different occasions! Actual heuristic might be

price change? Unnamed stocks?the last observation? Bull markets?long term mean? Bear markets?

Gulnur Muradoglu 25

Discussions

Behavioural assumptions of the EMH must be treated with caution!

Variations in risk premia should not only be explained by traditional risk measures!

Risk perceptions might differ across ….

Gulnur Muradoglu 26

DiscussionsMelding psychological and financial

research is necessary for a better understanding of financial markets!

Financial Theory must be based on more realistic assumptions of human behaviour!

Further research ?

Gulnur Muradoglu 27

Muradoglu, et.al. 2005 Motivation Morkowitz, 1959

mean - variance efficient portfolios estimations of expected risk and return from

past returns expectation formation process is assumed to be

rational We use subjective forecasts of investors to

represent expected prices and related variance - covariance matrix.

Gulnur Muradoglu 28

Muradoglu, et.al. 2005

Purpose: To investigate the portfolio performance of

subjective forecasts given in different forms expectation formation process is based on

subjective forecasts rather than past prices and human behavior is integrated into financial

modeling.

Performance compared to that of the standard approach of time series data.

Gulnur Muradoglu 29

Muradoglu, et.al. 2005

Contributions-1 Literature on forecasting studies focus on

accuracy; Yates et.al. 1991 Muradoglu and Onkal, 1994

biases Muradoglu, 2002 De Bondt, 1993 Andreassen, 1990

We focus on portfolio performance

Gulnur Muradoglu 30

Muradoglu, et.al. 2005

Contributions-2 Port folio performance studies focus on

export managed funds Ippolito, 1989

standard tests of market efficiency Fama, 1991

We focus on subjective forecasts of experts we investigate expert subjects revealing

judgement in different formats findings robust to task format.

Gulnur Muradoglu 31

Muradoglu, et.al. 2005

Research Design 31 experts working for bank affiliated brokerage houses. Reached at company - paid 20 hours training programs. All licensed as brokers

Managing funds giving investment advice to corporate and private

clients preparing research reports

No monetary/non monetary bonuses offered An opportunity to forecast stock prices and reveal

uncertainty in different formats.

Gulnur Muradoglu 32

Muradoglu, et.al. 2005

Procedure Participants were given a folder

containing three forms: Information about purpose of study Response sheets for real time forecasts Questionnaire about participants’

experience in stock market trading, its duration and information sources utilized.

Gulnur Muradoglu 33

Muradoglu, et.al. 2005

Response forms Same as you have

Task was defined as giving point forecasts interval forecasts probabilistic forecasts

For a horizon of one week25 compromises listed as ISEhighest volume of trade during previous yearseasy to follow, reduces task complexity

Gulnur Muradoglu 34

Muradoglu, et.al. 2005

MethodWe estimate the efficient frontier

using three sets of data representing three sets of expectation

formation processes.“Historical Efficient Frontier”

• Historical distribution of stock returns“Best estimate efficient Frontier”

• point and interval estimates of experts.“Probabilistic Efficient Frontier”

• probabilistic forecasts of experts

Gulnur Muradoglu 35

Historical Efficient Frontier

Min 2(RH)

subject to E(RH) = K

where 2(RH) the variance

E(RH) mean of the historical values of the stock portfolios

K different levels of the mean

WW

WR

N

N

NH

N

NH

w

w

w

wwwR

w

w

w

RERERERE

2

111211

212

2

1

21

)(

)()()()(

N

RRE

P

PPR

N

tit

i

it

ititit

1

1

1

)(

•R' is the (1XN)row vector of expected returns, •W is (NX1) column vector of weights held in each asset

•sum of weights add up to one •and negative weights are not allowed,

• is the (NXN) variance-covariance matrix•Expected returns and variance-covariance matrix

• calculated using the last 24 weeks Friday closing prices

Gulnur Muradoglu 36

Best Estimate Efficient Frontier

Min 2(RB)

subject to E(RB) = K

2(RB) and E(RB) are calculated from point and interval forecasts as:

UIFijt is the price level for which forecaster j assigns a 2.5% probability that the actual price of stock i will turn out higher,

LIFijt is the price level for which forecaster j assigns a 2.5 % probability that the actual price of stock i will turn out to be lower than her/his time t price estimate.

The experiment is designed such that the above distance corresponds to the two standard deviations assuming that the distribution of returns implied by forecasters is normal.

Off-diagonal covariance terms are calculated from historical returns

1

1)(

it

itijtji P

PPFRE

2

])([])([ 1111 ititijtititijt

ii

PPLIFPPUIF

Gulnur Muradoglu 37

Consensus Best Estimate Efficient Frontier

In the consensus forecast expected return E(Ri) and variances (ii) are calculated as follows:

J

RE

RE jij

i

)(

)(

J

LIF

ALIF

andJ

UIF

AUIF

where

PPALIFPPUIFA

jijt

it

jijt

it

ititititititii

2

])([])[ 1111

Gulnur Muradoglu 38

Probabilistic Efficient Frontier

Min 2(RP)

subject to E(RP) = K

2(Rp) and E(Rp) are the variance and mean calculated from the probabilistic forecasts

it is difficult to assume normality of distributions revealed by each forecaster

therefore we decided to form a consensus distribution by averaging the probabilities assigned to each interval by different forecasters for each stock as follows.

CPFIji is the consensus probability forecast for stock i in interval j.

PFIjin is the probability forecast for stock i in interval j of forecaster n.

Although the consensus distribution is closer to normal normality cannot be assured.

At this point we defined the risk based on losses rather than gains.

We assumed that forecasters are more concerned with large losses than with large gains.

N

njinji PFICPFI

1

• Therefore we used intervals correspond to losses larger than 3% on a weekly basis. • We formed the implied consensus normal distribution for each stock using the following optimization procedure.

• E(Rpi) is the expected return for stock i , • ii is the variance of returns for stock i,

• obtained from consensus probabilistic forecasts of professionals.

•F(.) stands for the normal cumulative distribution.

i

i

iiPi

CPFIFF

CPFIFtoSubject

REMax

2

1

)6()3(

)6(

)(

Gulnur Muradoglu 39

Estimations

Efficient frontiers are estimated using the Ibbotson Associates Encorr optimization program.

Names and weights of stocks at each portfolio recorded for minimum risk portfolio maximum risk portfolio four medium risk portfolios the portfolio that matches the standard deviation of the

actual market portfolio Index tracking portfolio is used on the benchmark portfolio Performance measured the week following the

forecasts/forecast horizon of experts.

Gulnur Muradoglu 40

Findings

Comparison of expectations formation process historical best estimate probabilistic efficient portfolio

Comparison of expected & realized returns historical best estimate probabilistic efficient portfolios

Investment performance of portfolios based on expert’s assessments compared to that based on historical data.

Gulnur Muradoglu 41

Expected Efficient Frontiers

Figure 1. Expected Efficient Frontiers

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Standard Deviation

Retu

rn

Historical Best Estimates Probabilistic

Gulnur Muradoglu 42

Expected Historical

Figure 2. Expected Historical Efficient Frontier Versus Realised Returns

-0.188

-0.003

-0.031

-0.064

-0.088

-0.107

-0.125

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Standard deviation

Re

turn

Expected Return Realised Return

Efficient Frontier Versus Realized Returns

Gulnur Muradoglu 43

Best Estimates Efficient Frontier Versus Realized Returns

Figure 3. Best Estimates Efficient Frontier Versus Realised Returns

-0.005

0.0160.020

0.0230.026

0.0290.033

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Standard deviation

Re

turn

Expected Return Realised Return

Gulnur Muradoglu 44

Probabilistic Efficient Frontier Versus Realized Returns

Figure 4. Probabilistic Efficient Frontier Versus Realised Returns

-0.109

-0.007 -0.006-0.003

0.0020.008

0.012

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

Standard Deviation

Re

turn

Expected Return Realised Return

Gulnur Muradoglu 45

Realized Returns of the Portfolios on the Efficient Frontiers

Figure 5. Realised Returns of the Portfolios on the Efficient Frontiers

-0.2

-0.15

-0.1

-0.05

0

0.05

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Standard Deviation

Retu

rn

Historical Best Estimates Probabilistic

Gulnur Muradoglu 46

Summary Expectations formation process based on historical prices:

loss on all portfolios minimum loss (.13%) index tracking portfolio maximum loss (18.8%) on minimum risk portfolio as risk

increases loss detonates. Expectations formation process based on probabilistic forecasts

improved portfolio performance at all risk levels mild losses, modest gains at higher risk levels

(1.2% max risk portfolio) Expectation formation process based on point and interval

forecasts. further improvement in performance at all risk levels. gains at all risk levels (except min risk portfolio) weekly returns of 1.4% to 3.3%.

Gulnur Muradoglu 47

Conclusion

We integrate human behavior into financial modeling.

We report the performance of portfolios based on real time forecasts of actual portfolio managers

Portfolio performance of subjective forecasts much better than that based on historical data.

Literature on poor forecast accuracy versus excellent portfolio performance!

Better performing financial models that utilize human judgement.