U C M H o n o r s P r o j e c t

Disposition Effect: Experiment and Analysis of VariablesAlyssa HofstetterThe disposition effect has many real-world implications in financial fields. The definition of the disposition effect is: the tendency for stockholders to sell winning assets too early, and to hold losing assets too long. By selling winners too early and holding losers too long, stockholders are losing out on much of the potential profits due to poor decision-making. In this study, I performed an experiment to test for the disposition effect. I used fifty-eight subjects who performed in a simulation of a financial market, and provided demographic information to research correlations. I found that the majority of subjects did present disposition effect, and there were some potential correlations between disposition effect and: gender, age, major, and level of risk-aversion.

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I. Introduction

The disposition effect is a phenomenon in behavioral finance stating that

investors tend to sell winning assets too early, and to hold on to losing assets too long.

Empirical evidence of this has been found in numerous studies and possible theoretical

explanations have been discussed. One explanation uses prospect theory, which

applies value to utility gains/losses. Investors apply a greater loss in value to selling

losing assets; thus, investors desire to put off this negative utility, and present

disposition effect. The same explanation applies to selling for positive utility too early.

Endowment effect is another theory providing a possible explanation for disposition

effect. Endowment effect suggests that when assets are owned by the trader, or the

trader feels a sense of ownership over the assets, the trader will be more likely to trade

based on emotions, and less on rational decision making. This suggestion could have

applications in financial advising, such as: how should advisors be paid? Or, all else

held equal, how much more advantageous is it to utilize an advisor with no endowment

effect? Implications (like these) of continued research into the disposition effect and

behavioral finance are quite relevant to many, and could have many real world

applications yet to be fully utilized. Previous research has tried connecting dots between

disposition effect and many various factors, and through the data collected in my

experiment this semester, I plan to try connecting a few more.

In this paper I will analyze the effects on disposition effect of factors such as:

finance specialization, gender, age, personality type, risk-aversion level, and wealth-

status. This evidence has been collected through an asset simulation run on collegiate

students. Students must to decide whether to sell or hold a portfolio of assets through a

series of periods as the asset values fluctuate. This study will show which students

present disposition effect more, which will then be connected to data collected on their

demographic factors.

Section II is a continued introduction to disposition effect (abbreviated as DE

from here on out), covering previous research done on the topic including relating DE to

gender, mutual fund performance, and even psychophysiological studies. Section III is a

detailed description of my experiment from start to finish. Section IV will describe the

results of my experiment. Finally, sections V and VI will discuss statistical data analysis

and theoretical analysis with potential implications therein.

II. Previous Research

Research on the disposition effect began in 1985 with the publication of Shefrin

and Statman’s article, “The Disposition to Sell Winners Too Early and Ride Losers Too

Long: Theory and Evidence”. However, Kahneman and Tversky’s 1979 article on

Prospect Theory laid the behavioral finance background to develop the disposition

effect. Thus, prospect theory holds as a primary explanation for DE as I explained in the

introduction. Shefrin and Statman (1985) began research on analyzing the anomaly by

proving it with empirical evidence on data from another study, and actual mutual fund

trading data. Their focus with this data is on the length of time between the purchase of

an asset and the sale of the asset. Since then, other studies have been done, including

numerous experiments designed and run specifically for gathering data on the DE. In

1997, Weber and Camerer proved experimentally that subjects did in fact sell winners

too early and hold losers too long, despite Bayesian optimization, as did Odean in 1998.

In 2001 Odean did a study on overconfidence in investing and excessive trading

levels related to gender. Overconfidence is a possible explanation for DE, in that when

the subject is overconfident in their abilities, they are likely to trade more frequently (on

winning stocks) or hold losing stocks too long, believing that they will beat the market. In

reality, investors rarely beat the market, which is a commonly known truth in finance. Of

course, there are exceptions to the rule, and with overconfidence, everyone believes

they are the exception, and thus, we see the anomaly. In Odean’s 2001 study, he found

that overconfidence is seen more in men than in women, 45% more in fact. “Trading

reduces men’s net returns by 2.65 percentage points a year as opposed to 1.72

percentage points for women.” (Odean 2001) As these results directly prove

overconfidence and gender correlations, the results in Da Costa Jr, Meneto, and Da

Silva’s 2008 study also proved correlation between gender and DE. These results were

found fourteen years, and seven years ago from today, respectively, so I included

gender as a variable in my current study to see if there is still a correlation, or if the

rapidly changing times have adjusted those inclinations in men and women today.

Another study looked at the correlation between investor “sophistication” and DE

(Dhar and Zhu (2006)). “Sophistication” in this study meaning the level of wealth,

education, literacy, and job prestige. Their results: “sophistication” has a negative

correlation with DE, or, more “sophisticated” investors present less DE. I found this to

be very interesting, and decided that I would include a question in my survey pertaining

to wealth and income to compare results.

Recent studies have looked at DE and prospect theory (Henderson 2011),

mutual fund performance (Ammann, Ising and Kessler 2012), psychophysiological

correlates (Goulart, Da Costa Jr., Santos, Takase, and Da Silva 2013), and even social

interaction between investors (Heimer 2014). The 2013 paper on “Psychophysiological

Correlates of the Disposition Effect” is unique and interesting; subjects conducted asset

management while their physical vitals (heart rate, perspiration, and body temperature)

were measured. The results of the study found that subjects who presented greater DE,

perspired more, had lower heart rates, and lower body temperatures. The 2014 social

interaction report was also unique, but quite logical. Results of this study found that

more peer interaction due to recent increases in technology (such as investment social

media platforms) led to a large increase in DE. This is interesting because the economic

rule of rational behavior suggests that increased information should decrease errors,

thus decrease the DE. Unfortunately, instead we see a misuse of information sharing,

when the information provided is not accurate, or of poor quality, but believed to be true.

This leads to overconfidence (the idea that they have all this information, accurate or

not, makes them believe they have a better chance), in turn leading to higher trading,

and greater DE.

III. Experimental Design

The data used in the analysis of this paper was collected using an experimental

simulation and student subjects. The simulation is a computer program that replicates a

financial market. Each participant is asked to decide whether to hold or sell a valued

asset for twenty periods. There are three assets to be managed, and each asset begins

valued at $100. Each period, the asset values change based on randomly assigned,

actual historical stock price fluctuations. Some assets will rise in value, and some will

fall (depending on which historical period and stock the asset is assigned to replicate).

Each participant is randomly assigned a particular five-year period between 1962

through 2013 (twenty rounds, each representing one quarter). Then, the three assets

will be randomly assigned a different stock from the Dow Jones Industrial Average,

within that time period. It is up to the participant to decide how to manage the assets

they are given, their goal being to end with the highest possible value in their total

portfolio. Each participant is paid cash based on their portfolio results, so they have

complete incentive to make their decisions carefully, with thoughtful reasoning. Payment

criteria is as follows:

An unconditional $5 payment will be provided; additional payments are

determined by the following ranking (assigned 1st place participant first,

then through as many participants as there were):

*Performance ranking:

Real Dollar Value:

1st $25

2nd , 3rd $17

4th , 5th $12

6th , 7th , 8th $10

9th , 10th , 11th $9

12th , 13th , 14th $8

15th , 16th , 17th $7

18th , 19th , 20th, 21st ,

22nd , 23rd , 24th , 25th$5

*Performance ranking determined by the following formula:

( Actual ending portfolio value )(Maximum potential portfolio value)

*Each asset is different, and will have different potential results. Participants

will not be judged by which asset they are randomly assigned, but by how well

they manage the assets given.

The simulation results are sent directly to an administrator web page that I am

able to access immediately. The report lists each participant’s 700#, their ending

portfolio values, and the maximum potential portfolio value. A ratio of individual ending

values to max portfolio values is automatically computed, and used to rank each

individual in the last session from best performance, to worst (the closer the ratio is to

one, the better). Following the simulation, each participant was asked to complete a

demographic survey, providing their information on: major, age, gender, collegiate

standing, race/ethnicity, citizenship, family wealth status, income, personality type, and

level of risk-aversion. These variables will be tested on correlation with the disposition

effect in the analysis section of this paper.

I used undergraduate students as participants in this experiment. Students were

recruited using a message of request to be either read to classrooms, or sent through

emails. I had hoped to get half finance students and half non-business students, to

focus my analysis on the correlation between financial education and DE. Unfortunately,

I was only able to recruit two finance majors and six economics majors (both majors that

learn about the disposition effect). While this is not enough data to make a valid claim,

we did get a wide variety of other majors, giving us an opportunity to consider potential

correlations. I was able to get a variety of other demographics as well. Invited

participants were sent emails when an upcoming session was scheduled, and asked to

rsvp via email prior to the event.

IV. Results

I was able to hold three sessions composed of 20, 13, and 25 participants in

each session, a total of 58 participants. Unfortunately, there was a system error on two

of the participant’s simulations, and their results were not recorded. Therefore, I have 56

testable trials. This number is adequate to determine whether disposition effect is

present or not. The data used to determine correlation between other variables is not

quite as statistically sound, particularly when each characteristic has more than two

variables, providing less than 30 trials as evidence (30 or greater is a statistically sound

number of trials to draw quality conclusions from). The spreadsheet containing all the

data gathered is attached at the end of this report (listed anonymously). A summary of

the data is as follows:

Performance:

“Performance” is equal to a participant’s ending wealth divided by the maximum

possible wealth of the assets they were given. Essentially, performance is half of what

makes up the Disposition Effect; subjects are likely to sell winning assets too early. If

there were absolutely no disposition effect, then the subject’s performance would equal

1. Overall, performance ranged between 0.236 and .947. I also somehow had two

outliers, 1.009 and 1.097, where the ending values were actually greater than the max

possible wealth. We are assuming this is a system error, and I will not include it in the

analyses. None of the participants were international students, so I was unable to see

any differences between nationality cultures. I had a wide variety of majors, the largest

groups being: Marketing, Sports Management, Business Management, Criminal Justice,

and Economics. The performance range of Econ (5) and Finance (2) majors was .846

through .553, which was on the high end, as the average was .659, and the median was

.687. The performance range of non-business majors was between .932 and .239 (Non-

business majors include: Biology, Child and Family Development, CIS, Criminal Justice,

Electronic Technology, Elementary Education, English Education, Exercise Science,

Fashion Merchandising, Industrial Technology, IT, Physical Therapy, Psychology,

Public Relations, Safety Management, and Sports Management. Business majors

include: Accounting, Economics, ESE, Finance, Management, and Marketing.) I had 22

females and 32 males, performance ranging between .893 and .239 for females, and

between .947 and .236 for males.

Most participants fell within the 18-23 age range, which closely followed the

collegiate standing measure. There were 17 freshmen, whose performance ranged

between .392 and .893, 16 sophomores, ranging from .236 to .850, 4 juniors ranging

from .553 to .805, and 12 seniors ranging between .682 and .787. I had 33 white

students, 18 black students, and 1 Asian student. White performance ranged from .236

to .947, black: .239 through .850, and the Asian student got .528. The majority of

participants considered themselves middle class, performance ranging from .236

to .947. Four students referred to themselves as being upper class, and they performed

in a range of .392 and .932. Another four students classified under lower class, and

performed in a range of .463 to .667. Because we collected data from students, about

half of them had no income, and the other half had part time jobs, but this characteristic

did not have a significant impact on performance.

I asked a question on the demographic survey regarding risk preference, and 32

of the participants indicated that they are willing to take some risk, and 15 said that they

are willing to take a large amount of risk. The performance ranges on the risk variable is

slightly more significant than most of the other variables: “some risk”, .392-.932, and

“much risk”, .236-.947. I also found that the standard deviation of the “some risk”

category was only .133, while the standard deviation of the “much risk” category

was .224. I also included questions pertaining to personality type in the survey, but

there were no significant differences in the performance of each, and I was unable to

obtain a complete sample, so there is little backing to any statements.

Disposition Effect:

To determine if disposition effect is present in our experiment, I needed to find

the percentage of realized gains and percentage of realized losses per participant. If the

percentage of realized gains is larger than the percentage of realized losses, there is a

disposition effect. To calculate this, I counted the number of chances that each

participant had to sell each asset at a gain (where the value is greater than the

starting/purchase value, of $100), and the number of chances they had to sell the asset

at a loss (a value below $100). Then, I added the number of times that they sold the

asset at a gain, and the number of times they sold the asset at a loss (0, 1, 2 or 3). To

calculate realized gain/loss I used the formulas:

%Realized Gain= (¿of×sold at again)(¿ of chances¿ sellat again )

%Realized Loss= (¿of×sold at a loss)(¿of chances¿sell at a gain)

To determine disposition effect, I used the logical function:

If :%Realized Gain>%Realized Loss

Then :Disposition Effect=True

I found that 29 observations tested positive for disposition effect, and 18 observations

tested negative for disposition effect. Therefore, 61.7% of participants presented

disposition effect.

V. Analysis

Overall, we see a majority of the participants presenting disposition effect, which

is in line with the academic consensus on the subject. This means that subjects sell

earlier than they should on winning assets, and hold longer than they should on losing

assets. The fact that what subjects “should” do is the opposite of what they actually do,

is enough to make us pause and ask, why?

Correlations:

Going into this experiment, I had pondered the hypothesis that age would have a

negative correlation with disposition effect (i.e. the older the subject, the less disposition

effect they would present). Interestingly however, the opposite proved to be true in my

observation. About half of my participants were 20 years old or older, and 70.8% of

them presented DE, while of the other half that were 19 years and younger, only 52.2%

presented DE. A 20% difference is quite substantial, yet hardly explainable. My only

guess is that many of the younger participants allowed their assets to force sell in the

last period, suggesting a more index-following approach to asset management, which

typically provides less disposition effect since there is little to no intentional selling or

holding. If they took this approach intentionally, knowing that index funds require less

reliance on decision-making, and thus present less mistakes and DE, then this is

evidence suggesting that younger subjects are “better” at asset management. However,

it is more likely that they just did not know what else to do and did not want to make a

mistake that hurt them and just let the assets ride out regardless of the direction they

were going, and it just so happened that more of them were increasing than decreasing.

Previous studies had found that females present less disposition effect than

males (Da Costa 2008). However, in my analysis I found that females presented DE

70% of the time, while males presented DE 55.6% of the time. This could be explained

by the fact that I only had 20 useable female observations and 27 useable male

observations, which is not quite enough to make any substantial claims.

The level of risk-aversion characteristic proved to have a slight impact on

disposition effect: participants indicating a medium level of risk-aversion (take some

risk) presented 57.7% disposition effect, while participants willing to take more risk

presented 64.3% disposition effect. Most asset managers that are willing to take more

risk, wind up trading more frequently on winning stocks, because they are more

confident on their abilities, and getting into risky investments that may begin losing

value, and turning into losing assets, which they hold onto hoping for an upturn. This is

the definition of DE, and it is shown to be present in this study. Disclaimer: I only had 26

useable subjects pertaining to the “some risk” category, and only 14 in the “much risk”

category.

Majors ranged widely as I mentioned in the performance section above, but the

main categories I was interested in regarding disposition effect were the econ/finance

versus non-econ/finance. As it happens, I had 6 useable econ/finance major

observations, and three tested positive for disposition effect, and three tested negative

for DE. While this is not a large enough sample to justify a conclusion, I would like to

believe that if there were going to be a significant outcome, the six that I was able to

observe would have had at least a slight lean one way or the other rather than exactly

50% presenting DE. In comparison, of the 41 useable non-econ/finance subjects, 63.4%

of them presented DE, but this is easily explained by the sheer number of them being

much greater than the econ/finance majors and thus, having more opportunities to

present DE.

Conclusions:

While I was unable to obtain enough participants in the sub-categories to make

any new, empirically proven, conclusions, I did have enough to determine that the

majority of subjects did in fact present disposition effect. This is enough to solidify the

previous researchers’ conclusions on the subject. I would suggest future studies done

on the gender, age, and major sub-categories, to find more concrete correlations.

VI. References

Ammann, Manuel, Alexander Ising and Stephan Kessler. "Disposition effect and mutual fund performance." Applied Financial Economics 22 (2012): 1-19.

Da Costa Jr., Newton, Carlos Mineto and Sergio Da Silva. "Disposition effect and gender." Applied Economics Letters 15 (2008): 411-416.

Dhar, Ravi and Ning Zhu. "Up Close and Personal: Investor Sophistication and the Disposition Effect." Management Science 52.5 (2006): 726-740.

Heimer, Rawley Z. "Peer Pressure: Does Social Interaction Explain the Disposition Effect?" Federal Reserve Bank of Cleveland. (2014): 1-8.

Henederson, Vicky. "Prospect theory, liquidation, and disposition effect." Management Science 58.2 (2012): 445-460.

Kahneman, Daniel and Amos Tversky. "Prospect Theory: An Analysis of Decision Under Risk." Econometrica 47.2 (979): 263-291.

Odean, Terrance. "Are Investors Reluctant to Realize Their Losses?" The Journal of Finance LIII.5 (1998): 1775-1798.

Sewell, Martin. "Behavioural Finance." University of Cambridge (2007): 1-7.

Shefrin, Hersh and Meir Statman. "The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence." The Journal of Finance XL.3 (1985): 777-790.

Weber, Martin and Colin Camerer. "The disposition effect in securities trading: an experimental analysis." Journal of Economic Behavior & Organization 33 (1998): 167-184.

VII. Appendix A: Asset Simulation Program

InstructionsYou currently hold three assets whose values change over time. All assets will begin with a value of $100, but their values will vary independently after the first period, meaning that the value of each asset could go up or could go down in each period.

For each period of the game, you will be given a table that shows the return earned on each asset in the previous period and the average return for each of your three assets over all periods of the game. In addition, you will see a market index showing the return of all assets, both yours and others, over the period.

In each period, you may choose to hold or sell each asset. If you choose to sell, simply click on the ``sell'' button next to the asset you decide to sell. If you choose to hold the asset, do nothing. When you sell an asset, the price that you receive will be delivered into your ``Cash'' drawer. Once you sell an asset, you cannot buy it back. You will, however, continue to receive updates on the asset's performance.

Each trading period will last for 20 seconds. There is a countdown clock at the top of your trading screen to allow you to monitor your time. You will play the game for 20 periods. In order to begin, please enter your Trader ID and the ID of the person administering the game.

Sample Game Table

VIII. Appendix B: Demographic Survey

Please fill out, or circle, the following demographic information as it applies to you. You are welcome to omit any questions you do not wish to reveal. This survey is used for

academic data collection purposes only, and data presentation will be strictly anonymous. Your name and 700# will only be used to connect your information to your results in the simulation, and to provide you with the accurate payment you earned in the simulation. If you have any questions you may ask me at any time. Thank you for

your cooperation.

Name:________________________________ 700#:________________________

Major:________________________________ Age: ___________

Gender (circle one): Male / Female

Collegiate Standing (circle one): Freshman / Sophomore / Junior / Senior

Race/Ethnicity (circle all that apply): Hispanic or Latino / American Indian or Alaska

Native / Asian / Black or African American / Native Hawaiian or Other Pacific Islander /

White

Citizenship: U.S. Citizen / Other:_______________________

Family Wealth Status: Lower-class / Middle-class / Upper-class

Personal Income: No income (Full-time student) / $1,000 - $8,000 (Part-time worker) /

$8,000 - $16,000 (Part/Full-time, minimum wage worker) / $16,000 - $32,000 / greater

than $32,000

How would you describe your personality type? (If you know your Myers-Briggs

type, please write it here: ______________ . If you do not know your Myers-Briggs

type, you may circle the descriptions below that best suit you.)

I prefer to focus on: the outer world / your own inner world

I prefer to focus on: realistic, basic information / my interpretation and analysis

When making decisions, I prefer to first look at: logic and consistency / the

people and special circumstances

In dealing with the outside world, I prefer to: make decisions quickly / stay

open to new information and options

How would you describe your level of risk-aversion?: I do not like to take any risks

at all, regardless of the return. / I take some risk, allowing for a moderate reward. / I am

comfortable with taking risk, even for a small return.

IX. Appendix C: Participant Data

[See Excel attachment on next page.]