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The Disposition Effect in the Venture Capital
Decision-Making Process: An Experimental Approach
Job Market Paper
Simposio de Analisis Economico 2008, Zaragoza
Marta Maras
Universitat Pompeu Fabra
December 12, 2008
1
MOTIVATION (1)
 Problem
 Mr. Martinez owns a portfolio consisting of stocks A and B. Since
the purchase stock A went up in price from €30 to €50, while
stock B was less successful and went down in price, from €70
down to €50.
 At the moment Mr. Martinez needs to raise money and is thinking
of selling part of his portfolio.
 Which stocks should he decide to sell? Winning or losing stocks?
December 12, 2008
2
MOTIVATION (2)
 Empirical Evidence shows the following (Frazzini, 2004):
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MOTIVATION (3)
 Disposition Effect (Shefrin&Statman, 1985)
 Tendency of investors to retain losing investments in their
portfolios longer relative to their winning investments
 Losses hurt more than gains benefit
 Wealth-destroying behaviour
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LITERATURE REVIEW
 Disposition Effect (Shefrin&Statman, 1985)
 MARKETS
 Stocks (Odean, 1998; Ranguelova, 2001), Stock options (Heath et al., 1999),
 Real-estate market (Genesove&Mayer, 2001), Futures (Heisler, 1994;
Locke&Mann, 1999)
 INVESTOR BEHAVIOUR
 Above-average risks after losses (Coval&Shumway, 2005)
 Profitability of momentum trading strategy (Grinblatt&Han, 2001)
 Post-earnings announcement drift (Frazzini, 2006)
 INVESTOR TYPE
 Individual vs. professional (Shapira&Venezia, 1998)
 Wealth, experience, trading frequency (Dhar&Zhu, 2005; Chen et al., 2004)
 EXPERIMENTS
 Share positions automatically closed (Weber&Camerer, 1998; Chui, 2001)
 Markets with different trading mechanisms (Oehler et al.,2002)
 Stability across tasks and time (Weber&Welfens, 2006)
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CONTRIBUTION
 Existence of the Disposition Effect in venture capital markets?
 Importance of venture capitalists as intermediaries in financial markets
 Previous experimental studies focus on stock market only
 Differences in predictability, trading opportunities, provision of funds,
publicity of information
 Introduction of the following features:
 Learning prior to decision making  controlled expertise
 Variations in competitive environment (venture selection process)
 Decomposition of performance  learning, investment choice and
management
 Assessing costs and benefits of competition
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DESIGN (1)
 Prior learning and varying levels of competitive environment
introduced by means of 2x3 between-subjects experimental design
LEARNING STAGE
* Multiple Cue Probability
Learning Task
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DESIGN (1)
 Example of Multiple Cue Probability Learning Task
VENTURE
7
Estimate the future return of this venture.
Management Capability
3
Market Growth
-6%
Timing of Entry
Pioneer
Competitive Rivalry
5
FUTURE RETURN
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8
DESIGN (1)
 Cue Abstraction Model:
Y     1XMG   2 XMC   3 XTE   4 XCR  
where  (=-25), 1 (=8),  (=2.5),  (=3.5) and  (=1)
Y = venture return in the next 10 years
([-100%,100%], 10% increments)
XMG = market growth ([-10%,10%], 2% increments)
XMC = management capability (1-10 scale)
XTE = timing of entry (pioneer, intermediate, late follower)
XCR = competitive rivalry (1-10 scale)
December 12, 2008
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DESIGN (2)
 Prior learning and varying levels of competitive environment
introduced by means of 2x3 between-subjects experimental design
LEARNING STAGE
INVESTMENT STAGE
* Multiple Cue Probability
Learning Task
NO COMPETITION
* Venture Selection
* Cue Importance Ranking
COMPETITION
* Venture Competition
ASSIGNMENT
* Venture Assignment
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DESIGN (2)
 Example of Venture Attribute Matrix in Period t=0
VENTURE SELECTION
Invest up to ECU 1,000,000 in the ventures according to your preferences.
VENTURE
Management Capability
Market Growth
Timing of Entry
Competitive Rivalry
A
B
C
D
E
F
G
2
-6%
Pioneer
4
8
2%
Late Follower
7
5
8%
Middle
3
3
-4%
Late Follower
9
7
-10%
Late Follower
2
4
4%
Pioneer
6
6
0%
Middle
8
INVESTMENT
TOTAL INVESTMENT
December 12, 2008
CASH
11
DESIGN (3)
 Prior learning and varying levels of competitive environment
introduced by means of 2x3 between-subjects experimental design
LEARNING STAGE
INVESTMENT STAGE
* Multiple Cue Probability
Task
NO COMPETITION
* Venture Selection
* Cue Importance Ranking
COMPETITION
* Venture Competition
Venture
Management
ASSIGNMENT
* Venture Assignment
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DESIGN (3)
 Investment Stage – Investment Process
t
=
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December 12, 2008
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13
DESIGN (3)
 Example of Venture Performance Matrix in Period t=1
VENTURE PERFORMANCE
Invest up to ECU 800,000 in your ventures. It is possible to exit, invest between
ECU 50,000 and 600,000 in each venture and/or deposit the money in cash.
t=1
VENTURE
A
RATE OF RETURN
VALUE/SELLING PRICE
COST OF INVESTMENT
PROFIT/LOSS
NEW INVESTMENT
EXIT
PAYOFF
B
C
D
F
G
1%
151500
150000
6%
318000
300000
-6%
47000
50000
-14%
0
0
14%
285000
250000
-4%
192000
200000
0
1500
18000
-3000
0
35000
-8000
TOTAL INVESTMENT
TOTAL PAYOFF
December 12, 2008
E
-6%
0
0
CASH
14
DESIGN (4)
 Optimal Investment Strategy
Invest full amount provided in t=0 (no cash) in 2 ventures with
best attribute values according to the cue model (due to upper
bound on individual venture investment)
 Keep both in portfolio until t=5 by investing maximally in the more
profitable venture

December 12, 2008
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DESIGN (5)
 Disposition Effect (Shefrin&Statman, 1985)
 Presence of disposition effect is reflected in the significant
difference between proportion of gains realised (PGR) and
proportion of losses realised (PLR)
Realised Gains
 Proportion of Gains Realised (PGR)
Realised Gains  Paper Gains
Realised Losses
 Proportion of Losses Realised (PLR)
Realised Losses  Paper Losses
Disposition Effect: PGR > PLR
December 12, 2008
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RESULTS (1)
 No strong evidence of Disposition Effect – with purchase price as
the reference point behavioural pattern in accordance with standard
economic theory (Ivkovic & Weisbenner, 2007) (table1)
 General consistency in realising losses and apparent heterogeneity
regarding realisation of gains
 No effects of training on Disposition Effect – training limited to
venture selection, not venture management
 Conflicting evidence in empirical studies (Feng & Seasholes (2005),
Dhar & Zhu (2005) and Genesove & Mayer (2001) vs. Chen et al.
(2004))
December 12, 2008
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RESULTS (1)
 Alternative measures of the Disposition Effect
 Price trends = Last period price as reference point (table2)
 Individual level effects = Disposition Coefficients (table3)
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RESULTS (2)
 Prior learning proved successful in training participants to make
better venture choices (visible from portfolio compositions) (table4)
 Expertise Effect – Participants with higher levels of learning
(reflected in correlation values between predictions and realisations)
had higher earnings in the experiment (better performance in the
investment stage)
December 12, 2008
19
RESULTS (2)
 Expertise Effect  Correlation coefficients between judgement
achievement components and performance measures (table5)
Linear Knowledge Linear Consistency
Judgment
a
(G)a
Achievement (ra)
(Rs)a
Residual
Achievement (C)a
Experimental Earnings
0.37
0.32
0.31
-0.05
Proportion of Gains Realized (PGR)
-0.13
-0.04
-0.06
-0.18
Proportion of Losses Realized (PLR)
-0.09
-0.14
-0.06
0.25
Disposition Effect
0.01
0.09
-0.02
-0.28
Note: correlation coefficients significant at p<0.01 are in bold, significant at p<0.05 are underlined. Sample size N=60.
a
after Fisher's z transformation
December 12, 2008
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RESULTS (2)
 Behaviour in subsequent venture management points to different
dimensions of expertise, specifically, learning to choose and
learning to manage
 If overall portfolio performance (i.e., earnings) is decomposed into
elements involving learning, choice and management of holdings, it is
shown that after training participants who faced competition had
better venture management strategies than others
December 12, 2008
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RESULTS (3)
 Optimal Strategy Analysis  Tracing reasons for earnings’ decreases
Treatment
Total Decrease
Earningsa
in
Learning
b
Decrease due to
Choicesc
Managementd
No Learning
No Competition
Competition
Assignment
0.25
0.39
0.25
-
0.17
0.30
-
0.09
0.09
0.08
Learning
No Competition
Competition
Assignment
0.22
0.34
0.23
0.05
0.10
0.07
0.04
0.18
-
0.13
0.07
0.14
Note: Sample size N=60.
comparison between the optimal and actual earnings of participants
b
comparison between the optimal earnings and maximal earnings according to the participants' models from
the learning stage (given optimal venture choices)
c
comparison between the optimal earnings (maximal earnings given optimal venture choices according to the
participants' models) and maximal earnings given their actual venture choices in No Learning (Learning).
The comparison cannot be calculated for Assignment treatment due to venture assignment feature in period t=0.
d
comparison between the maximal earnings given actual venture choices of participants and their actual final
earnings
a
December 12, 2008
22
RESULTS (3)
 Optimal Strategy Analysis  Holding periods of winning and
losing investments
Treatment
No Learning
Winners
Losers
Learning
Winners
Losers
No Competition
4.10
2.91
4.07
2.52
Competition
4.24
3.21
4.35
2.08
Assignment
4.01
2.72
4.03
2.44
 No Learning – no differences in holding periods
 Learning – equal holding periods of losing investments
 No Competition and Assignment – no change in holding strategy
 Competition – longest holding periods per winner, sold losing
investments earlier after training
December 12, 2008
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RESULTS (3)
 Initial venture competition and interaction gave incentives for a
more rational management of acquired ventures
 Competition  best environment for reaching optimality in
management (invested most per winner)
 Free selection  enhanced portfolio choices after training,
invested more per winner and less per loser, but underfunded and
sold best performing ventures
 Assigned choices  no effect of training on investment behavior
December 12, 2008
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DISCUSSION
 Implications – costs and benefits of competition
 Competition as the most efficient form of resource allocation and
management
 Costs = competition is expensive and can lead to wasted effort
(not getting the first-choice ventures)  inferior portfolios
 Benefits = enhanced strategies in management regarding winning
and losing ventures
December 12, 2008
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THANK YOU!
[email protected]
December 12, 2008
26
RESULTS (1*)
 Disposition Effect Analysis  Significance of PGR-PLR Difference
No Learning
Learning
Treatment
PGR
PLR
p-Valuea
No Competition
t=1
t=2
t=3
t=4
Mean
0.02
0.01
0.07
0.12
0.05
0.67
0.76
0.92
0.95
0.83
0.00
0.00
0.00
0.00
0.01
0.00
0.08
0.06
0.04
0.82
0.94
1.00
1.00
0.94
0.00
0.00
0.00
0.00
Competition
t=1
t=2
t=3
t=4
Mean
0.00
0.07
0.03
0.00
0.02
0.78
0.70
0.89
1.00
0.84
0.00
0.00
0.00
-
0.00
0.05
0.00
0.17
0.05
0.71
0.70
0.88
0.75
0.76
0.00
0.00
0.00
0.07
Assignment
t=1
t=2
t=3
t=4
Mean
0.05
0.06
0.07
0.23
0.10
0.73
0.73
0.80
0.81
0.77
0.00
0.00
0.00
0.02
0.05
0.25
0.00
0.47
0.19
0.86
0.75
1.00
0.53
0.79
0.00
0.13
0.44
a
PGR
PLR
p-Valuea
Entries indicate statistical significance of difference between the proportions of gains realized (PGR) and
the proportions of losses realized (PLR)
December 12, 2008
(back)
27
RESULTS (1*)
 Disposition Effect Analysis  Price Trends
Number of Exits in Period t Depending on Venture Value Gain (G) or Loss (L)
in Periods t-1 and t-2
Panel A. No Learning
Price
Trend
t-2
t-1
G
G
L
G
G
G
L
L
L
L
After G
After L
December 12, 2008
No Competition
Exits
%
43
2
2
29
39
46
47
114
26.7
1.2
1.2
18.0
24.2
28.6
29.2
70.8
No Learning
Competition
Exits
%
10
2
0
10
41
24
12
75
11.5
2.3
0.0
11.5
47.1
27.6
13.8
86.2
Assignment
Exits
%
46
4
7
13
32
57
57
102
28.9
2.5
4.4
8.2
20.1
35.8
35.8
64.2
28
RESULTS (1*)
 Disposition Effect Analysis  Price Trends
Number of Exits in Period t Depending on Venture Value Gain (G) or Loss (L)
in Periods t-1 and t-2 (back)
Panel B. Learning
Price
Trend
t-2
t-1
G
G
L
G
G
G
L
L
L
L
After G
After L
December 12, 2008
No Competition
Exits
%
52
4
2
14
12
33
58
59
44.4
3.4
1.7
12.0
10.3
28.2
49.6
50.4
Learning
Competition
Exits
%
10
0
0
9
29
27
10
65
13.3
0.0
0.0
12.0
38.7
36.0
13.3
86.7
Assignment
Exits
%
55
9
9
9
6
28
73
43
47.4
7.8
7.8
7.8
5.2
24.1
62.9
37.1
29
RESULTS (1*)
 Disposition Effect Analysis  Cumulative distributions of
disposition coefficients () (back)
 = (S+ - S-) / (S+ + S-))
Panel A. No Learning
December 12, 2008
S+ (S-) = number of sales of winners (losers) if
venture gained (lost) in value in the last period
Panel B. Learning
30
RESULTS (2*)
 Expertise Effect  Comparison of Environmental Model, Actual
and Stated Decision Policies
Environmental Model
Cues
Market Growth
Management Capability
Timing of Entry
Competitive Rivalry
a
Actual Decision Policy
b
Stated Decision Policy
Mean
Rank
Mean
Rank
Meanc
Rank
0.416
0.006
0.004
0.002
1
2
3
4
0.422
0.034
0.001
0.009
1
2
4
3
45.52
20.97
15.42
18.10
1
2
4
3
a
omega squared values for each cue based on the cue abstraction model
b
c
omega squared values for each cue based on the 30 trials completed in the MCPL task
according to the division of 100 points among the cues in line with their judgment importance
December 12, 2008
31
RESULTS (2*)
 Expertise Effect  Lens Model Components
Measure
Judgment Achievement (ra)
Linear Knowledge (G)
Linear Consistency (Rs)
Residual Achievement (C)
Mean
Standard
Deviation
Minimum
Maximum
0.84
0.97
0.93
-0.03
0.17
0.13
0.09
0.18
0.32
0.46
0.65
-0.41
0.98
1.00
0.99
0.33
a
Note: Predictability of the criterion (Re) = 0.97. Sample size N=60.
a
after the Fisher's z transformation
December 12, 2008
32
RESULTS (2*)
 Expertise Effect  Pairwise Correlation Coefficients between
Judgment Achievement Components and Participant Demographics
Judgment
Achievement (ra)a
Linear
Knowledge Linear Consistency
(G)a
(Rs)a
Residual
Achievement (C)a
Gender
-0.22
-0.02
-0.33
-0.20
Economics-related studies
0.33
0.25
0.31
-0.11
Financial Experienceb
0.21
0.18
0.14
-0.01
Note: correlation coefficients significant at p<0.01 are in bold, significant at p<0.05 are underlined. Sample size N=60.
a
after Fisher's z transformation
self-assessed on a 1-10 scale
b
(back)
December 12, 2008
33
RESULTS (2*)
 Portfolio Composition and Experimental Earnings (back)
Treatment
Earnings
No Learning
Ventures
Winnersa
Losersa
Earnings
Learning
Ventures
Winnersa
Losersa
No Competition
9.91
4.85
0.70
0.30
10.38
3.62
0.82
0.18
Competition
8.09
3.02
0.59
0.41
8.70
2.68
0.68
0.32
Assignment
9.97
4.85
0.70
0.30
10.18
3.62
0.82
0.18
a
proportions of winning (losing) investments in the portofolios of participants (averages across rounds)
December 12, 2008
34