1. 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

2. 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?
FINANCIAL MARKETS RULE
“Cut your losses, ride your gains!”
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3. MOTIVATION (2)
Empirical Evidence shows the following (Frazzini, 2004):
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4. 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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5. LITERATURE REVIEW Disposition Effect (Shefrin&Statman, 1985) 5 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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6. 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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7. 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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8. DESIGN (1)‏ Example of Multiple Cue Probability Learning Task
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9. DESIGN (1)‏ Cue Abstraction Model:
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)
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10. DESIGN (2)‏
Prior learning and varying levels of competitive environment introduced by means of 2x3 between-subjects experimental design
LEARNING STAGE
* Multiple Cue Probability Learning Task
* Cue Importance Ranking
INVESTMENT STAGE
NO COMPETITION
* Venture Selection
COMPETITION
* Venture Competition
ASSIGNMENT
* Venture Assignment
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11. DESIGN (2)‏ Example of Venture Attribute Matrix in Period t=0
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12. DESIGN (3)‏
Prior learning and varying levels of competitive environment introduced by means of 2x3 between-subjects experimental design
LEARNING STAGE
* Multiple Cue Probability Task
* Cue Importance Ranking
INVESTMENT STAGE
NO COMPETITION
* Venture Selection
COMPETITION Venture
* Venture Competition Management
ASSIGNMENT
* Venture Assignment
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13. DESIGN (3)
Investment Stage – Investment Process
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14. DESIGN (3)‏ Example of Venture Performance Matrix in Period t=1
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15. 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
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16. Disposition Effect: PGR > PLR
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)‏
Disposition Effect: PGR > PLR
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17. 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))‏
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18. 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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19. 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)‏
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20. RESULTS (2)‏
Expertise Effect  Correlation coefficients between judgement achievement components and performance measures (table5)
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21. 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
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22. RESULTS (3)‏
Optimal Strategy Analysis  Tracing reasons for earnings’ decreases
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23. RESULTS (3)‏
Optimal Strategy Analysis  Holding periods of winning and losing investments
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
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24. 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
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25. 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
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26. THANK YOU!
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27. RESULTS (1*)‏
Disposition Effect Analysis  Significance of PGR-PLR Difference
(back)
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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
Panel A. No Learning
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29. 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
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30. RESULTS (1*)‏
Disposition Effect Analysis  Cumulative distributions of disposition coefficients (a) (back)
a = (S+ - S-) / (S+ + S-)) S+ (S-) = number of sales of winners (losers) if venture gained (lost) in value in the last period
Panel A. No Learning Panel B. Learning
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31. RESULTS (2*)‏
Expertise Effect  Comparison of Environmental Model, Actual and Stated Decision Policies
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32. RESULTS (2*)‏ Expertise Effect  Lens Model Components
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33. RESULTS (2*)‏
Expertise Effect  Pairwise Correlation Coefficients between Judgment Achievement Components and Participant Demographics
(back)
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34. RESULTS (2*)‏ Portfolio Composition and Experimental Earnings (back)
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