Why Does Venture Capital Performance Persist Over Time? Evidence from a Dynamic Simulation

1. Why Does Venture Capital Performance Persist Over Time? Evidence from a Dynamic Simulation March 28, 2011 Long Gao AGSM, University of California Riverside David Porter Economic Science Institute, Chapman University Richard Smith AGSM, University of California Riverside

2. 2 The Persistence Paradox • Persistence of financial performance is established for: • Venture Capital Funds • Mutual Funds • Hedge Funds • Persistence is paradoxical • Appears to be inconsistent with weak-form market efficiency. • Why do managers of VC funds leave money on the table? • Should entrepreneurs and investors take account of track record? • Does persistence imply skill?

4. 4 Persistence of VC Fund Performance • IRRs are significantly explained by the IRRs of prior funds. • Kaplan and Schoar(JF 2005) • Exit rate is positively related to exit rate of the prior fund. • Hochberg, Ljungqvist, and Lu (JF 2007)

5. 5 VC Experience, Reputation, and Performance • VC Experience is positively related to exit performance: • Exit rate (IPO or M&A) is positively related to VC firm experience. • Hochberg, Ljungqvist, and Lu (JF 2007) • Industry-specific experience of VC enhances the probability of successful exit (IPO or M&A). • Gompers, Kovner, Lerner, and Scharfstein(JFE 2008) • VC Reputation is positively related to exit performance: • Ventures backed by more reputable VCs (based on IPO capitalization share) are more likely to exit successfully. • Nahata(JFE 2008) • VC firm IPO share is positively related to probability of IPO exit. • Ivanov, Krishnan, Masulis, and Singh (JFQA forthcoming)

6. 6 Why does VC Fund Performance Persist? Luck v. Skill • The “Luck” hypothesis • Persistence because of sorting of opportunities and VC firms • Herding based on track record. • VC firms with records of success face lower cost, richer deal flow. • Potential for suboptimal aggregate performance. • The “Skill” hypothesis • Persistence because fund managers add value: • Expertise in selecting opportunities • Adapting to changing conditions • Monitoring investments • Shaping management teams • Facilitating and timing exits.

7. 7 Luck v. Skill – Sorensen (JF 2007) • Does VC experience (number of prior deals) affect success probability? • The value of experience is hard to assess due to endogenous sorting that is correlated with experience. • Sorensen is unable to find a suitable instrument to control for endogeneity – resorts to a selection-type approach.

8. 8 Luck v. Skill – Smith, Pedace, Sathe (2011) • Financial returns persist • But are mean-reverting • VCs add value beyond sorting: • Exercise of abandonment options • Style persistence • Weak evidence of the value of agility • Find evidence of the value of: • Industry-specific experience (controlling for exit type) • Generic reputation (controlling for exit type)

9. Motivations • Non-archival approaches to studying the relative roles of initial luck and skill in the VC market • Agent-based modeling • Laboratory experiments • With agent-based modeling… • Can we use reasonable behavioral assumptions to produce results similar to stylized facts we can observe in real markets? • Can we use the models to better understand what we observe in real markets? • Can we generate hypotheses that can be tested in laboratory experiments or with archival data? • A personal motivation: • To learn about the uses and value of some different research modalities.

10. Agent-based modeling • Computer simulates the interactions of independent agents in interactive/competitive environment • Agents are boundedly rational • Agents pursue strategies intended to maximize utility • Agents can learn over time and adapt strategies to new information in the market • Heterogeneity of strategies emerges endogenously

11. Project Summary • Stylized facts about the VC market • Overview of the model • Calibration of model assumptions • Model performance metrics • Results • when VCs follow static strategies • when VCs follow myopic dynamic strategies

12. Model Overview • Two types of agents: • Entrepreneurs (Es): Nt = set of Es • Venture Capital firms (VCs): Mt = set of VCs • Es participate only once, VCs are repeat players • Each Ei has one project of uncertain quality, • VCs vary in ability (αj) • Total project payoff depends on project quality and VC ability, • Payoffs to Es and VCs are, respectively,

13. Model Overview • Matching constraints: • Each project requires exactly one fund • VC capacity constraint • Es’ participation constraint

14. Model Overview • Knowledge assumptions • Es know… • Project class, • VC ability signals, • VC track records of payoffs to Es by VC, • VCs know… • Own ability, αj • Project quality of each Ei ,

15. Model Overview • Es’ strategies • Aggressive => propose to perceived top 1/3 of VCs • Conservative => propose to perceived bottom 1/3 of VCs

16. Model Overview • Adaptive sequential learning • Es can switch strategies based on expected performance of the strategy. • Genetic algorithm – random strategies

17. Model Overview • VCs’ strategies: static or dynamic ownership share in each period • Myopic local search based on the payoff improvement relative to the forecast • where a and b are the adjustment factors

18. Model Overview • Search Costs and Capacity Constraints • Search is costly for entrepreneurs • Can propose their projects to at most m VCs of their choice. • VCs have limited human and financial capital • Can undertake at most c projects.

19. Model Overview • Matching process – modified Gale-Shapley • Entrepreneur ranks VCs attempting to maximize the expected value over the set of choices • Search is de facto sequential – the E will accept the offer from his highest ranked VC regardless of s (knows only the history of payoffs to Es). • VCs rank all Es based on project quality and is matched with highest ranked Es first, provided that the E has not already been matched with a preferred VC. • E’s opportunity cost – VCs will not accept projects if they expect the project to return to the E less than the E’s opportunity cost (=1.0) (i.e., no ex ante hold-ups).

20. Model Calibration – base case • Es: • Market size: N = 300 entrepreneurs, • Entrepreneur’s search cost: proposal limit m = 3 • Project quality: K = 4 classes: quality ~ exp(k), • mean k = 1, 2, 3, 4 • truncated at 4, 6, 8, and 10 depending on class • VCs: • Market size: M=10, • Capacity limit: c=15 • Ability: uniform over [0.9, 1.5] • Horizon: T = 200 periods or more • Forecast adjustment factors for VCs: a=0.25, b=0.005 • Ability signal noise: uniform over [-0.2, +0.2] • Environmental factor in payoff: uniform over [-0.2, +0.2]

22. Numerical Results • VCs’ performance persistence • Ability or sorting? • Impact of ability signal A • Can VCs deceive the learning Es via systemically biased ability signal ? • What if the ability signal is largely noise? • Impact of VCs’ share decision s • How do Es response to VCs’ share decision s? • What are the impact of s on the market performance? • Impact of static and dynamic VC strategies • What are the effects of the demand/supply balance , and the search cost?

23. Performance Metrics • Consider mean performance of 3 VC ability groupshigh (3), middle (4), low (3) • Market share % of total payoff V • VC payoff • Track record R for Es • Capacity Utilization % • Project quality (funded) • Efficiency loss %: total payoff gap between actual match and perfect match

24. VC performance persistence • Is the superior performance solely due to VCs' ability, sorting, or both? • Two cases, with static VC ownership share s= 10% • Random Es: disregard both ability signal A and track record R • Learning Es: make choice base on class k, A and R

25. VC performance persistence • Learning Es’ behavior: low class k=1, high class k=4

26. VC performance persistence • Summary

27. VC performance persistence

28. Impact of the ability signal A • Can VCs deceive the learning Es via systemically biased ability signal A? • What if the noise is larger than the signal itself? • Experiment: • Learning Es, static share s=10% for all VCs • Information: • only ability signal A available • track record R is not publicly disclosed • Baseline v.s. reverse signal case • Baseline v.s. huge noise case (noise ~U[-1, 1])

29. Impact of the ability signal A • Can VCs deceive the learning Es via systemically biased ability signal A? • High class Es behavior: baseline vs. reverse signal

30. Impact of the ability signal A • What if the noise is larger than the signal itself? • High class Es behavior: baseline vslarge noise. • Esare confused by extremely noisy signals!

31. Impact of the ability signal A

32. Impact of the ability signal A • The signals mainly serve to identify VCs, linking their IDs to their performance for Es. • The ranking itself has only nominal meaning, and has no real attraction (appeal) to the learning Es. • The learning Es only care about the stationarity of signals. • The learning Escannot be deceived by systematically biased signals. • Noisy signals, however, do confuse the learning Es by blurring the link between VCs’ identity and their performance.

33. Impact of VCs’ share decision s • How do Es’ strategies and market performance change? • VCs: Static shares linearly distributedTop VC’s s=10, 20,.., 90%Fix the bottom VC s=10%Others linear in between • Es: learning • Information: Track record R only

36. Impact of VCs’ share decision s • By increasing ownership share s, top VCs • Increase immediate ownership share of each project • Send out weaker track record signal • The learning Es’ response to the track cord • Herding on different groups of VCs • The payoff of top VCs decreases when s>50% • Herding effect results in low quality pools. • Within each pool, less Esaccept his offers. • Social welfare efficiency loss

37. Static VCs vs Dynamic VCs • Static VCs: all demand the same share 20% • Dynamic VCs: initial 20%, but change based on the myopic local search algorithm • Myopic local search: based on the payoff improvement relative to the forecast, where a and b are the adjustment factors

38. Impact of market size N

39. Impact of search cost (proposal limit m)

40. Summary of the numerical results • The persistence of VCs’ performance is attributable to both VCs’ ability and market sorting. • The signals mainly serve to identify VCs. The ranking itself has only nominal meaning, and has no real attraction (appeal) to the learning Es. • The track record serves both to identify VCs for Es, and to influence the behavior of the learning Es(e.g., via varying ownership share terms). • The learning Es only care about the stationarity of signals. They will not be deceived by systematically biased signals. Noisy signals, however, do confuse the learning Es by blurring the link between VCs’ identity and their performance. • Overly greedy top VCs drive high quality Esto lower ability VCs. This leads to high social welfare efficiency loss. • Bottom VCs benefit most from larger market size; top VCs benefit most from lower search cost.