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Investor Experience Impact: Insights from India's IPO Lotteries

This study explores how investor experience affects risk attitudes and asset choices using IPO lotteries in India, revealing changes in preferences and behaviors. Results show treated investors engaging more in IPOs, trading, and diversifying. Commentators highlight robust data analysis, applicability across age and wealth groups, and the need for further examination of heterogeneity and self-selection effects.

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Investor Experience Impact: Insights from India's IPO Lotteries

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  1. The Effects of Experience on Investor Behavior:Evidence from India's IPO Lotteries Santosh Anagol, VimalBalasubramaniam, TarunRamadorai Discussion by: Sergey Gelman, ICEF, Higher School of Economics, Moscow

  2. Summary (I)

  3. Summary (II) • Classical finance theory assumes that preferences are constant and beliefs are slow changing • New empirical evidence: investor experience seems to change risk attitude • problem: experience is endogenous • This paper shows • Experience changes investor preferences with regard to risk (and the choice of assets) • Quasi-experimental design with experience assignment

  4. Summary (III) • Data & design • 1.5 mln investor accounts; oversubscribed IPOs in India • Share allotment is performed through a lottery • Results • Treated investors are more likely to • Participate in further IPOs • Trade other stocks • Get more diversified • Realize gains • Treatment effect is moderated by “age”, wealth, bid size

  5. Comments (I) • Very convincing results • Impressive data analysis • Thorough robustness checks. Main results hold for • “Age” groups • Wealth groups • Application size groups • Interesting read!

  6. Comments (II) • Needs better explanation: • How does positive IPO experience change risk aversion? • Decreases RA: IPO participation ↑, sector weight ↑, trading ↑ • Increases RA: realization of paper gains ↑, diversification ↑

  7. Comments (III) • Closer look at: experiment heterogeneity and self-selection • “Gamblers” vs. “committed investors”

  8. Comments (III) • Self – selection: average propensity is treated with fixed effects • But what about treatment effect heterogeneity?

  9. Comments (III)

  10. Comments (III) • experiment heterogeneity and self-selection: • Cluster standard errors by experiment or at least share category (may be even use SUR to calculate s.e.)

  11. Comments (III) • Explain heterogeneity: • Pure gambling • Or forced gambling (budget constraints)? • Control for portfolio size, “age” (also include interaction terms)

  12. Comments (IV) • The questionofgeneralisability – investorstakingpart in IPO‘sarepossibly riskier thantradingonly on secondarymarket • Are theresultsdrivenbycrisisyears? Ifyouhaveparticipated in crisis, youwouldhavepossibly ….? Year x treatment?

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