Trading Under Uncertainty

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Trading Under Uncertainty Ankur Pareek Yale School of Management

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Trading Under Uncertainty. Ankur Pareek Yale School of Management. Motivation. To study the interaction between arbitrageurs and uninformed investors and measure the ex-ante allocation efficiency of the market. - PowerPoint PPT Presentation

Transcript of Trading Under Uncertainty

Page 1: Trading Under Uncertainty

Trading Under Uncertainty

Ankur Pareek

Yale School of Management

Page 2: Trading Under Uncertainty

Motivation

• To study the interaction between arbitrageurs and uninformed investors and measure the ex-ante allocation efficiency of the market.

• Provide some insight into validity of some of the theories behind the dotcom bubble: – Ofek and Richardson(2003)- short sales restrictions with

heterogeneous beliefs explain the internet stock bubble– Pastor and Veronesi (2006)- high uncertainty in future earnings

growth rate explained the existing prices of tech stocks in late 90s– Lamont and Stein (2004) – less arbitrage capital for short-selling in

rising overvalued market

• Understanding the behavior of arbitrageurs under uncertainty

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Experimental Design

• Create market for stocks of a technology firm Sysco which is based on single technology still in R&D phase

• Three sets of traders with different information sets– Arbitrageurs with perfect information about the final dividend

realization– Traders with partial/noisy information about the final dividend.– Uninformed traders

• Arbitrageurs faced with uncertainty about when the arbitrage window will close (end of period 4 or period 5)

• Three sessions with 4 or 5 periods which vary in final dividend and signal received by partially informed traders.

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Time 0No information

Time 14 traders givenNoisy info

Time 24 traders givenPerfect info

Time 3Public announcementNoisy info.announced

Time 4 or 5Dividend paidand trading ends

Timeline for a Trading Session

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Experimental Results

• Prices did not converge to fundamental values when there was overvaluation in the market in session 1 and session 3– Consistent with Ofek and Richardson (2003): short sell

constraints and heterogeneous beliefs

• Traders with noisy private information trade on it aggressively immediately after receiving it but don’t trade on it or reverse some of their trades later

• Prices converge close to fundamental value when dividend is high

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Experimental Results (contd’)

• Arbitrageurs did not sell all their securities before the end of period 4 in low dividend sessions 1 and 3– Action inconsistent with risk aversion/ risk neutrality of

arbitrageurs.– Can be explained by risk loving preferences like prospect theory

with convex utility over losses w.r.t some benchmark target profit

• Arbitrageurs did not indulge in speculative behavior in most of the cases.

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S toc k P ric es and E ffic ienc y of Alloc ation for S es s ion 2

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1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77

T rade number

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ce/P

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TransactionP rice

E quilibriumPrice

AllocationEfficiency

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S toc k P ric es and E ffic ienc y of Alloc ation for S es s ion 3

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1 5 9 13 17 21 25 29 33 37 41 45 49 53 57

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  Informed Partial info. Uninformed

Initial Stocks 20 20 35

Final Stocks 7 29 39

Aggregate Profit 8600 1255 5145

Profit/Trader 2150 313.75 735.00

Exante Exp Profit 3400 -280 360

Expected number of stocks 0 31 44

  Informed Partial info. Uninformed

Initial Stocks 20 20 35

Final Stocks 30 15 30

Aggregate Profit 15291 17513 27196

Profit/Trader 3822.75 4378.25 3885.14

Exante max Exp Profit 5525 3400 3075

Expected number of stocks 75 0 0

Session 1 summary statistics

Session 2 summary statistics

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  Informed Partial info. Uninformed

Initial Stocks 20 20 35

Final Stocks 17 14 44

Aggregate Profit 5200 6500 3531

Profit/Trader 1300.00 1625.00 504.43

Exante max Exp Profit 2047.22 866.67 477.78

Expected number of stocks 0 24 51

Session 3 summary statistics

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Conclusion

• Heterogeneous investors combined with short-sales constraints could lead to persistence of overpricing.

• Perfectly informed arbitrageurs more risk-loving compared to investors with noisy information sets.– Investors with partial information risk-averse as shown by their

trading behavior.– Arbitrageurs risk taking in final period can only be justified by

risk-loving behavior

• Difficult for under pricing to persist in a market with arbitrageurs with perfect information.

• Future experiments could help in resolving the debates about the existence and reasons behind the dot-com bubble of 1990s.