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UNCERTAINTY AVERSION VS. COMPETENCE: AN EXPERIMENTAL MARKET STUDY Carmela Di Mauro Università di Catania, Italy.

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Presentation on theme: "UNCERTAINTY AVERSION VS. COMPETENCE: AN EXPERIMENTAL MARKET STUDY Carmela Di Mauro Università di Catania, Italy."— Presentation transcript:

1 UNCERTAINTY AVERSION VS. COMPETENCE: AN EXPERIMENTAL MARKET STUDY Carmela Di Mauro Università di Catania, Italy

2 General research question: Does ambiguity aversion arise and persist in markets?

3 Ambiguity effects in markets Theoretical foundations Dow and Werlang (1992) Epstein and Wang (1994) Epstein (2000) Mukerji and Tallon (2001) Mukerji and Tallon (2003)

4 Evidence from experimental markets Camerer and Kunreuther (1989), J. Risk and Uncertainty Sarin and Weber (1993),Management Science Di Mauro and Maffioletti (2004), Applied Economics Di Mauro (2005), under review

5 Making uncertainty operational Ellsberg’s urn Second order probabilities Real event uncertainty Chance processes

6 Source preference [Heath and Tversky (1991), Wakker and Tversky (1995), Tversky and Fox (1995)] Ambiguity attitude is determined not by the fact that the decision maker lacks the knowledge about some aspects of the stochastic structure of a problem, but rather by the fact that one source of uncertainty is preferred over another.

7 The Competence hypothesis [ Heath and Tversky (1991)] Individuals prefer betting on their own judgment over an equiprobable chance event when they consider themselves knowledgeable, but not otherwise. Competence effects are inconsistent with ambiguity aversion because judgmental probabilities are more ambiguous than chance events

8 Evidence on competence effects Heath and Tvesky (1991) Fox and Tversky (1995) Kuehberger and Perner (2003) Keppe and Weber (1995) Kilka and Weber (2000)

9 Specific research questions - Does competence effects or ambiguity aversion persist in the face of market specific discipline? -Are market prices and volumes affected?

10 Market organization Computerised Double Auction market run for 12 periods, plus 2 dry runs each market period lasts 4 minutes 8 traders per session 3 couples of complementary two-outcome assets are traded Both chance based and natural event based bets are traded Uncertainty is solved at the end of each market period Traders are allowed to buy and sell all traded assets Initial endowment: cash + risky position in assets Earnings are based on profits gained in a randomly selected period (average earning €15).

11 Making event uncertainty operational in the experiment The resolution of uncertainty is tied to the realization of natural events about which the decision maker is more or less knowledgeable Choice/valuation of natural events is then contrasted with comparable chance events

12 Experiment 1 E1 (5 sessions) As in Heath and Tversky’s experiment 5, it is assumed that subjects are more knowleadgeable about home, rather than foreign events

13 A – You win 10,000 francs if a white ball has been drawn from an urn containing 10 white and 10 black balls, you win zero otherwise. B – You win 10,000 francs if a black ball has been drawn from an urn containing 10 white and 10 black balls, you win zero otherwise. C - You win 10,000 francs if the maximum temperature in Paris on 10th July 2004 was higher than 20° C (the historical average), you win zero if it was lower. D - You win 10,000 francs if the maximum temperature in Paris on 10th July 2004 was lower than 20° C (the historical average), you win zero if it was higher. E - You win 10,000 francs if the maximum temperature in Missoula (USA) on 10th July 2004 was higher than 20° C (the historical average), you win zero if it was lower. F - You win 10,000 francs if the maximum temperature in Missoula (USA) on 10th July 2004 was lower than 20° C (the historical average), you win zero if it was higher. Example of lotteries traded

14 Pricing predictions No arbitrage – Arbitrage should push the sum of prices of complementary assets to equal the aggregate payoff irrespective of risk and ambiguity attitude. Ambiguity aversion – The sum of prices for complementary ambiguous lotteries is lower than that for chance lotteries. Competence effects – The sum of prices for higher knowledge lotteries exceeds that for chance lotteries, but not otherwise

15 Sum of mean prices – E1 (average over periods) S1*S2*S3S4S5* SUM(chance) 31015 (12226) 21687 (6164) 12923 (3439) 19770 (5918) 22213 (4571) SUM(EU) 21541 (10276) 17964 (4873) 15515 (6526) 15690 (3774) 11891 (6746) SUM(US) 20483 (6408) 11774 (4745) 15314 (6148) 18117 (9122) 13471 (4561) SUM(chance) > SUM(EU): S1, S2, S4, S5 SUM(chance) > SUM(US): S1, S2, S4, S5 SUM(EU) > SUM(US): S1, S2, S3

16 Estimated eq: P t =  +  P t-1 +  t stationary price =  /(1-  ) sessionchanceEUUS 132569.621703.520924.2 223481.818755.112058.4 313341.616169.749084.9 418568.415154.717721 522067.510975.313846.5 Prices for chance bets higher than those for ambiguous bets in four sessions out of five

17 What may explain the different result with respect to Heath and Tversky’s experiment? Maybe traders in experiment 1 consider questions relating to US and EU weather as equally ambiguous.

18 Frequency of preference to bet on own judgement/chance event (%) responseSession 1Session 2Session 3Session 4Session 5 judgement59.446.959.434.446.9 indifferent12.531.318.831.36.3 chance28.121.9 34.446.9 Frequency of preference for complementary bets (%) responseSession 1Session 2Session 3Session 4Session 5 judgement56.346.971.965.659.4 indifferent28.137.512.515.63.1 chance15.6 18.837.5

19 Experiment 2 E2 (5 sessions) Before market trading begins, participants state whether they feel knowledgeable /unknowledgeable with respect to 70 EU and US cities. Only known EU cities and unknown US cities are used to build lotteries traded in subsequent markets

20 Sum of mean prices – E2 S1*S2S3S4S5* SUM (chance) 15241 (5293) 13908 (1760) 13149 (3714) 17637 (4243) 16851 (701) SUM(EU)25220 (14227) 12267 (4702) 13294 (4200) 18471 (8541) 17160 (3309) SUM(US)17862 (11390) 13390 (6406) 10927 (2045) 17755 (4478) 15190 (961) SUM(EU) > SUM(chance): S1, S3, S4, S5 SUM(chance) > SUM(US): S2, S3, S5 SUM(EU) > SUM(US): S1, S2, S3, S5

21 stationary prices sessionchanceEUUS 1151242147515329 2139691415213610 3123401336111620 4155941514014834 5168101734815273 SUM(EU) > SUM(US): S1, S2, S3, S4, S5 SUM(EU) > SUM(chance): S1, S2, S3, S5 SUM(chance) > SUM(US): S2, S3, S4, S5

22 Conclusions and research agenda Evidence of a competence effect in Exp. 2, but need to check its robustness across alternative specification of “knowledge” Judged probabilities are not elicited, so doubts exist whether the natural and chance events are considered equiprobable No convergence to the no arbitrage value is observed –Re-run experiment with experienced subjects –Pre-market tutorial on how to arbitrate

23 Extra slides

24 Experiment 3 Mean sum of prices over periods S1S2S3 SUM (chance) 159191236315279 SUM(UK) 168241217914806 SUM(IT) 162231080014728

25 Proportion of trades (average over periods) SessionchanceEUUS 10.330.350.32 20.470.300.23 30.310.350.34 40.350.370.28 50.400.310.29

26 Frequency of preference for high/low knowledge bets (%) – average over periods responseSession 1Session 2Session 3Session 4Session 5 High kn. 56.353.140.634.437.5 Low kn. 12.521.931.3 50.0 indifferent 31.325.028.134.412.5 Frequency of preference for complementary bets responseSession 1Session 2Session 3Session 4Session 5 High kn. C 59.446.975.062.5 Low kn. C 15.631.39.428.118.8 indifferent25.021.915.69.418.8 Subjects prefer the high knowledge bet in 4 sessions out of 5

27 RANDOM EFFECTS PANEL ESTIMATION DEP.VBL.= SUMPRICE T1 Wald chi-square (3) = 26.75, prob> chi2 =0.00 Rho =.24 COEF (st.err)tP > | t | constant 18096.1(2710)6.680.00 DUMMY CD -4626.5(1303.7)-3.550.00 DUMMY EF -5005 (1339.2)- 3.740.00 VOLUME 216.4 (115.6)1.870.06 DEP.VBL.= SUMPRICE T2 Wald chi-square (3) = 7.16, prob> chi2 =0.067 Rho =.21 constant 18220 (2418.7)7.530.00 DUMMY CD 1906.7 (1152.3)1.650.098 DUMMY EF - 525 (1158.2)-0.450.650 VOLUME -180 (109.9)-1.640.10

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