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Determinants of Aggressive Bidding in the Buying a Company Task Andy Lockett - School of Business, University of Nottingham Elke Renner - School of Economics,

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Presentation on theme: "Determinants of Aggressive Bidding in the Buying a Company Task Andy Lockett - School of Business, University of Nottingham Elke Renner - School of Economics,"— Presentation transcript:

1 Determinants of Aggressive Bidding in the Buying a Company Task Andy Lockett - School of Business, University of Nottingham Elke Renner - School of Economics, University of Nottingham Martin Sefton - School of Economics, University of Nottingham Deniz Ucbasaran - School of Business, University of Nottingham

2 Winners Curse In general, when bidding for an object of unknown value the person who most overestimates true value most likely to be winner … Winning bidder often makes expected losses n Auctions (e.g. Oil field drilling rights) n Acquisitions n Start-ups

3 Buying a Company (Samuelson and Bazerman, 1985) Company worth v to current owner and 3v/2 to potential buyer v uniformly distributed on [0, 100], observed by owner Potential buyer makes bid, b Current owner accepts Owner receives b - v Buyer receives 3v/2 - b or rejects Owner receives 0 Buyer receives 0

4 Optimal bid Owner: accepts if b v, rejects otherwise Buyer: expected profit = E[3v/2 – b|b v] Pr{b v} Optimal bid: b* = 0

5 Samuelson and Bazerman (Research in Experimental Economics, 1985). Subjects generally bid between E(v) = 50 and E(3v/2) = 75. Ball, Bazerman and Carroll (Organizational Behavior and Human Decision Processes, 1991). Learning from own outcome over multiple trials does not reduce winners curse. Holt and Sherman (American Economic Review, 1994). Vary distribution of v and find data consistent with a model of naiive bidding: maximize E[3v/2 – b] Pr{b v}. Charness and Levin (2005). Frame task as an individual decision problem, vary complexity of distribution of v, measure mathematical sophistication. Winners Curse survives, is lower with less complex distributions, lower for more mathematically sophisticated. Experimental Evidence

6 Selten, Abbink and Cox (Experimental Economics, 2005): Frame task as individual decision problem, vary lower bound of uniform distribution (u), focus on learning. Experimental Evidence

7 Goal of this experiment 1. Can winners curse be reduced by learning from others? n We compare bids made by individuals with bids made by pairs who can discuss problem n We study effect of giving bidders information about others outcomes 2. How are bids related to individual characteristics? n We elicit risk attitudes, measure mathematical sophistication, demographic data.

8 The Experiment 178 Subjects from University of Nottingham School of Business Each Session: Buying a Company Eliciting Risk Attitudes Assessing Probabilities Questionnaire 4 Sessions: INDIVIDUAL: 27 subjects TEAM: 25 x 2 subjects INDIVIDUAL + INFO: 39 subjects TEAM + INFO: 31 x 2 subjects Average earnings £ for sessions lasting approx. one hour

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11 Notes: This is one of the parameterizations used in Selten, Abbink, Cox (2005) We also had subjects make decisions for their other parameterizations: Task B (same as A but chips 11-99) Task C (same, but chips 21-99) Task A: optimal bid = 1 Task B: optimal bid = 22 Task C: optimal bid = 42 All three tasks played out at end of experiment

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13 Notes: This is similar to risk elicitation procedure used in Dohmen, Falk, Huffman, Sunde, Schupp and Wagner (2005) Switching point gives measure of risk attitude Switching point = amount up to which subject chooses lottery (e.g. 2.5 means subject chose lottery when safe option is 2.5 or less, then chooses safe option when safe option 3 or more)

14 INDIVIDUALTEAM Frequency Switching Point No significant difference between INDIVIDUAL and TEAM (p=0.559) 68 risk averse decisions, 47 consistent with risk neutrality (2.5 or 3), 4 risk loving decisions, 3 inconsistent decisions (not shown)

15 Based on INDIVIDUAL data: Male (n = 49) average switchpoint = 2.21 Female (n = 17) average switchpoint = 1.74 Women more risk averse than men (Fisher exact test: p = 0.026)

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18 Notes: This is same as used in Charness and Levin (2005) INDIVIDUALTEAM Frequency # correct (part III) Significant difference between INDIVIDUAL and TEAM (p=0.007) Based on INDIVIDUAL data, males score higher than females (1 vs. 0.59) but difference marginally significant (p = 0.13)

19 Histograms of Bids: INDIVIDUAL (n=27) Task A Task B av = 40 s.d. = 26 av = 45 s.d. = 21 Task C av = 57 s.d. = 20

20 Statistical Comparisons 1. Bids increase significantly across tasks as predicted: bidA < bidB (signrank p =0.001) bidB < bidC (p=0.000) 2. Significant over-bidding. Median bid significantly above predicted bid: Median Bid A = 40 (signtest p=0.000) Bid B = 45 (p=0.000) Bid C = 58 (p=0.001)

21 Histograms of Bids: TEAM Task A Task B av = 37 s.d. = 20 Task C av = 44 s.d. = 20 av = 53 s.d. = 21 Histograms of Bids: TEAM (n=25)

22 Statistical Comparisons 1. Bids increase significantly across tasks as predicted: bidA < bidB (signrank p =0.000) bidB < bidC (p=0.000) 2. Significant over-bidding. Median bid significantly above predicted bid: Median Bid A = 37 (signtest p=0.000) Bid B = 48 (p=0.001) Bid C = 53 (p=0.011) 3. No significant differences between INDIVIDUAL and TEAM: Bid A: INDIVIDUAL = TEAM (ranksum p=0.8185) Bid B: INDIVIDUAL = TEAM (p=0.9051) Bid C: INDIVIDUAL = TEAM (p=0.3540)

23 + INFO Sessions Subjects given selective information from earlier sessions: n INDIVIDUAL + INFO: 19 subjects given negative information, 20 given positive information n TEAM + INFO: 15 teams given negative information, 16 given positive information

24 Positive Information

25 Negative Information

26 Summary of Bids

27 Statistical Comparisons INDIVIDUAL: No statistical effect: bidA (Kruskal-Wallis p =0.590), bidB (p = 0.491), bidC (p=0.274) TEAM: only bid C significantly differs across information conditions: bidA (p = 0.264), bidB (p = 0.346), bidC (p = 0.038) bid C: negative info leads to higher bid than no info

28 Determinants of Bids - INDIVIDUALS Dependant Variable Bid ABid BBid C Cons32.21***(10.90)30.89***(9.13)50.82***(9.37) PosInfo7.92(7.44)7.32(6.23)-4.57(6.40) NegInfo6.91(7.31)7.10(6.12)2.81(6.29) RISK2.81(4.19)6.34*(3.51)2.07(3.60) MATH-4.56(3.24)-2.97(2.73)0.01(2.80) FEMALE18.23**(7.38)12.68**(6.18)5.86(6.34)

29 Preliminary Conclusions n Robust Winners Curse – individuals systematically overbid n Two Heads not Better than One – teams make statistically indistinguishable bids n No Information effect – individuals not affected by pos/neg info n Risk attitudes have small and insignificant effect on bids n Mathematical sophistication has small and insignificant effect on bids n Females bid more aggressively


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