The relationship between Psychology and Economics: The case of Behavioural Finance Dr. Guillermo Campitelli

– Similarities: Adam Smith: – The Theory of Moral Sentiments (1759) – An Inquiry into the Nature and Causes of the Wealth of Nations (1776). James Mill: – Elements of Political Economy (1821) – An Analysis of the Phenomena of the Human Mind (1829) Psychology: study of behaviour > economic behaviour Economics: allocation of resources > cognitive resources Psychology and Economics

– Differences: Economics became more mathematical Development of mathematical models Testing models with public data Psychology became more experimental Creation of artificial conditions in a lab Control of variables Recruitment of participants Psychology and Economics

– Herbert Simon (Nobel Prize in Economics, 1978) Substantive rationality vs. Bounded rationality Limitations of the cognitive system Study of cognitive processes leading to decisions – Daniel Kahneman (Nobel Prize in Economics, 2002) Heuristics and biases in judgements and decisions Prospect Theory vs. Expected Utility Theory Risk aversion for losses Psychology and Economics

– Study of financial issues experimentally – Incorporation of psychological knowledge onto financial models – Assumption of economic agents with limited capacities and with bounded rationality Psychology and Finance: Behavioural Finance

– Decisions of individuals violate principles of economic theory (Thaler, 1999) Change of default values modify decisions Psychology and Finance: Behavioural Finance

– People judge their skills, performance, knowledge or probability of success higher than the objective probabilities (e.g., Lichtenstein, Fischhoff & Phillips, 1982) – Example:  Which of the following cities has the larger population: Sydney or Melbourne?  How confident are you on your choice? (50% - 100%) – Hard-Easy Effect: overconfidence in difficult items and underconfidence in easy items Behavioural Finance: Overconfidence

– Traders learn about their ability inferring it from their successes and failures. – In assessing their ability traders take too much credit for their successes. This leads them to become overconfident. – Trader's overconfidence increases in the early stages of their career. Overconfidence: Gervais and Odean’s (2001) model

– In this model, the most overconfident and non-rational traders are not the poorest traders. – For any given level of learning bias and trading experience, it is successful traders, though not necessarily the most successful traders, who are the most overconfident. – Overconfidence does not make traders wealthy, but the process of becoming wealthy can make traders overconfident. Overconfidence: Gervais and Odean’s (2001) model

– One criticism of models of non-rational behaviour is that non-rational traders will underperform rational traders and eventually be driven to the margins of markets, if not out of them altogether [Alchian (1950), Friedman (1953), and more recently, Blume and Easley (1982, 1992), Luo (1998)]. – De Long et al. (1990) present a model in which non- rational traders earn higher expected profits than rational traders. Overconfidence: Gervais and Odean’s (2001) model

Traders updated belief about having high ability with learning bias γ Probability of having high ability at time t at an s number of successful judgments Initial probability of having low ability Initial probability of having high ability Traders updated expected ability Overconfidence: Gervais and Odean’s (2001) model

Optimistic overconfidence (Miller & Ross, 1975) Human tendency to wishful thinking and self-enhancement Confirmation bias (Koriat, Lichtenstein & Fischhoff, 1980; Yates, Lee, Sieck, Choi & Price, 2002) Human tendency to search for evidence that supports own hypotheses Case-based judgement (Griffin & Tversky, 1992) Assessment of a current case ignoring class issues: overconfidence occurs when the strength of evidence is high and weight of evidence is low Overconfidence: Psychological models

Ecological model (Gigerenzer et al., 1991) Good calibration to ecological probabilities, not to the ones artificially created in the laboratory Random error model (Erev, Wallsten & Budescu, 1994) Generally good calibration with random error (but not systematic error or bias) Regression to prior belief (Moore & Healy, 2008) Bayesian updating of prior belief: overconfidence when prior belief is too high, and underconfidence when prior belief is too low Overconfidence: Psychological models

Moore and Healy’s (2008) model of overestimation Test takers ( i ) possess a prior belief (X i ) on how they would perform in a test. – This belief is unbiased with random error. Performing a test gives the test takers a signal (y i ) on how well they performed. – This signal is unbiased with random error. Test takers update their belief after taking the test

X i = S + L i Prior belief about own performance in a future test Participant’s (i) prior belief in performance in a future test S is the global average score in the test (proxy for overall simplicity of the test), with mean mS and variance vS L i is i’s idiosyncratic performance. Assumption: mean zero and variance vL Moore and Healy’s (2008) model

E[X i |y i ] = αmS + (1- α)y i Updated belief about own performance after taking a test Participant’s (i) updated belief about own performance in a recently taken test (before knowing score) given signal yi y i is a realization of the random variable Y i = Xi + E i, where E i is a zero-mean random error term (variance vE) that represents the imperfections of i’s signal α = (vL + vE ) /(vs + vL + vE ) Moore and Healy’s (2008) model

mS < E[X i |y i ] < x i Model’s prediction i’s expectation of his/her own score after observing his/her signal (yi) is that his/her score lies between the prior mean (mS) and the observed signal (yi). Assuming yi is unbiased, on average, yi = xi If test is easier than expected: underestimation prior mean expected score true score Moore and Healy’s (2008) model If test is more difficult than expected: overestimation mS > E[X i |y i ] > x i

Guillermo Campitelli (Edith Cowan Univeristy, Australia) Merim Bilalic (University of Tübingen, Germany) Guillermo Macbeth (Universidad del Salvador, Argentina) Paper presented at Australasian Mathematical Psychology Conference Melbourne, February 18th 2011 Experimental study on overconfidence: Past feedback and anticipation of feedback in judgements about own performance

Types of judgements prediction evaluation J T J T task judgment

JT J Confidence and frequency judgments about own performance CONFIDENCE JUDGMENTS FREQUENCY JUDGMENTS TTTTTJJJJ

Investigate the role of feedback on performance judgments Goal of the study Internal feedback (performance) External feedback (referee) score signal actual score evaluation prediction anticipation of feedback

How judgments (beliefs) are updated given feedback (internal or external) Internal feedback (performance) External feedback (referee) score signal actual score evaluationprediction Moore and Healy’s (2008) model of overestimation Gervais and Odean’s (2001) model of overconfidence Moore and Healy’s (2008) ?

40 psychology students from Universidad Abierta Interamericana (Buenos Aires, Argentina). Methods: Participants

Performance judgments in a general knowledge test (maths and language) Measurement of prior beliefs about performance in future tests: – a general knowledge test similar to the one performed earlier. – a domain-specific test (psychology exam). Updated belief about future performance after receiving feedback in a previous general-knowledge test. Updated belief about performance after taking tests. Updated belief about past performance in anticipation of immediate performance feedback Methods: Measures

E[X i |y i ] = αmS + (1- α)y i Updated belief about own performance after taking a test Participant’s (i) updated belief about own performance in a recently taken test (before knowing score) given signal yi y i is a realization of the random variable Y i = Xi + E i, where E i is a zero-mean random error term (variance vE) that represents the imperfections of i’s signal α = (vL + vE ) /(vs + vL + vE ) Moore and Healy’s (2008) model

J1J2J3 J1J2J3J4 J1J2J3J4J5J6 W0 W+1 W+2... W+6... W+6(+2D) T T T F F F

J1J2J3 J1J2J3J4 J1J2J3J4J5J6 W0 W+1 W+2... W+6... W+6(+2D) T T T F F F

Results- Performance judgments in task 1 No anticipation of feedback effect overestimation

p =.068 past feedback effecttask effect? Results- Performance judgments in task 2 JUDGEMENT IN TASK 1

FT p =.001 p =.003 p =.592p =.083 p =.046p =.061 p =.110 P <.001 F no past feedback effect no task effect immediacy of feedback effect overconfidence Results- Performance judgments in task 3 JUDGEMENT IN TASK 1

W0 W+1 W+2... W+6... W+6(+2D) T T T >.59 > >.37? > <.67? <.54 Moore & Healy (2008) OK ?

W0 W+1 W+2... W+6... W+6(+2D) T T T >.50 > >.63 >.52 Moore & Healy (2008) X Moore & Healy (2008) ? X

Moore & Healy’s (2008) model of overestimation had some support in our exam test. The model did not predict well updating of belief due to a previous task feedback. Discussion

The lack of update of belief due to feedback in the exam suggests that in some situations long-term beliefs are not easily modified by new information Discussion

Overconfidence is learned but not with a learning bias Internal feedback is unbiased Prior belief tends to be too high due to biased external feedback External feedback is biased due to the fact that we try to avoid environments with negative feedback Overconfidence in domains where we received positive feedback and underconfidence in domains where we receive negative feedback. Current feedback would not change much our judgement on domains in which we are experts. Proposal

Source of prior belief (prediction) J T F B TT F T BBBBB LONG-TERM BELIEF OF PERFORMANCE IN A DOMAIN BIASED UUUUU TJ Subjective Domain Expertise

We propose, as Gervais and Odean (2001) that overconfidence is learned. Instead of assuming a learning bias, we propose an external feedback bias We propose, as Moore and Healy (2008) that prior beliefs play an important role on judgments about own performance. Summary