Experimental and Neuro Finance Elena Asparouhova (U Utah) and - PDF document

7/26/12& Experimental and Neuro Finance Elena Asparouhova (U Utah) and Peter Bossaerts (Caltech) Melbourne, July 25, 2012 what we do • We study financial decision making, all the way from the level of markets (“asset pricing”) down to the individual (“behavioral finance”). • We don’t just want to describe (GARCH, Prospect Theory,…); we want to understand! • Why do prices move to levels where only systematic risk is priced? • Why are humans able to track risk – but confused about outliers (black swans)? • Our methodology: Experiments • Observe humans make (financial) decisions and interact in a controlled setting 2 1&

7/26/12& Experiments!? • We eventually want to understand real-world financial markets and the creatures that inhabit them (humans). • Are these markets not “too big” and “too complex” to be studied in the laboratory? • Too complex? That is precisely their problem… They cannot be described in terms of simple equations; so you need control, i.e., laboratory study • Too big! Sure, but we have to start somewhere. Without experimentation, we are "likely to go completely astray into imaginary conjecture” [Hannes Alfven, Nobel (Astro)Physics] 3 Analogy with astrophysics The TORPEX at EPFL, a tiny vacuum- like space where physicists generate “plasma” using a microwave “oven” in order to study physical processes inside stars (that float in a real vacuum). It’s small relative to the real stars, but the only way to be sure that we correctly interpret the “signals” that the stars send to us! 4 2&

7/26/12& More about experiments • Asset pricing theory in particular is not ready for real markets; it is too stylized, it leaves out too many details (taxes, intermediaries, ambiguity about returns, demographics, politics…) • Yet it is ready for laboratory experiments (we hope to convince you of this today): it makes precise predictions about prices and allocations, but still leaves out the details of how to get there • Experiments lead to a deeper understanding of the theory ( Richard Feynman , Nobel Prize Winner) • … and to new theory (in our case: about HOW markets equilibrate) 5 Two examples • The CAPM • Risk prediction in the human brain 6 3&

7/26/12& CAPM • Theory: In equilibrium, the expected return on risky securities is solely determined by their covariance with aggregate risk (“beta”) • Equivalent: the “market portfolio” will give you maximum expected reward for its risk (risk=return variance), or the Sharpe ratio of the market portfolio is maximal • Sharpe ratio = Expected return on portfolio minus riskfree rate / return standard deviation 7 Lab • Anywhere from 20 to 30 subjects in the lab (up to 70 if participating from home). • The participants are given initial allocations of securities (called, say, A, B and Notes) and cash. • Initial allocations are risky and different for different subjects, and they can be improved upon through trading (with other subjects). 8 4&

7/26/12& LAB • Each laboratory session is a repetition of the same situation, and each repetition is called a period. 6-10 periods in a session. • Period=Initial endowments, trading, final portfolios, payoff. • Example: • Three markets (one riskfree, shortsales allowed), several pe- riods. • Three states; determine liquidation value at end of each pe- riod; known probabilities. Security State X Y Z A 170 370 150 B 160 190 250 Notes 100 100 100 9 LAB • Endowment of risky securities and cash, refreshed every pe- riod. E.g., 5 of A, 4 of B, and 400 cash (may vary across subjects). • Loan repayment of, say, 1900 at end of period (leverage!). • Trade through a web-based open book system, Marketscape , developed at Caltech. Example Of This Experiment: 011126 Draw Subject Signup Endowments Cash Loan Exchange Type Type Reward A B Notes Rate (#) (franc) (franc) (franc) $/franc D 18 125 5 4 0 400 2200 0.04 18 125 2 8 0 400 2310 0.04 5&

7/26/12& Logout Exit Market Marketplace closes in 01:56:25 Navigation Markets -- Actual Cash on hand: $500.0 Marketplace A B Notes A (5) B (4) Notes (0) View Transaction Table Item: A Item: B Item: Notes Order Form Order Type: ������ Price: 195.00 � ���� �� � Market: A ������������ Price: $195.00 ����������� Units: 1 �������������� ����� Total Value: $195.00 Messaging Messaging Received Messages mc : Markets will be called in 10 minutes mc : Markets will be called in 9 minutes mc : Markets will be called in 8 minutes mc : Markets will be called in 7 minutes Message History 11 ��������������� ��������������� ������������������������������ ����������������������������� CAPM PREDICTIONS • The expected returns of each of securities A and B are positively related to the betas of the securities • The market portfolio is mean-variance efficient. • The final portfolio of each individual should have risky securities in the same proportion as in the market portfolio. 12 6&

7/26/12& Prices • Prices of the two securities are virtually the same. • Expected payoff of A is higher, i.e., expected return of A is higher. • Precisely because Beta(A)>Beta(B) 13 Efficiency of market portfolio • Compute Sharpe ratio of market portfolio with each transaction. • Take the difference between Sharpe of the market and the highest Sharpe ratio. • CAPM predicts this difference should be 0. 14 7&

7/26/12& INDIVIDUAL Behavior • For each of the 8 periods plot each 1 subject’s final holdings of asset A. 0.8 • Red dot indicates the 0.6 proportion proportion of A in the market portfolio. 0.4 • Individuals are all 0.2 over the place. Allocations do not 0 improve with time. 1 2 3 4 5 6 7 8 period 15 2. Risk prediction in the human brain • How does the human brain process risk? • Specifically, what are the computational algorithms that the brain uses? • How general are these (algorithms)? Any potential flaws (that would explain cognitive biases)? 16 8&

7/26/12& On cognitive biases • The human brain is a remarkably adapted computational device • But it does not do everything right • Example • Knowing whether you are level when flying through clouds • Pilots need instrument rating to get permission to fly through clouds) • Important: there is NO WAY to teach the brain to do this right! You need instruments 17 a simple gamble • With each card, update: • EXPECTED REWARD • RISK 18 9&

7/26/12& Risk and reward • Expected reward increases linearly • Risk (reward variance) is quadratic 19 Functional magnetic resonance imaging (fMRI) • Subjects play game repeatedly while brain is being scanned in fMRI • Measures brain activation indirectly (through blood flow) 20 10&

7/26/12& Neural activation correlating with risk • fMRI signal per level of reward probability; averaged across subjects • Activation AFTER seeing first card 21 Anterior insula • Known to “convert” emotions into “feelings” • (Ability to sense heartbeat ~ size) • Related to self-awareness • Activates especially in reaction to disgust, pain Cool mathematics in a quintessentially emotional • Involved in empathy part of the human brain! 22 11&

7/26/12& Neural activation correlating with risk prediction error • Traditional reward prediction error: reward minus expected reward • Risk = expectation of size of reward prediction error • Risk prediction error 23 fMRI evidence Risk prediction error after first and second card. No risk, and hence, no risk prediction error if first card is 1 or 10 24 12&

7/26/12& Effectively, brain signals whether there are outliers • Is size of realized reward prediction error bigger than expected? • If so, things may have changed… • Regime shifts • This, however, is not always the right interpretation! Black swans are not unusual… • Does the brain distinguish? 25 Pupil dilation • Pupil dilation • … correlates with risk prediction error • There is a link with phasic changes in norepinephrine levels • Could study this pharmacologically (propranolol?) 26 13&

7/26/12& Final remarks • We are bringing finance to the laboratory, in order to distinguish what is intrinsically wrong from what is wrong because we don’t know the parameters. • We have seen some fundamentals concepts in asset pricing theory at work, like CAPM. What we learned: the system has its own laws, different from the individuals • We started exploring things where the theory is not quite right • We have seen excessive volatility, but it does not affect allocations (welfare)! • We are beginning to understand how markets equilibrate • We have started to explore the neurobiological foundations of cognitive biases – the brain uses specific algorithms to track the environment, and these may not always be well adapted to financial markets • Implementation of the algorithms often engages emotions – emotions are part of “reasoned” decision making 27 14&