Experimental Finance

1. Experimental Finance Behavioral Finance Week 5 Read Muradoglu, 2001 Muradoglu et.al. 2005

2. Why Experimental Methodology? • Limitations of Share Price Data • Controlled Design

3. Muradoglu,2001 • Motivation • Efficient Markets Hypothesis, Fama • Overreaction Hypothesis, DeBondt and Thaler • Experimental work by DeBondt, 1993 • If investors are positive feedback traders, they will expect past trends to continue in the future • Anchors used will be determined by past price changes and past price levels • Confidence interval assessments will not be symmetric

4. Limitations of DeBondt,1993 • DeBondt experiments • conducted by student subjects • “… an acceptable proxy for the typical investor?” • quasi experimental design • “…does not control for other factors than past price” • forecasts of various stock indexes and FX • real time forecast of specific stocks? • Short term forecast horizons

5. Purpose of Muradoglu,2001 • To investigate if return expectations and risk perceptions of investors are adoptive? • If so, what is the expectation formation process and hedging behaviour? • Is it similar for • stock market professionals versus novices • real-time stock price forecasts versus • real-time stock index forecasts • unknown calendar time, unnamed stock forecasts • different forecast horizons

6. Research Design and Procedure • Subjects • Student subjects, 45 • 19 MBA, 26 undergraduates • exposed to EMH and financial forecasting • Professionals in stock market, 35 • all licensed brokers • working for brokerage houses • 15 prepare research reports • 20 managing funds and giving advice

7. Research Design and Procedure • Folder for response forms • Info about the study • Price series for unnamed stocks • in graphical and tabular form • Response sheets for unnamed stocks • Response sheets for real-time forecasts • stock index • eight stocks of respondents’ choice • Questionnaire

8. Research Design and Procedure • Task • Give point and interval forecasts • I estimate the Friday closing price, one week from now as...............................................pence • The probability that the Friday closing price one week from now is greater than..........pence is 10%. • The probability that the Friday closing price one week from now is less than...............pence is 10%. • For forecasting prices of • unnamed stocks, stock index,specific stocks • For forecast horizons of • one, two, four and twelve weeks (Long Term?)

9. Measurement • Expected price change • EPCi is the difference between the subject's (k) point forecast of a stock (j) for a forecast horizon of (i=1,2,4,12) weeks (Fijk) and the last known price level (P0) • EPCi = Fijk- P0 • The average EPCi is calculated as EPCi =jkEPCijk • DeBondt findings indicated • EPC i, bull 0 • EPC i, bear< 0 • EPC i, bull EPC i, bear

10. Measurement • Risk Perceptions • Confidence intervals • UCIijk = Hijk – Fijk • LCIijk = Lijk – Fijk • Mean Skewness • Si = jk (UCIijk - LCIijk) • DeBondt Findings indicated • S i, bull <0, S i, bear >0 • S i, bull < S i, bear

11. Tests for differences • Expected price changes and skewness coefficients are normalised by dividing to matching standard deviations • t-statistics used for differences in means • comparisons of • bull versus bear markets • unnamed stocks, versus index, actual stocks • experts versus novices • LT versus ST forecast horizons

12. Results • Extrapolate the series and hedge forecasts • EPC i, bull 0, EPC i, bear< 0 , EPC i, bull EPC i, bear • S i, bull <0, S i, bear >0, S i, bull < S i, bear • Experts behave like this for • unnamed stocks and unknown calendar time • short forecast horizons of 1,2,4 weeks • real time index forecasts • short horizons of 1, 2 weeks • Experts are optimistic otherwise! • Novices are optimistic!

13. Muradoglu,2001 Bull Market Bear Market For unknown stocks and short forecast horizons

14. Results • Immaculate Optimism • EPC i, bull 0, EPC i, bear >0 , EPC i, bull EPC i, bear • S i, bull >0, S i, bear >0, S i, bull > S i, bear • Experts are optimistic for • Long horizons in forecasts of • unnamed stocks, Index, Specific stocks • Novices are optimistic for • All forecast horizons for • real time forecasts of Index and specific stocks • unknown stocks - insignificant (?)

15. Muradoglu, 2001 Bull Market Bear Market Immaculate Optimism!!!

16. Results • Hedging Speculations • Trend followers in bull markets have positive but smaller skewness coefficients than contrarians • for short horizons of • 2 weeks for index - experts • 1 week for specific stocks - novices • Trend followers in bear markets have positive and larger skewness coefficients than contrarians • for long horisons of • 4, 12 weeks for unnamed stocks - experts • 12 weeks for index - novices

17. Muradoglu, 2001 Bull Market Bear Market Trend Followers versus Contrarians

18. Results • Experts versus novices • stocks traded at the stock exchange • Bear market EPC of experts < EPC novices • Bull market skewness of experts > skewness novices • Experts more optimistic in price reversals in bear markets • and hedge better on the continuation of a bullish trend • May be one reason for high volatility in the market ? • Maybe anchor for adjustment is the last price, NOT the price change ?

19. Muradoglu, 2001 Bull Market Bear Market Novices Experts Novices versus Experts

20. Results • Different forecast horizons • For unknown stocks • EPC is higher for longer horizons • S is higher for longer horizons • For index • In bull market EPC is lower for longer horizons • In bear market EPC is higher for longer horizons • In bear market S is lower for longer horizons • For stocks traded at the exchange • EPC is higher for longer horizons • S is higher for longer horizons

21. Discussions • Results are different from DeBondt mainly due to • the presence of contextual information • the trends in the stock market • participants level of expertise • forecast horizon

22. Discussions • Real-time, real-task forecasting behaviour is different! • Many factors involved • Task complexity increases exponentially • Sometimes not possible to duplicate in experimental setting

23. Discussions • Immaculate optimism • Subjects extrapolate bullish trends and expect price reversals in bearish trends • Optimists exaggerate their talents! • Underestimate likelihood of bad outcomes! • Optimism accompanied by overconfidence! • Source of high volatility (?) • Source of various inefficiencies (?) • Due to selection bias? - Optimism again!

24. Discussions • Different decision-making processes may be at work at different occasions! • Actual heuristic might be • price change? Unnamed stocks? • the last observation? Bull markets? • long term mean? Bear markets?

25. Discussions • Behavioural assumptions of the EMH must be treated with caution! • Variations in risk premia should not only be explained by traditional risk measures! • Risk perceptions might differ across ….

26. Discussions • Melding psychological and financial research is necessary for a better understanding of financial markets! • Financial Theory must be based on more realistic assumptions of human behaviour! • Further research ?

27. Muradoglu, et.al. 2005 • Motivation • Morkowitz, 1959 • mean - variance efficient portfolios • estimations of expected risk and return from past returns • expectation formation process is assumed to be rational • We use subjective forecasts of investors to represent • expected prices • and related variance - covariance matrix.

28. Muradoglu, et.al. 2005 • Purpose: • To investigate the portfolio performance of subjective forecasts given in different forms • expectation formation process is based on subjective forecasts rather than past prices and • human behavior is integrated into financial modeling. • Performance compared to that of the standard approach of time series data.

29. Muradoglu, et.al. 2005 • Contributions-1 • Literature on forecasting studies focus on • accuracy; • Yates et.al. 1991 • Muradoglu and Onkal, 1994 • biases • Muradoglu, 2002 • De Bondt, 1993 • Andreassen, 1990 • We focus on portfolio performance

30. Muradoglu, et.al. 2005 • Contributions-2 • Port folio performance studies focus on • export managed funds • Ippolito, 1989 • standard tests of market efficiency • Fama, 1991 • We focus on subjective forecasts of experts • we investigate expert subjects revealing judgement in different formats • findings robust to task format.

31. Muradoglu, et.al. 2005 • Research Design • 31 experts working for bank affiliated brokerage houses. • Reached at company - paid 20 hours training programs. • All licensed as brokers • Managing funds • giving investment advice to corporate and private clients • preparing research reports • No monetary/non monetary bonuses offered • An opportunity to forecast stock prices and reveal uncertainty in different formats.

32. Muradoglu, et.al. 2005 • Procedure • Participants were given a folder containing three forms: • Information about purpose of study • Response sheets for real time forecasts • Questionnaire about participants’ experience • in stock market trading, • its duration and • information sources utilized.

33. Muradoglu, et.al. 2005 • Response forms • Same as you have • Task was defined as giving • point forecasts • interval forecasts • probabilistic forecasts • For • a horizon of one week • 25 compromises listed as ISE • highest volume of trade during previous years • easy to follow, reduces task complexity

34. Muradoglu, et.al. 2005 • Method • We estimate the efficient frontier • using three sets of data • representing three sets of expectation formation processes. • “Historical Efficient Frontier” • Historical distribution of stock returns • “Best estimate efficient Frontier” • point and interval estimates of experts. • “Probabilistic Efficient Frontier” • probabilistic forecasts of experts

35. Historical Efficient Frontier Min 2(RH) subject to E(RH) = K where • 2(RH) the variance • E(RH) mean of the historical values of the stock portfolios • K different levels of the mean • R' is the (1XN)row vector of expected returns, • W is (NX1) column vector of weights held in each asset • sum of weights add up to one • and negative weights are not allowed, •  is the (NXN) variance-covariance matrix • Expected returns and variance-covariance matrix • calculated using the last 24 weeks Friday closing prices

36. Best Estimate Efficient Frontier Min 2(RB) subject to E(RB) = K • 2(RB) and E(RB) are calculated from point and interval forecasts as: • UIFijt is the price level for which forecaster j assigns a 2.5% probability that the actual price of stock i will turn out higher, • LIFijt is the price level for which forecaster j assigns a 2.5 % probability that the actual price of stock i will turn out to be lower than her/his time t price estimate. • The experiment is designed such that the above distance corresponds to the two standard deviations assuming that the distribution of returns implied by forecasters is normal. • Off-diagonal covariance terms are calculated from historical returns

37. Consensus Best Estimate Efficient Frontier In the consensus forecast expected return E(Ri) and variances (ii) are calculated as follows:

38. Probabilistic Efficient Frontier Min 2(RP) subject to E(RP) = K • 2(Rp) and E(Rp) are the variance and mean calculated from the probabilistic forecasts • it is difficult to assume normality of distributions revealed by each forecaster • therefore we decided to form a consensus distribution by averaging the probabilities assigned to each interval by different forecasters for each stock as follows. • CPFIji is the consensus probability forecast for stock i in interval j. • PFIjin is the probability forecast for stock i in interval j of forecaster n. • Although the consensus distribution is closer to normal normality cannot be assured. • At this point we defined the risk based on losses rather than gains. • We assumed that forecasters are more concerned with large losses than with large gains. • Therefore we used intervals correspond to losses larger than 3% on a weekly basis. • We formed the implied consensus normal distribution for each stock using the following optimization procedure. • E(Rpi) is the expected return for stock i , • ii is the variance of returns for stock i, • obtained from consensus probabilistic forecasts of professionals. • F(.) stands for the normal cumulative distribution.

39. Estimations • Efficient frontiers are estimated using the Ibbotson Associates Encorr optimization program. • Names and weights of stocks at each portfolio recorded for • minimum risk portfolio • maximum risk portfolio • four medium risk portfolios • the portfolio that matches the standard deviation of the actual market portfolio • Index tracking portfolio is used on the benchmark portfolio • Performance measured the week following the forecasts/forecast horizon of experts.

40. Findings • Comparison of expectations formation process • historical • best estimate • probabilistic efficient portfolio • Comparison of expected & realized returns • historical • best estimate • probabilistic efficient portfolios • Investment performance of portfolios based on expert’s assessments compared to that based on historical data.

41. Expected Efficient Frontiers

42. Expected Historical Efficient Frontier Versus Realized Returns

43. Best Estimates Efficient Frontier Versus Realized Returns

44. Probabilistic Efficient Frontier Versus Realized Returns

45. Realized Returns of the Portfolios on the Efficient Frontiers

46. Summary • Expectations formation process based on historical prices: • loss on all portfolios • minimum loss (.13%) index tracking portfolio • maximum loss (18.8%) on minimum risk portfolio as risk increases loss detonates. • Expectations formation process based on probabilistic forecasts • improved portfolio performance at all risk levels • mild losses, modest gains at higher risk levels • (1.2% max risk portfolio) • Expectation formation process based on point and interval forecasts. • further improvement in performance at all risk levels. • gains at all risk levels (except min risk portfolio) • weekly returns of 1.4% to 3.3%.

47. Conclusion • We integrate human behavior into financial modeling. • We report the performance of portfolios based on • real time forecasts • of actual portfolio managers • Portfolio performance of subjective forecasts much better than that based on historical data. • Literature on poor forecast accuracy versus • excellent portfolio performance! • Better performing financial models that utilize human judgement.