Abstract
The paper first tries to replicate experiments by Huber (J Econ Dyn Control 31:2536–2572, 2007) and Huber et al. (J Econ Behav Org 65:86–104, 2008), which show that in double auction markets with uneven information distribution that is common knowledge, returns are a J-shaped function of the information known by different investors. Huber proposed the pattern of future earnings as the reason of J-shaped function. But our paper secondly asserts the psychological state of personal investor as the reason. It also asserts that the psychological state of personal investor often destroys efficient market. Functional magnetic resonance imaging scans of subjects in a simple game indicate that subjects with medium amounts of information use different brain areas. The paper argues that these patterns are consistent with medium-informed investors using Matching Law strategy rather than the maximizing strategy of the least and best informed investors. The paper motivates an accounting connection by remarking that financial statement disclosure is mandated in most developed stock markets.
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Notes
The rational expectations hypothesis has been called as the “ efficient markets hypothesis and used quite extensively in financial market research (see Sheffrin 1983, p. 112).
Assuming a market which does not incorporate the future expectations, etc., of market participants—which is less likely to be influenced by psychological bias—it is clear that the market pricing mechanism is significantly robust and is likely to see through misleading information in a timely manner (see also Yamaji and Gotoh 2010).
The rationale behind choosing six subjects was to clarify that the objective of the experiment was to explore the relationship between the information level and the return achieved as a result of decisions made. It is desirable to have more than five participants, but the number of participants need not necessarily be limited to six.
The time required for the experiment was at the longest 120 min because we did four treatments simultaneously.
We used “Francs” as the money unit for the game instead of “Yen,” the use of which is so widespread that it would induce subjects to imagine the real world.
Please refer to Appendix 2 for details on the stimulation image.
In this section concerning the fMRI experiment, we do not discuss the returns of investors because the returns of differently informed subjects are not interlocked with each other in a market. Our fMRI experiment is exclusively aimed at reproducing the psychological conditions of differently informed investors in simulated laboratory markets.
The description uses the term “three subjects”. In the actual experiment, the two subjects other than the fMRI subject are not real people, but are substituted by subjects from a sample extracted from records of past games. Therefore, the fMRI subject actually plays games with virtual subjects on a PC.
Software from Neurobehavioral Systems, Inc.
There is a tendency to consider the time the button is pushed as the time the decision is made. However, we recognize the time lag between the decision being made and the button being pushed, so our onset time setting enables us to collect fMRI data within a certain time span.
Software developed by Statistical Parametric Mapping.
Five subjects were supposed to sleep in a noisy MRI equipment.
Strictly speaking, we recognized the activated parts through the mentioned significant level.
Numerical data supplement to Fig. 5 show the results of (S2–S1) and (S6–S5) respectively. We did not reproduce nuro-image data because we cannot recognize any activated parts with the mentioned significant level. So supplementary data were also shown only in the electronic supplementary material.
Based on the induced value theory by Smith (1976).
References
Arrow, J. K. (1971). Essays in the theory of risk bearing. Amsterdam: North-Holland Pu.co.
Ball, R. (1978). Anomalies in relationships between securities’ yields and yield-surrogates. Journal of Financial Economics, 6, 103–126.
Bloomfield, R., & Libby, R. (1996). Market reaction to differentially available information in laboratory. Journal of Accounting Research, 34, 183–207.
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307, 1118–1121.
Camerer, C.F. (2009). Behavioral game theory and the neural basis of strategic choice. Chapter 13, in C. F. Glimcher, E. Fehr & R. A. Poldrack (Eds.) Neuroeconomics: Decision making and the brain. London: Academic Press.
Fama, E. F. (1970). Efficient market hypothesis: A review of theory and empirical work. Journal of Finance, 25, 383–417.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56.
Glimcher, P.W., Camerer, C. F., Fehr, E., & R. A. Poldrack (Eds.). (2009). Neuroeconomics: Decision making and the brain. London: Academic Press.
Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 306(15), 447–452.
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70, 393–408.
Herrnstein, R. J. (1997). The Matching law. Cambridge: Russell Sage Foundation and Harvard Uviversity Press.
Herwig, U., Kaffenberger, T., Baumgartner, T., & Jänckeb, L. (2007). Neural correlates of a ’pessimistic’ attitude when anticipating events of unknown emotional valence. NeuroImage, 34, 848–858.
Hirshleifer, D. (2001). Investor psychology and asset pricing. The Journal of Finance, 56, 1533–1597.
Huber, J. (2007). J’-shaped returns to timing advantage in access to information—experimental evidence and a tentative explanation. Journal of Economic Dynamics & Control, 31, 2536–2572.
Huber, J. (2008). J-shaped returns to timing advantage in access to information—experimental evidence and a tentative explanation. Working paper at Yale University.
Huber, J., Kirchler, M., & Sutter, M. (2008). Is more information always better? Experimental financial markets with cumulative information. Journal of Economic Behavior & Organization, 65, 86–104.
Kirsten, G. V., Ricarda, I., Schubotz, D., & von Cramon, Yves. (2005). Variants of uncertainty in decision-making and their neural correlates. Brain Research Bulletin, 67, 403–412.
Kyle, A. S. (1985). Auctions and insider trading. Econometrica, 53, 1315–1335.
Lucas, R. E., & T. J. Sargent (Eds.). (1981). Rational expectations and econometric practice. Minnesota: The University of Minnesota Press.
Lundholm, R. J. (1991). What affects the efficiency of a market ? Some answers from the laboratory. The Accounting Review, 66, 486–515.
Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29, 315–335.
O’Hara, M. (1995). Market microstructure theory. Cambridge: Blackwell.
Ou, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11, 295–329.
Plott, C. R., & Sunder, S. (1982). Efficiency of experimental security markets with insider information: An application of rational-expectations models. Journal of Political Economy, 90, 663–698.
Plott, C. R., & Sunder, S. (1988). Rational expectations and the aggregation of diverse information in laboratory security markets. Econometrica, 56, 1085–1118.
Sakai, Y., & Fukai, T. (2008). When does reward maximization lead to Matching Law ? PLoS One, 3(11), 1–7.
Samuelson, P. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–49.
Sanfey, A. G., Rilling, J. K., Aaronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision making: An fMRI investigation of the ultimatum game. Science, 300(5626), 1755–1758.
Sheffrin, S. M. (1983). Rational expectations. New York: Cambridge University Press.
Simon, H. A. (1957). Behavioral Model of Rational Choice. in H.A. Simon (Ed.) Models of man, social and rational: Mathematical essays on rational human behavior in a social setting. New York: Wiley.
Smith, V. L. (1976). Experimental economics: Induced value theory. The American Economic Review, 66, 274–279.
Smith, V. L. (2003). Constructivist and ecological rationality in economics. American Economic Review, 93, 465–508.
Spulber, D. F. (1999). Market microstructure. New York: Intermediaries and the Theory of Firm. Cambridge University Press.
Volz, K. G., Ricarda, I., Schubotz, D., & von Cramon, Yves. (2005). Variants of uncertainty in decision-making and their neural correlates. Brain Research Bulletin, 67, 403–412.
Yamaji, H., & Gotoh, M. (2010). Cognitive bias in the laboratory security market. Computational Economics, 35(2), 101–126.
Yamasaki, H., LaBar, K. S., & MaCarthy, G. (2002). Dissociable prefrontal brain systems for attention and emotion. PNAS, 99(17), 11447–11451.
Young, L., Cushman, F., Hauser, M., & Saxe, R. (2007). The neural basis of the interaction between theory of mind and moral judgment. PANS, 104(20), 8235–8240.
Acknowledgments
The authors gratefully acknowledge the financial support for this research which was provided by Grant-in-Aid for Scientific Research on Priority Areas (for this research which was provided Experimentally- based New Social Sciences for the twenty first Century) headed by Professor Tatsuyoshi Saijo and Scientific Fund (C) (2015-17) headed by Hidetoshi Yamaji.
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Appendices
Appendix 1: Instructions for Laboratory Experiment Subjects
You will play one type of game with six investors (subjects) in which you buy and sell one stock.
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(1)
The six investors each have a different information level: One trader knows the dividend for the current period; the second trader knows the dividend for the current period plus the dividend for the next period, and so on. The sixth trader knows the dividend for the current period plus the dividends for the five following periods shown in Fig. 9.
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(2)
Each investor is endowed with 100 securities and 10,000 Francs at the beginning of the trading game.
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(3)
The initial tentative price of the security is 100 Francs. A risk-free interest rate of 5 % is paid for cash holdings in each period. Trading time per period is one minute. One experiment consists of 15–25 consecutive periods. The number of periods until termination is decided by the subjects.
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(4)
A certain dividend stream will be used throughout each trading experiment treatment. Each investor has the possibility of experiencing the four types of information level during four trading experiment treatments. For every experiment treatment, the six investors are ranked according to their Franc-denominated returns. Using the Franc/Yen exchange rate, the return is converted into Yen for actual payment of the experiment return.Footnote 18
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(5)
The image on the PC screen display seen by the investor (subject) during the game is shown in Fig. 10.
Suppose a subject has received dividend information for three periods by random choice; the PC screen displayed will look like Fig. 10. The screen shows his/her dividend information for period X, i.e., the dividend for the current period, the next period, and the period after the next period. In the next period, period X \(+\) 1, the information shifts by one period. What used to be the dividend for “the next period” now becomes that for the current period, that for “the period after the next period” becomes that for the following period, and new dividend information (7.0 Francs) appears on the screen as the dividend for the period after the next period. The subject who receives three periods of information will see three pieces of dividend information on the PC screen shown in Fig. 10 throughout one experiment treatment to make investment decisions and trade securities. Trading is conducted in such conditions in order to compare the earnings of the six subjects (market participants).
Appendix 2: Instructions for fMRI Experiment Subjects
There are three participants in this game. At the start of the game (Display 1: T \(=\) 0) you are looking at three (or 1 or 5) numerical value(s) selected from a set of six numbers. Next, as additional information, the assessments of the other two players are shown for two seconds just after two seconds into the game (Display 2 : T \(=\) 4). (The assessments of the two other participants have been pre-installed in the computer. In the above example, one player can see five numbers and the other one number.) The subject is required to answer whether the average of the six numbers is higher (H) or lower (L) than five. The subject can answer by using one of two buttons (one button is for high and the other is for low). If the subject answers correctly, the cumulative number of correct answers on his display is increased by one (Display 3). A new game starts soon after he gives his answer.
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Yamaji, H., Gotoh, M. & Yamakawa, Y. Additional Information Increases Uncertainty in the Securities Market: Using both Laboratory and fMRI Experiments. Comput Econ 48, 425–451 (2016). https://doi.org/10.1007/s10614-015-9532-5
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DOI: https://doi.org/10.1007/s10614-015-9532-5