Resource allocation in the brain and the Capital Asset Pricing Model

Siddiqi, Hammad (2020): Resource allocation in the brain and the Capital Asset Pricing Model.

Abstract

What happens when information reaches the human brain? In economics, a black-box approach to information processing in the brain is generally taken with an implicit assumption that information, once it reaches the brain, is accurately processed. In sharp contrast, research in brain sciences has established that when information reaches the brain, a mental template or schema (neural substrate of knowledge) is first activated, which influences information absorption. Schemas are created through a resource intensive process in which finite brain resources are allocated to different tasks, with resource allocation in the brain having an impact on the structure of schemas. In this article, we explore the implications of this richer view from brain sciences for the capital asset pricing model (CAPM). We show that two versions of CAPM arise depending on how the brain resources are allocated in schema creation. In one version, the relationship between beta and expected returns is flat along with value and size effects. In the second version, the relationship between beta and expected return is strongly positive with an implied risk-free rate which could be negative. The two version CAPM provides a unified explanation for a series of empirical findings including high-alpha-of-low-beta, size and value effect as well as strongly positive relationship between beta and average stock returns at specific times such as on macroeconomic announcement days, and at market open. As certain morbidities, such as autism, are thought to be associated with lack of schemas that attenuate information, a laboratory experiment with high functioning autism sufferers might be our best bet at observing the classical CAPM in its full glory.

• Item Type: MPRA Paper
• Language: English
• Keywords: CAPM, Value Premium, Size Effect, Schema Theory, Low Beta Anomaly
• Subjects: G - Financial Economics > G0 - General > G02 - Behavioral Finance: Underlying Principles; G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates; G - Financial Economics > G1 - General Financial Markets > G19 - Other

References

Alonso, Brocas, and Carrillo (2014), “Resource allocation in the brain”, Review of Economic Studies, Vol. 81, pp. 501-534.

American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders. 5th. Washington, DC.

Anderson, J. R. (2000). Cognitive Psychology and Its Implications (5th ed.). New York, NY. Worth Publishers.

Basu, Duong, Markov and Tan (2013), “How Important are Earnings Announcements as an Information Source”, European Accounting Review, Vol. 22, pp. 221-256.

Cohen, Polk, and Vuolteenaho (2005), “Money illusion in the stock market: The Modigliani-Cohn hypothesis”, Quarterly Journal of Economics, Vol. 120, pp. 639 – 668.

Fang and Peress (2009), “Media coverage and the cross section of stock returns”, Journal of Finance, Vol. 64, pp. 2023-2052.

Frazzini and Pedersen (2014), “Betting against beta”, Journal of Financial Economics, Vol. 114, pp. 1-25.

Gilboa, A., and Marlatte, H. (2017), “Neurobiology of schema and schema mediated memory”, Trends in Cognitive Science, Vol. 21, pp. 618-631.

Graham, Harvey, and Rajgopal (2005), “The economic implications of corporate financial reporting”, Journal of Accounting and Economics, Vol. 40, pp. 3-73.

Hampson, P. J. & Morris, P. E. (1996) Understanding Cognition. Cambridge, MA. Blackwell Publishers.

Hendershott, Livdan, and Rosch (2019), “Asset pricing: A tale of night and day”, Journal of Finance (Forthcoming)

Ohki and Takei (2018), “Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations”, European Journal of Neuroscience, Vol. 48, pp. 2416-2430.

Spalding, Jones, Duff, Tranel, and Warren (2015), “Investigating the Neural Correlates of Schemas: Ventromedial Prefrontal Cortex Is Necessary for Normal Schematic Influence on Memory”, Journal of Neuroscience, Vol. 35, pp. 15745-15751.

Sweegers, Coleman, van Poppel, Cox, and Talamini (2015), “Mental schemas hamper memory storage of goal-irrelevant information”, Frontiers in Human Neuroscience, Vol. 9, pp. 6-29.

van Kesteren MT, Ruiter DJ, Fernandez G, Henson RN (2012), “How schema and novelty augment memory formation”, Trends in Neuroscience, Vol. 35, pp. 211–219.