Information Token Driven Machine Learning For Electronic Markets: Performance Effects In Behavioral Financial Big Data Analytics.

Jim Samuel


Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of ‘big-data’ phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned: ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions.


Information, Big Data, Electronic Markets, Analytics, Behavior

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Ajzen, Icek, and Martin Fishbein. "Understanding attitudes and predicting social behaviour." (1980).

Ajzen, Icek, et al. "Knowledge and the prediction of behavior: The role of information accuracy in the theory of planned behavior." Basic and Applied Social Psychology 33.2 (2011): 101-117.

Alpaydin, Ethem. Introduction to machine learning. MIT press, 2014.

Barberis, Nicholas, and Richard Thaler. "A survey of behavioral finance." Handbook of the Economics of Finance 1 (2003): 1053-1128.

Bentzen, Eric, John K. Christiansen, and Claus J. Varnes. "What attracts decision makers' attention? Managerial allocation of time at product development portfolio meetings." Management Decision 49.3 (2011): 330-349.

Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey. "Business intelligence and analytics: From big data to big impact." MIS quarterly 36.4 (2012): 1165-1188.

Cipriani, Marco, and Antonio Guarino. "Herd behavior in a laboratory financial market." The American Economic Review 95.5 (2005): 1427-1443.

De Bondt, Werner, Rosa M. Mayoral, and Eleuterio Vallelado. "Behavioral decision-making in finance: An overview and assessment of selected research." Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad 42.157 (2013): 99-118.

Delone, William H., and Ephraim R. McLean. "The DeLone and McLean model of information systems success: a ten-year update." Journal of management information systems 19.4 (2003): 9-30.

Dennis, Alan R., and Susan T. Kinney. "Testing media richness theory in the new media: The effects of cues, feedback, and task equivocality." Information systems research 9.3 (1998): 256-274.

Fenton‐O'Creevy, Mark, et al. "Thinking, feeling and deciding: The influence of emotions on the decision making and performance of traders." Journal of Organizational Behavior 32.8 (2011): 1044-1061.

Fischer, Asja, and Christian Igel. "Training restricted Boltzmann machines: An introduction." Pattern Recognition 47.1 (2014): 25-39.

Floridi, L. (2011). The Philosophy Of Information. Oxford University Press.

Garcia, Maria Jose Roa. "Financial education and behavioral finance: new insights into the role of information in financial decisions." Journal of Economic Surveys 27.2 (2013): 297-315.

Gopal, Ram D., Ram Ramesh, and Andrew B. Whinston. "Microproducts in a digital economy: Trading small, gaining large." International Journal of Electronic Commerce 8.2 (2003): 9-30.

Greiner, Martina E., and Hui Wang. "Building consumer-to-consumer trust in e-finance marketplaces: An empirical analysis." International Journal of Electronic Commerce 15.2 (2010): 105-136.

Grover, V., Lim, J., & Ayyagari, R. (2006). The dark side of information and market efficiency in e‐markets. Decision Sciences, 37(3), 297-324.

Hirshleifer, David. "Behavioral finance." Annual Review of Financial Economics 7 (2015): 133-159.

Madden, Thomas J., Pamela Scholder Ellen, and Icek Ajzen. "A comparison of the theory of planned behavior and the theory of reasoned action." Personality and social psychology Bulletin 18.1 (1992): 3-9.

Meyer, George, et al. "A machine learning approach to improving dynamic decision making." Information Systems Research 25.2 (2014): 239-263.

Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

Nassirtoussi, Arman Khadjeh, et al. "Text mining for market prediction: A systematic review." Expert Systems with Applications 41.16 (2014): 7653-7670.

Olbrich, Rainer, and Christian Holsing. "Modeling consumer purchasing behavior in social shopping communities with clickstream data." International Journal of Electronic Commerce 16.2 (2011): 15-40.

Sahi, Shalini Kalra, Ashok Pratap Arora, and Nand Dhameja. "An exploratory inquiry into the psychological biases in financial investment behavior." Journal of behavioral finance 14.2 (2013): 94-103.

Schwartz, Robert, Avner Wolf, and Jacob Paroush. "The dynamic process of price discovery in an equity market." Managerial Finance 36.7 (2010): 554-565.

Sharma, Anuj, and Prabin Kumar Panigrahi. "A Review of Financial Accounting Fraud Detection based on Data Mining Techniques." International Journal of Computer Applications 39.1 (2012): 37-47.

Vapnik, Vladimir N. "An overview of statistical learning theory." IEEE transactions on neural networks 10.5 (1999): 988-999.

Wang, Xiao-Wei, Dan Nie, and Bao-Liang Lu. "Emotional state classification from EEG data using machine learning approach." Neurocomputing 129 (2014): 94-106.

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