Abstract
This case study introduces a new investment technique approach to make investment decisions in the stock market with minimum risk and reduced potential human intuition bias. The document introduces a fuzzy recommender system (FRS) and discusses its impact in generating positive revenue compared with decisions of real investors. The theoretical background, design and implementation of the FRS in a stock exchange platform are properly presented. The performance is evaluated with respect to the strategies used by real investors in weekly investment rounds, considering three different investment scenarios: conservative, explorer and adventurer. Finally, a proper discussion about the results of the investment via the stock exchange platform, where the FRS performed in the top three of the list of best investors during the evaluation period and improvement opportunity areas is presented.
The current chapter is based on the work of José Mancera and Minh Tue Nguyen in the Web Analytics & Monitoring seminar research: “Fuzzy Recommender Systems in the Stock Market” at University of Fribourg, Switzerland, May 2015.
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Adapted from the course “Introduction to Recommender Systems” of Joseph A. Konstan, University of Minesota, United States.
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Mancera, J., Nguyen, M.T., Portmann, E. (2019). A Fuzzy-Based Recommender System: Case Börsenspiel for Swiss Universities. In: Meier, A., Portmann, E., Terán, L. (eds) Applying Fuzzy Logic for the Digital Economy and Society. Fuzzy Management Methods. Springer, Cham. https://doi.org/10.1007/978-3-030-03368-2_10
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