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Artificial Counselor System for Stock Investment

Author

Listed:
  • Hadi NekoeiQachkanloo
  • Benyamin Ghojogh
  • Ali Saheb Pasand
  • Mark Crowley

Abstract

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance. Two different methods are used for suggesting best portions, which are Markowitz portfolio theory and fuzzy investment counselor. The first approach is an optimization-based method which considers merely technical features, while the second approach is based on Fuzzy Logic taking into account both technical and fundamental features of the stock market. The experimental results on New York Stock Exchange (NYSE) show the effectiveness of the proposed system.

Suggested Citation

  • Hadi NekoeiQachkanloo & Benyamin Ghojogh & Ali Saheb Pasand & Mark Crowley, 2019. "Artificial Counselor System for Stock Investment," Papers 1903.00955, arXiv.org.
    • Handle: RePEc:arx:papers:1903.00955
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    References listed on IDEAS

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    1. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    2. Steve Y. Yang & Sheung Yin Kevin Mo & Anqi Liu, 2015. "Twitter financial community sentiment and its predictive relationship to stock market movement," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1637-1656, October.
    3. Abarbanell, JS & Bushee, BJ, 1997. "Fundamental analysis, future earnings, and stock prices," Journal of Accounting Research, Wiley Blackwell, vol. 35(1), pages 1-24.
    4. Ozgur Ican & Taha Bugra Celik, 2017. "Stock Market Prediction Performance of Neural Networks: A Literature Review," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(11), pages 100-108, November.
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