Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Combinatorial Portfolio Selection with the ELECTRE III method: Case study of the Stock Exchange of Thailand (SET)

,

DOI: http://dx.doi.org/10.15439/2016F228

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 719–724 ()

Full text

Abstract. Various techniques of portfolio selection are applied to interpret the status of the market and predict the market's future trend, but they are not beneficial to small investors because these techniques should be administered by an expert. In addition, these techniques desire accumulation of data about the market and complicated calculations, which is too much effort for individual small investors. Therefore, portfolio selection with two significant financial ratios using the ELECTRE III method is proposed for these investors to make trading decisions. In order to demonstrate the effectiveness of this new method, it is compared to the situation where a fix percentage allocation existed and data was collected from the Stock Exchange of Thailand (SET).

References

  1. Allen F, Karjalainen R. (1999). Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics 51, 245–271.
  2. Basu S. (1977). Investment performance of common stocks in relation to their Price-Earnings ratios: a test of the efficient market hypothesis, Journal of Finance 32(3), 663-682.
  3. Boonyapatkul P (2011). Investor flows and stock return empirical evidence from stock exchange of Thailand, Master of Science Program in Finance (International Program), Faculty of Commerce and Accountancy Thammasat University, Bangkok, Thailand.
  4. Cao L J, Tay F E H. (2003). Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transactions on Neural Networks 14(6), 1506–1518.
  5. Chan L K, Lakonishok J. (2004). Value and growth investing: review and update. Financial Analysts Journal 60(1), 71-86.
  6. Creamer G, Freund Y. (2007). A Boosting Approach for Automated Trading. Journal of Trading 2(3), 84–96.
  7. Creamer G. (2007). Using Boosting for Automated Planning And Trading Systems. Ph.D. Dissertation. Columbia University.
  8. Creamer G. (2012). Model Calibration and Automated Trading Agent for Euro Futures. Quantitative Finance 12(4), 531–545.
  9. Dempster M A H, Payne T W, Romahi Y, Thompson G W T. (2001). Computational Learning Techniques for Intraday FX Trading Using Popular Technical Indicators. IEEE Transactions on Neural Networks 12(4), 744–754.
  10. Fafuła A., Drelczuk K. (2015). Buying stock market winners on Warsaw Stock Exchange - quantitative backtests of a short term trend following strategy. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Wrocław, 1361–1366.
  11. Greenblatt J. (2006). The little book that beats the market, John Wiley & Sons, Hoboken.
  12. Kimoto T, Asakawa K, Yoda M, Takeoka M. (2000). Stock Market Prediction System with Modular Neural Networks. Neural Networks in Finance and Investing, 343–357.
  13. Liu J N K, Leung T T S (2001). A Web-based CBR Agent for Financial Forecasting. In: Proceeding of the 4th International Conference on Case-Based Reasoning, 243-253.
  14. Lu C J, Lee T S, Chiu C C. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems 47(2), 115–125.
  15. Mahfoud S, Mani G. (1996). Financial Forecasting Using Genetic Algorithms. Applied Artificial Intelligence 10, 543–565.
  16. Mandziuk J, Jaruszewicz M. (2011). Neuro-genetic System for Stock Index Prediction. Journal of Intelligent & Fuzzy Systems 22, 93–123.
  17. Moody J, Saffell M. (2001). Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks 12 (4), 875–889.
  18. Moody J, Wu L, Liao Y, Saffell M. (1998). Performance Functions and Reinforcement Learning For Trading Systems and Portfolios. Journal of Forecasting 17, 441–471.
  19. Maneesilasan N. (2011). GARP Investing in Thailand., Working Paper, National Institute of Development Administra-tion, Bangkok.
  20. O J, Lee J W, Zhang B T. (2002). Stock Trading System Using Reinforcement Learning with Cooperative Agents. In Proceedings of the 19th International Conference on Machine Learning, 451–458.
  21. Roy B.: Electre III (1978). Algorithme de classement base sur une representation floue des preferences en presence de criteres multiples, Cahiers du CERO 20(1), 3-24.
  22. Sareewiwatthana P. (2011). Value Investing in Thailand: The Test of Basic Screening Rules, International Review of Business Research Papers 7(4), 1-13.
  23. Tay F E H, Cao L J. (2002). Modified Support Vector Machines in Financial Time Series Forecasting. Neurocomputing 48, 847–861.
  24. Tsang E, Yung P, Li J. (2004). EDDIE-Automation, A Decision Support Tool for Financial Forecasting. Decision Support Systems 37, 559–565. (Periodical style)