Abstract
Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.
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References
Wen, Q., Yang, Z., Song, Y., Jia, P.: Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications 37(2), 1015–1022 (2010)
Wang, Y.F.: Mining stock prices using fuzzy rough set system. Expert System with Applications 24(1), 13–23 (2003)
Chiu, D.Y., Chen, P.J.: Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm. Expert Systems with Applications 36(4), 1240–1248 (2009)
Hiemstra, C., Jones, D.: Testing for Linear and Nonlinear Granger Causality in the Stock Price-volume Relation. Journal of Finance 49(5), 1639–1664 (1994)
Black, A.J., Mcmillan, D.G.: Non-linear predictability of value and growth stocks and economic activity. Journal of Business Finance and Accounting 31(3/4), 439–474 (2004)
Bao, D., Yang, Z.: Intelligent stock trading system by turning point confirming and probabilistic reasoning. Expert Systems with Applications 34(1), 620–627 (2008)
Chang, P.C., Liu, C.H.: A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications 34(1), 135–144 (2008)
Leigh, W., Modani, N., Purvis, R., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Systems with Applications 23(2), 155–159 (2002)
Edwards, R., Magee, J.: Technical analysis of stock trends, 7th edn. Amacom, New York (1997)
Malkiel, B.G.: A random walk down wall street. Norton & Co., New York (1995)
Wang, J.L., Chan, S.H.: Stock market trading rule discovery using two-layer bias decision tree. Expert Systems with Applications 30(4), 605–611 (2006)
Kovalerchuk, B., Vityaev, E.: Data mining in finance: advances in relational and hybrid methods. Kluwer Academic, Dordrecht (2000)
White, H.: Economic prediction using neural networks: The case of IBM daily stock returns. In: Proceedings of the 2nd Annual IEEE Conference on Neural Networks, II, pp. 451–458 (1988)
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural network. In: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, pp. 1–6 (1990)
Trippi, R.R., DeSieno, D.: Trading equity index futures with a neural network. Journal of Portfolio Management 19(1), 27–33 (1992)
Aiken, M., Bsat, M.: Forecasting market trends with neural networks. Information Systems Management 6(4), 42–48 (1994)
Wang, L.P., Fu, X.J.: Data Mining with Computational Intelligence. Springer, Heidelberg (2005)
Yao, J., Herbert, J.P.: Financial time-series analysis with rough sets. Applied Soft Computing 9(3), 1000–1007 (2009)
Wilder Jr., J.W.: New Concepts in Technical Trading Systems. Hunter Publishing Company, Greensboro (1978)
Arulampalam, G., Bouzerdoum, A.: A generalized feedforward neural network architecture for classification and regression. Neural Networks 16(5-6), 561–568 (2003)
Fernández-Rodríguez, F., González-Martel, C., Sosvilla-Rivero, S.: On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters 69(1), 89–94 (2000)
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Rodríguez-González, A. et al. (2010). Improving Trading Systems Using the RSI Financial Indicator and Neural Networks. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_3
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DOI: https://doi.org/10.1007/978-3-642-15037-1_3
Publisher Name: Springer, Berlin, Heidelberg
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