Abstract
This chapter addresses the topic of automated day trading systems based on artificial neural networks and multi-timeframe technical indicators, a very common market analysis technique. After we introduce the context of this study and give a short overview of day trading, we set out our approach and methodological framework. Then, we present the results obtained through these procedures on several of the most liquid stocks in the Romanian stock market. The final section of the chapter concludes the study and brings some insight about possible future work in the area.
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References
Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Expert Syst. Appl. 29(4), 927–940 (2005)
Farmer, D., Sidorowich, J.: Predicting chaotic time series. Phys. Rev. Lett. 59, 845–848 (1987)
Fernández-RodrÃguez, F., Sosvilla-Riveiro, S., GarcÃa-Artile, M.: An empirical evaluation of non-linear trading rules (Working paper), FEDEA., 16 (2001)
Gallagher, L., Taylor, M.P.: Permanent and Temporary Components of Stock Prices: Evidence from Assessing Macroeconomic Shocks. South. Econ. J. 69, 345–362 (2002)
Gately, E.: Neural networks for financial forecasting. Wiley, New York (1996)
de Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22, 443–473 (2006)
Hayati, M., Shirvany, Y.: Artificial neural network approach for short term load forecasting for Illam Region. Proc. World Acad. Sci. Eng. Technol. 22 (2007)
Kavussanos, M.G., Dockery, E.: A multivariate test for stock market efficiency: the case of ASE. Appl. Finan. Econ. 11(5), 573–579 (2001)
Lawrence, R.: Using neural networks to forecast stock market prices (1997) (Course project, University of Maritoba, Canada)
McNelis, D.P.: Neural networks in finance: gaining predictive edge in the market. Elsevier Academic Press, USA (2005)
Moreno, D., Olmeda, I.: The use of data mining and neural networks for forecasting stock market returns. Eur. J. Oper. Res. 182(1), 436–457 (2007)
Priddy, L.K., Keller, E.P.: Artificial Neural Network: an introduction. SPIE Press, Washington–Bellington (2005)
Rotundo, G., Torozzi, B., Valente, M.: Neural networks for financial forecast, private communication (1999)
Qian, B., Rasheed, K.: Hurst exponent and financial market predictability, Proc. Finan. Appl. Eng. 437 (2004)
Walczak, S.: An empirical analysis of data requirements for financial forecasting with neural networks. J. Manage. Inf. Syst. 17(4), 203–222 (2001)
Acknowledgments
This work is a part of CNCSIS TE_316 Grant « Intelligent Methods for Decision Fundamentation on Stock Market Transactions Based on Public Information », manager V.P. Bresfelean, Assistant Professor PhD.
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Stan, A. (2014). Day Trading the Emerging Markets Using Multi-Time Frame Technical Indicators and Artificial Neural Networks. In: Iantovics, B., Kountchev, R. (eds) Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational Intelligence, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-319-00467-9_16
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DOI: https://doi.org/10.1007/978-3-319-00467-9_16
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