Abstract
A Kalman-Filtered Feature-space approach is taken to forecast changes in the ISEQ (Irish Stock Exchange Equity Overall) Index using the previous five days’ lagged returns solely as inputs. The resulting model is tantamount to a time-varying (adaptive) technical trading rule, one which achieves an out-of-sample Sharpe (’reward-to-variability’) Ratio far superior to the ’buy-and-hold’ strategy and its popular ’crossing moving-average’ counterparts. The approach is contrasted to Recurrent Neural Network models and with other previous attempts to combine Kalman-Filtering concepts with (more traditional) Multi-layer Perceptron Network models. The new method proposed is found to be simple to implement, and, based on preliminary results presented here, might be expected to perform well for this type of problem.
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References
Edelman, D., Davy, P.: “Adaptive Technical Analysis in the Financial Markets using Machine Learning: a Statistical View”. In: Applied Intelligent Systems. Springer series: Studies in Fuzziness and Applied Computing, pp. 1–16. Springer, Berlin Heidelberg New York (2004)
Haykin, S.: Kalman Filters and Neural Networks. Wiley, New York (2001)
LeBaron, B. et al.: ”Simple Technical Trading Rules and the Stochastic Properties of Stock Returns”. Journal of Finance 47(5), 1731–1764 (1992)
Lo, A. et al.: “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”. Journal of Finance 55(4), 1705–1765 (2000)
Simon, H.A.: A mechanism for social selection and successful altruism. Science 250(4988), 1665–1668 (1990)
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Edelman, D. (2007). Using Kalman-Filtered Radial Basis Function Networks to Forecast Changes in the ISEQ Index. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_25
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DOI: https://doi.org/10.1007/978-3-540-71805-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71804-8
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