Abstract
In this paper we consider a new strategy for supporting timing decisions in stock markets. The approach uses the logic data miner Lsquare, based on logic optimisation techniques. We adopt a novel concept of good session, based on the best return expected within a given time horizon. Such definition links indirectly the buying decision with the selling decision and make it possible to exploit particular features of stock time series. The method is translated into an investment strategy and then it is compared with the standard buy & hold strategy.
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Boros, E., Ibaraki, T., Makino, K.: Logical analysis of binary data with missing bits. Artificial Intelligence 107, 219–263 (1999)
Breiman, L., Friedman, J., Olshen, R.A., Stone, C.J.: Classification & Regression Trees. Pacific Grove, Wadsworth (1984)
Elton, E.J., Gruber, M.J.: Modern Portfolio Theory and Investment Analysis. John & Wiley & Sons Inc., Chichester (1995)
Fama, E.: Efficient capital markets: A review of theory and empirical work. Journal of Finance 25, 383–417 (1970)
Fama, E.: Efficient capital markets: II. Journal of Finance 46, 1575–1617 (1991)
Felici, G., Truemper, K.: A MINSAT Approach for Learning in Logic Domains. INFORMS Journal on Computing 13(3), 1–17 (2001)
Frankfurter, G., McGoun, E.: Anomalies in finance: what are they and what are they good for? International Review of Financial Analysis 10(4), 407–429 (2001)
Leigh, W., Modani, N., Purvis, R., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Systems with Applications 23, 155–159 (2002)
Leigh, W., Purvis, R., Ragusa, J.M.: Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems 32, 361–377 (2002)
Kovalerchuk, B., Vityaev, E.: Data Mining in Finance: Advances in Relational and Hybrid Methods. Kluwer Academic Publishers, Norwell Massachussetts (2000)
Pinches, G.E.: The Random Walk Hypothesis and Technical Analysis. Financial Analysts Journal (March-April 1970)
Pring, M.J.: Technical Analysis Explained: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points, 4th edn. McGraw-Hill, New York (2002)
Samuelson, P.: The Judgement of Economic Science on Rational Portfolio Management: Indexing, Timing, and Long Horizon Effect. Journal of Portfolio Management (1) (1989)
Triantaphyllou, E., Soyster, A.L.: On the Minimum Number of Logical Clauses Which Can be Inferred From Examples. Computers and Operations Research 23(8), 783–799 (1996)
Truemper, K.: Design of Logic-Based Intelligent Systems. Wiley Interscience, Hoboken (2004)
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Felici, G., Galante, M.A., Torosantucci, L. (2006). Logic Mining for Financial Data. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758549_65
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DOI: https://doi.org/10.1007/11758549_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34385-1
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