Applying rough sets to market timing decisions

A lot of research has been done to predict economic development. The problem studied here is about the stock prediction for use of investors. More specifically, the stock market's movements will be analyzed and predicted. We wish to retrieve knowledge that could guide investors on when to buy and sell. Through a detailed case study on trading S&P 500 index, rough sets is shown to be an applicable and effective tool to achieve this goal. Some problems concerning time series transformation, indicator selection, trading system building in real implementation are also discussed.

[1]  Steven B. Achelis.  Technical Analysis from A to Z : Covers Every Trading Tool... from the Absolute Breadth Index to the Zig Zag , 2000 .

[2]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[3]  A. E. Eiben,et al.  Genetic Modelling of Customer Retention , 1998, EuroGP.

[4]  Richard J. Bauer,et al.  Genetic Algorithms and Investment Strategies , 1994 .

[5]  Constantin Zopounidis,et al.  Prediction of company acquisition in Greece by means of the rough set approach , 1997, Eur. J. Oper. Res..

[6]  Huan Liu,et al.  Feature Selection via Discretization , 1997, IEEE Trans. Knowl. Data Eng..

[7]  Randy Kerber,et al.  ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.

[8]  Andrzej Skowron,et al.  Boolean Reasoning for Feature Extraction Problems , 1997, ISMIS.

[9]  Andrzej Skowron,et al.  Searching for Features Defined by Hyperplanes , 1996, ISMIS.

[10]  Constantin Zopounidis,et al.  Application of the Rough Set Approach to Evaluation of Bankruptcy Risk , 1995 .

[11]  E. Fama.  Random Walks in Stock Market Prices , 1965 .

[12]  Marcin Szczuka,et al.  Rough Sets in KDD , 2005 .

[13]  Wojciech Ziarko,et al.  Rough Sets and Knowledge Discovery: An Overview , 1993, RSKD.

[14]  Roman Slowinski,et al.  Rough Set Learning of Preferential Attitude in Multi-Criteria Decision Making , 1993, ISMIS.

[15]  Massimo Paruccini,et al.  Applying multiple criteria aid for decision to environmental management. , 1994 .

[16]  Dirk Van den Poel,et al.  Rough Sets for Database Marketing , 1998 .

[17]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[18]  P. A. Hansson,et al.  Chaos: Implications for forecasting☆ , 1991 .

[19]  Yeuvo Jphonen,et al.  Self-Organizing Maps , 1995 .

[20]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[21]  Zbigniew W. Ras,et al.  Methodologies for Intelligent Systems , 1991, Lecture Notes in Computer Science.

[22]  Ray R. Hashemi,et al.  A hybrid intelligent system for predicting bank holding structures , 1998, Eur. J. Oper. Res..

[23]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[24]  Francis Eng Hock Tay,et al.  A Modified Chi2 Algorithm for Discretization , 2002, IEEE Trans. Knowl. Data Eng..

[25]  Robert Golan,et al.  Temporal Rules Discovery Using Datalogic/R+ with Stock Market Data , 1993, RSKD.

[26]  Arlene Mandell.  Turning The Key , 1992 .

[27]  Ivo Düntsch,et al.  Classificatory filtering in decision systems , 2000, Int. J. Approx. Reason..

[28]  C. Zopounidis,et al.  Rough-Set Sorting of Firms According to Bankruptcy Risk , 1994 .

[29]  Sinh Hoa Nguyen,et al.  Rough Sets and Association Rule Generation , 1999, Fundam. Informaticae.

[30]  R. Ribeiro.  Soft computing in financial engineering , 1999 .

[31]  Francis Eng Hock Tay,et al.  Economic and financial prediction using rough sets model , 2002, Eur. J. Oper. Res..

[32]  Roman Słowiński,et al.  A New Rough Set Approach to Evaluation of Bankruptcy Risk , 1998 .

[33]  Tsau Young Lin,et al.  Attribute Transformations on Numerical Databases. Applications to Stock Market and Economic Data , 2000, PAKDD.

[34]  T. J. Euverman,et al.  Modelling Customer Retention with Statistical Techniques, Rough Data Models and Genetic Programming , 1999 .

[35]  W. Ziarko,et al.  An application of DATALOGIC/R knowledge discovery tool to identify strong predictive rules in stock market data , 1993 .

[36]  W. Sharpe.  The Sharpe Ratio , 1994 .

[37]  Krzysztof Skabek,et al.  Rough Sets in Economic Applications , 1998 .

[38]  W. Michalowski,et al.  Identifying Regularities in Stock Portfolio Tilting , 1997 .

[39]  Francis Eng Hock Tay,et al.  Classifying Market States with WARS , 2000, IDEAL.

[40]  C. Zopounidis.  Operational tools in the management of financial risks , 1997 .

[41]  Liangsheng Qu,et al.  Fault diagnosis using Rough Sets Theory , 2000 .

[42]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[43]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[44]  Ganesh Mani,et al.  An analysis of neural-network forecasts from a large-scale, real-world stock selection system , 1995, Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[45]  Frank Slisser,et al.  Modelling Customer Retention with Rough Data Models , 1997, PKDD.

[46]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[47]  Ivo Düntsch,et al.  Uncertainty Measures of Rough Set Prediction , 1998, Artif. Intell..

[48]  Wojtek Kowalczyk,et al.  Rough-Set Inspired Approach to Knowledge Discovery in Business Databases , 1998, PAKDD.

[49]  Dirk Van den Poel,et al.  Purchase Prediction in Database Marketing with the ProbRough System , 1998, Rough Sets and Current Trends in Computing.

[50]  Hiroshi Tanaka,et al.  Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough Sets , 1996, KDD.

[51]  Jerzy W. Grzymala-Busse,et al.  Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..

[52]  C. J. V. Rijsbergen,et al.  Rough Sets, Fuzzy Sets and Knowledge Discovery , 1994, Workshops in Computing.

[53]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[54]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[55]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.