Abstract
A methodology for the discovery of temporal rules in stock market data is presented. The two types of temporal rules defined are snapshot and aggregate. Snapshot temporal rules are formulated by associating a current decision attribute instance to the relevant past condition attribute instances. Aggregate temporal rules are formulated by associating a current decision attribute instance to the total change of relevant past condition attribute instances. Any chronologically based data can be disretized in this manner to produce snapshot and aggregate temporal rules. The knowledge discovery tool, Datalogic/R+, was used from Reduct Systems which applies the concepts of Rough Set Theory for pattern recognition in data. Monthly stock market data through the 80’s representing 120 cases containing 32 stock and economic indicators are analyzed. The main objective is to discover relationships with a company’s stock price change to stock and economic indicators over a time lapse of six months. Relationships were derived by the discovery of snapshot and aggregate temporal rules. The temporal rules discovered are consistent and confirm one another.
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References
Ziarko, W. and Golan, R. and Edwards, D. An Application of Datalogic/R Knowledge Discovery Tool to Identify Strong Predictive Rules in Stock Market Data. AAAI-93 Workshop on Knowledge Discovery in Databases.
Chinetti, D. and Garden, F. and Rossignoli, C. A Neural Network Model for Stock Market Prediction. The 2nd International Conference on AI Applications on Wall Street. April 19, 1993.
Barr, D. and Mani, G. Neural Networks in Investment Management: Multiple Uses. The 2nd International Conference on AI Applications on Wall Street. April 19, 1993.
Cassetti, M. A Neural Network System for Reliable Trading Signals. The 2nd International Conference on AI Applications on Wall Street. April 19, 1993.
Hiemstra, Y. Tactical Asset Allocation Using Fuzzy Logic and Mean-Variance Optimization. The 2nd International Conference of AI Applications on Wall Street. April 19, 1993.
Gargano, M and Chamoun, P, and von Kleeck, D. Using Genetic Algorithms to Solve Financial Portfolio Problems Related to Optimal Allocation, Portfolio Insurance, and Performance Predication. The 2nd International Conference of AI Applications on Wall Street. April 19, 1993.
Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991.
Piatetsky-Shapiro, G. Discovery of Strong Rules in Databases. Proc. of IJCAI-89 Workshop on Knowledge Discovery in Databases, 264–274
Ziarko, W. Variable Precision Rough Sets Model. Journal of Computer and Systems Sciences. Volume 46, no 1, 1993, pp 39–59.
Ziarko, W. Analysis of Uncertain Information in the Framework of Variable Precision Rough Sets. Foundations of Computing and Decision Sciences. Volume 18, no 3–4, 1993, pp 381–396.
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© 1994 British Computer Society
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Golan, R., Edwards, D. (1994). Temporal Rules Discovery using Datalogic/R+ with Stock Market Data. In: Ziarko, W.P. (eds) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3238-7_9
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DOI: https://doi.org/10.1007/978-1-4471-3238-7_9
Publisher Name: Springer, London
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