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Rule Discovery and Matching for Forecasting Stock Prices  

Ha, You-Min (연세대학교 컴퓨터과학과)
Kim, Sang-Wook (한양대학교 정보통신학부)
Won, Jung-Im (한양대학교 정보통신학부)
Park, Sang-Hyun (연세대학교 컴퓨터과학과)
Yoon, Jee-Hee (한림대학교 정보통신학부)
Publication Information
Journal of KIISE:Databases / v.34, no.3, 2007 , pp. 179-192 More about this Journal
Abstract
This paper addresses an approach that recommends investment types for stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to define various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and indexing them. We also suggest a method that finds the rules matched to a query issued by an investor from a frequent pattern base, and a method that recommends an investment type using the rules. Finally, we verify the superiority of our approach via various experiments using real-life stock data.
Keywords
stock databases; rule discovery; rule matching;
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