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http://dx.doi.org/10.3745/KIPSTB.2003.10B.3.257

A Two-Phase Stock Trading System based on Pattern Matching and Automatic Rule Induction  

Lee, Jong-Woo (광운대학교 컴퓨터공학과)
Kim, Yu-Seop (한림대학교 정보통신공학부)
Kim, Sung-Dong (한성대학교 컴퓨터시스템공학과)
Lee, Jae-Won (성신여자대학교 컴퓨터정보공학부)
Chae, Jin-Seok (인천대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.10B, no.3, 2003 , pp. 257-264 More about this Journal
Abstract
In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.
Keywords
Stock Trading System; Pattern Matching; Automatic Rule Generation; Finance Prediction System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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