Robust stock trading using fuzzy decision trees

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012 - Proceedings

First Page

149

Last Page

156

Publication Date

11-27-2012

Publication Status

1

Abstract

Stock market analysis has traditionally been proven to be difficult due to the large amount of noise present in the data. Different approaches have been proposed to predict stock prices including the use of computational intelligence and data mining techniques. Many of these methods operate on closing stock prices or on known technical indicators. Limited studies have shown that Japanese candlestick analysis serve as rich information sources for the market. In this paper decision trees based on the ID3 algorithm are used to derive short-term trading decisions from candlesticks. To handle the large amount of uncertainty in the data, both inputs and output classifications are fuzzified using well-defined membership functions. Testing results of the derived decision trees show significant gains compared to ideal mid and long-term trading simulations both in frictionless and realistic markets. © 2012 IEEE.

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Digitial Object Identifier (DOI)

10.1109/CIFEr.2012.6327785

Disciplines

Electrical and Electronics

Keywords

Stock exchanges; Stocks—Prices; Stock price forecasting

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