Robust stock trading using fuzzy decision trees
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 closi...
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oai:animorepository.dlsu.edu.ph:faculty_research-11392021-07-09T08:11:27Z Robust stock trading using fuzzy decision trees Ochotorena, Carlo Noel Yap, Cecille Adrianne Dadios, Elmer Sybingco, Edwin 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. 2012-11-27T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/140 Faculty Research Work Animo Repository Stock exchanges Stocks—Prices Stock price forecasting Electrical and Electronics |
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Stock exchanges Stocks—Prices Stock price forecasting Electrical and Electronics Ochotorena, Carlo Noel Yap, Cecille Adrianne Dadios, Elmer Sybingco, Edwin Robust stock trading using fuzzy decision trees |
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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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Ochotorena, Carlo Noel Yap, Cecille Adrianne Dadios, Elmer Sybingco, Edwin |
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Ochotorena, Carlo Noel Yap, Cecille Adrianne Dadios, Elmer Sybingco, Edwin |
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Ochotorena, Carlo Noel |
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Robust stock trading using fuzzy decision trees |
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Robust stock trading using fuzzy decision trees |
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Robust stock trading using fuzzy decision trees |
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Robust stock trading using fuzzy decision trees |
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Robust stock trading using fuzzy decision trees |
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robust stock trading using fuzzy decision trees |
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2012 |
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https://animorepository.dlsu.edu.ph/faculty_research/140 |
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