Automated Trading Point Forecasting Based on Bicluster Mining and Fuzzy Inference
| Autor: | Jie Yang, Xiangfei Feng, Alan Wee-Chung Liew, Xuelong Li, Qinghua Huang |
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| Rok vydání: | 2020 |
| Předmět: |
Fuzzy rule
Artificial neural network Computer science business.industry Applied Mathematics 02 engineering and technology computer.software_genre Fuzzy logic Support vector machine Market research Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Technical analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Algorithmic trading business computer Stock (geology) |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 28:259-272 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2019.2904920 |
| Popis: | Historical financial data are frequently used in technical analysis to identify patterns that can be exploited to achieve trading profits. Although technical analysis using a variety of technical indicators has proven to be useful for the prediction of price trends, it is difficult to use them to formulate trading rules that could be used in an automatic trading system due to the vague nature of the rules. Moreover, it is challenging to determine a specified combination of technical indicators that can be used to detect good trading points and trading rules since different stock may be affected by different set of factors. In this paper, we propose a novel trading point forecasting framework that incorporates a bicluster mining technique to discover significant trading patterns, a method to establish the fuzzy rule base, and a fuzzy inference system optimized for trading point prediction. The proposed method (called BM-FM) was tested on several historical stock datasets and the average performance was compared with the conventional buy-and-hold strategy and five previously reported intelligent trading systems. Experimental results demonstrated the superior performance of the proposed trading system. |
| Databáze: | OpenAIRE |
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