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Erschienen in: World Wide Web 6/2018

21.02.2018

Discovery of trading points based on Bayesian modeling of trading rules

verfasst von: Qinghua Huang, Zhoufan Kong, Yanshan Li, Jie Yang, Xuelong Li

Erschienen in: World Wide Web | Ausgabe 6/2018

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Abstract

Mining hidden patterns with different technical indicators from the historical financial data has been regarded as an efficient way to determine the trading decisions in the financial market. Technical analysis has shown that a number of specific combinations of technical indicators could be treated as trading patterns for forecasting efficient trading directions. However, it is a challenging assignment to discover those combinations. In this paper, we innovatively propose to use a biclustering algorithm to detect the trading patterns. The discovered trading patterns are then utilized to forecast the market movement based on the Naive Bayesian algorithm. Finally, the Adaboost algorithm is applied to improve the accuracy of the forecasts. The proposed method was implemented on seven historical stock datasets and the average performance was compared with that of four existing algorithms. Experimental results demonstrated that the proposed algorithm outperforms the other four algorithms and can provide a valuable reference in the financial investments.
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Metadaten
Titel
Discovery of trading points based on Bayesian modeling of trading rules
verfasst von
Qinghua Huang
Zhoufan Kong
Yanshan Li
Jie Yang
Xuelong Li
Publikationsdatum
21.02.2018
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 6/2018
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0534-9

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