A novel technical analysis-based method for stock market forecasting

Autor: Yuh-Jen Chen, Yuh-Min Chen, Shu Fan Hsieh, Shiang Ting Tsao
Rok vydání: 2016
Předmět:
Zdroj: Soft Computing. 22:1295-1312
ISSN: 1433-7479
1432-7643
DOI: 10.1007/s00500-016-2417-2
Popis: Owing to the dynamic changes of the stock market and numerous influences on stock prices, assessing stock prices has become increasingly difficult. Furthermore, when dealing with information on stocks, people tend to amplify the importance of available and self-correlative information, a habit that runs contrary to objective and reasonable investment decision-making. Therefore, how to use effective stock information to assist investors in making stock investment decisions is a major topic in stock investment. This study develops a novel technical analysis method for stock market forecasting to effectively promote forecasting accuracy, which can help investors to increase their decision support quality and profitability. Specifically, this study involves the following tasks: (1) design a technical analysis-based stock market forecasting process, (2) develop techniques related to technical analysis-based stock market forecasting, and (3) demonstrate and evaluate the developed technical analysis-based method for stock market forecasting. In developing techniques associated with the technical analysis-based stock market forecasting method, the techniques involve trend-based stock classification, adaptive stock market indicator selection, and stock market trading signal forecasting.
Databáze: OpenAIRE
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