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Soft Computing

, Volume 22, Issue 4, pp 1295–1312 | Cite as

A novel technical analysis-based method for stock market forecasting

  • Yuh-Jen Chen
  • Yuh-Min Chen
  • Shiang-Ting Tsao
  • Shu-Fan Hsieh
Methodologies and Application

Abstract

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.

Keywords

Stock market forecasting Technical analysis Support vector machine Particle swarm optimization 

Notes

Acknowledgments

The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC100-2410-H-327-003-MY2. Additionally, we deeply appreciate Yu-Ting Luo for her editorial assistance and the editor and reviewers for their constructive comments and suggestions on the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yuh-Jen Chen
    • 1
  • Yuh-Min Chen
    • 2
  • Shiang-Ting Tsao
    • 2
  • Shu-Fan Hsieh
    • 3
  1. 1.Department of Accounting and Information SystemsNational Kaohsiung First University of Science and TechnologyKaohsiungTaiwan, ROC
  2. 2.Institute of Manufacturing Information and SystemsNational Cheng Kung UniversityTainanTaiwan, ROC
  3. 3.Department of Money and BankingNational Kaohsiung First University of Science and TechnologyKaohsiungTaiwan, ROC

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