Enhancement of stock market forecasting using an improved fundamental analysis-based approach

Yuh Jen Chen, Yuh Min Chen, Chang Lin Lu

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Stock investment is regarded as a high-risk financial activity, in which life savings can be destroyed when investors fail to consider factors in stock price variation or do not master professional knowledge and experiences related to investment. To enhance decision-making quality and profitability for investors, numerous different methods for forecasting stock market prices have appeared. However, these forecasts usually do not appear as expected because of uncertainties in the stock market. Therefore, how to effectively use stock information to help investors make stock investment decisions has become a primary issue in stock investment. Based on the bottom-up approach that considers financial conditions of listed companies, industrial environment, macroeconomics, and financial news respectively, an improved fundamental analysis-based approach for stock market forecasting is developed for selecting optimal stocks from the stock market and predicting their future price trends to provide a reference for investor decisions. This study involves the following tasks: (1) design an improved fundamental analysis-based approach to stock market forecasting; (2) develop techniques related to fundamental analysis-based stock market forecasting, and (3) demonstrate and evaluate the proposed fundamental analysis-based approach to stock market forecasting. The improved fundamental analysis-based approach to stock market forecasting involves techniques such as calculating the weight of financial indicators, evaluating and selecting individual stocks, selecting financial news features, determining stock trading signals based on financial news, and forecasting stock price trend.

Original languageEnglish
Pages (from-to)3735-3757
Number of pages23
JournalSoft Computing
Volume21
Issue number13
DOIs
Publication statusPublished - 2017 Jul 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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