Deep Learning Tools for Predicting Stock Market Movements

13Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction

Renuka Sharma, Archana Goel^* and Kiran Mehta

Chitkara Business School, Chitkara University, Punjab, India

Abstract

The stock market substantially influences every domain in our economy, and each investment in this market endeavors to optimize gains while diminishing associated risks. As a result, investors actively participate in the market to capitalize on their investment horizon. Notwithstanding, the forecasting of stock markets is dingy due to various uncertainties, including general economic conditions at both domestic and international levels. Consequently, considerable research has focused on stock market prediction using technical analysis, fundamental analysis, or a mix of both, and machine learning algorithms. The goal of the current paper is to perform a thorough systematic literature review and bibliometric analysis of 89 research works related to fundamental analysis and stock market prediction for 2002–2023. From the bibliometric analysis, the paper identified the highly contributing authors, institutes, countries, sources, keywords, and articles. The intellectual structure and future research opportunities were visualized using bibliographic coupling. Besides this, the study also explored various algorithms used in machine learning, feature selection criteria, the ratio of training and testing datasets, accuracy, performance metrics, technical indicators, ...