A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Author:

Vuong Pham Hoang12,Phu Lam Hung1,Van Nguyen Tran Hong3,Duy Le Nhat2,Bao Pham The1,Trinh Tan Dat1ORCID

Affiliation:

1. Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam

2. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

3. Faculty of Finance and Banking, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Abstract

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

Publisher

SAGE Publications

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