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Title: | Machine learning in finance and economics: comparative analysis of machine learning and econometric approaches in forecasting Vietnamese stock market | Authors: | Nguyen Quoc Trung | Keywords: | Machine learning;Deep Learning;Financial Econometrics;Time Series Econometrics | Issue Date: | 2023 | Abstract: | The Vietnamese stock market has undergone a remarkable transformation in recent years, marked by significant expansion, notable fluctuations, and increased global recognition. This evolution has piqued the interest of a wide range of stakeholders, including local and international investors looking for opportunities, policymakers concerned with market stability and financial development, and researchers attempting to unravel the complexities of this burgeoning financial landscape. In response to the dynamic and evolving nature of the Vietnamese stock market, this thesis undertakes an extensive and multifaceted exploration. It centers on the proactive pursuit of accurate stock market performance prediction in Vietnam, a pursuit that is poised to address the growing demand for insight and foresight. The core methodology driving this research involves an in-depth comparative analysis of two distinctive, yet complementary, forecasting approaches: established econometric models and state-of-the-art deep learning techniques. This multifaceted approach recognizes the many facets of stock market dynamics in Vietnam, with the goal of not only anticipating future movements but also elucidating the underlying forces at work in this thriving economic ecosystem |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1763 | Rights: | Attribution-ShareAlike 3.0 United States |
Appears in Collections: | Computer Science (CS) |
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