Accounting information and stock returns in Vietnam securities market: Machine learning approach

This paper studies the relationship between accounting information reflected in financial statements and stock return in Vietnam Stock Market. The authors propose a research model to define the relationship. Besides, machine learning algorithms are used in researching and forecasting, with data obta...

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Bibliographic Details
Published in:	Contabilidad y negocios Vol. 17; no. 33; pp. 94 - 118
Main Authors:	Hung Dang, Ngoc, Van Vu, Thi Thuy, Le Dao, Thi Nhat
Format:	Journal Article
Language:	English
Published:	Lima Fondo Editorial PUCP 01-01-2022 Pontificia Universidad Católica del Perú
Subjects:	Accounting accounting information accrual quality Algorithms aprendizado de máquina aprendizaje automático decision tree Artificial intelligence calidad devengada decision tree Decision trees Dividends Earnings per share elastic net Financial ratios Financial statements gradient boosting información de cuenta informação contábil Investments Literature reviews Machine learning Profitability Profits qualidade da provisão Rates of return Ratios rentabilidad de acciones retorno de ações Securities markets Stock exchanges Stock prices stock return Volatility Vietnam
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Summary:	This paper studies the relationship between accounting information reflected in financial statements and stock return in Vietnam Stock Market. The authors propose a research model to define the relationship. Besides, machine learning algorithms are used in researching and forecasting, with data obtained from observed firms during the period from 2009 to 2020. Research results show that gradient boosting algorithm has the best self-reporting performance and finan-cial ratios also have great impact on stock returns, including operating income growth, stock earnings volatility, dividend yield, earnings before tax-to-equity ratio, cash holding ratio, and accrual quality. Based on the research results, the authors make some recommendations for investors, firms, and policy makers.
ISSN:	1992-1896 2221-724X 2221-724X
DOI:	10.18800/contabilidad.202201.004