Analyzing Effects of News on Manipulated Stock Price using Large Language Model and Statistical Tests: Evidence in Vietnam Market

Abstract

In emerging markets such as Vietnam, where retail investors dominate and regulatory enforcement remains limited, financial news plays a pivotal role in shaping stock price dynamics. This study examines the relationship between financial news sentiment and stock price manipulation using the case of the FLC Group. PhoBERT, a pre-trained Vietnamese large language model, is employed to classify financial news into positive, neutral, and negative categories. The resulting daily sentiment scores are aggregated and analyzed alongside stock price data from 2018 to 2023 through Pearson correlation, Granger causality, and Threshold Vector Autoregression tests. The empirical results reveal significant correlations and causal relationships between sentiment and stock prices, though their strength weakens after 2022, reflecting shifts in investor behavior and market transparency following regulatory events. The nonlinear analysis further demonstrates that sentiment-price interactions are regime-dependent, with mean reversion in pessimistic periods and momentum effects in optimistic ones. These findings highlight the vital role of news sentiment in influencing and potentially manipulating prices in emerging markets, providing insights for investors, policymakers, and regulators.