Integrating market sentiments for stock price prediction: A comparative study of Bi-LSTM and multilayer perceptions

Abstract

This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory (Bi-LSTM) networks and multilayer perceptrons (MLPs) developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentiment features diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between market sentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations.

Keywords

• Bi-LSTM
• Multilayer perceptrons
• Sentiment analysis
• Machine learning

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