Explainable stock prices prediction from financial news articles using sentiment analysis.

Area of Science:

• * Computational Finance
• * Machine Learning
• * Natural Language Processing

Background:

• * Stock market volatility is influenced by media-driven sentiments, complicating accurate price prediction.
• * Traditional technical analysis and fundamental understanding of market trends are crucial but often insufficient.
• * Achieving high precision in stock price prediction remains a significant challenge in financial research.

Discussion:

• * This research proposes a hybrid approach integrating advanced machine learning with deep learning (LSTM) for stock price forecasting.
• * The model incorporates sentiment analysis derived from news headlines, acknowledging their impact on trader behavior.
• * Combining historical stock data with sentiment analysis aims to create a more robust predictive model.

Key Insights:

• * Machine learning and LSTM networks are employed to capture temporal dependencies in stock data.
• * Sentiment analysis from news headlines is integrated as a key feature to improve prediction accuracy.
• * The proposed model demonstrates a potential for enhanced stock price prediction by fusing technical and sentiment data.

Outlook:

• * Further research can explore other deep learning architectures and sentiment analysis techniques.
• * Real-time implementation and backtesting on diverse market datasets are recommended.
• * Investigating the impact of different news sources and sentiment quantification methods could refine the model.