Beyond traditional analysis: Leveraging social media sentiments and market data for nifty50 returns forecasting
Keywords:
Social media, sentiment analysis, deep learning, stock returns, prediction modelAbstract
Researchers across various fields are increasingly focused on the influence of social media networks. In finance and economics, particular attention has been given to the relationship between social media sentiments and stock market returns. This study introduces a robust, high-performance deep learning prediction model, by combining long short-term memory (LSTM) networks with densely connected neural networks (DCNN), to forecast Nifty50 index movements. Notably, this approach integrates data from multiple social media platforms (Twitter, StockTwits, Facebook, and YouTube) for sentiment analysis, combined with market position data—a novel application in this context. Extensive tuning of the model's hyperparameters achieved an accuracy of over 98%, demonstrating the effectiveness of combining social media sentiments with Nifty50 market positions. The resulting model exhibits strong reliability and robustness in forecasting index returns, making it a valuable tool for market forecasting.
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