Predict Market Fluctuations Based on the TSI and the Sentiment of Financial Video News Sites via Machine Learning.

Scientists have long been interested in forecasting stock market fluctuations. Traditional data like financial textual news, stock prices, and comments are simply no longer sufficient because they don't provide a comprehensive picture. In this study, the efficacy of using financial video news stories versus the use of conventional text news stories to forecast the stock market is examined. We used the Granger causality test to evaluate the robustness of the causal connection between share prices, text news sentiment, video news sentiments, and the Twitter sentiment index.Several models for sentiment analysis of S&P 500 stock were assessed using LR, SVM, LSTM, ATT-LSTM, and CNN models. This study is distinctive because it compares the use of financial video news stories, conventional text news stories, and the Twitter Sentiment Index to forecast stock market movements. The experimental results suggest that there is a stronger causal connection between video news sentiment and stock market fluctuation compared to conventional text news sentiments. The result shows that we can more accurately predict market changes using video news than we can with traditional news.