Recurrent Neural for Time Series Prediction in Finance

Overcoming risk is crucial when predicting financial time series, such as stock prices, due to market volatility influenced by factors like economic reports, political events, and investor sentiment. Linear models like ARIMA handle linear dependencies well but fail to capture nonlinear relationships, which are key in financial forecasting. While Long Short-Term Memory (LSTM) models excel at capturing temporal dependencies, they struggle with identifying significant market events, potentially contaminating predictions. This paper introduces an Attention RNN with Sentiment Integration, which combines Recurrent Neural Networks (RNN) with an attention mechanism for sentiment analysis of financial news and social media. The model considers relevant time steps and market sentiment, improving context-awareness. Results demonstrate that this hybrid approach outperforms traditional methods by providing a more accurate and flexible forecasting model, addressing the complexities of financial markets. Additionally, sentiment data helps the model capture both stock-specific and overall market sentiment, particularly during periods of heightened uncertainty or volatility, offering a more comprehensive approach to stock price prediction. Keywords: Risk, Financial Time Series, ARIMA, LSTM, Attention RNN, Sentiment Analysis, Market Sentiment, Stock Prices, Forecasting, Volatility.