Use of Heterogeneous Data for Forecasting of Stock Market Movement Using Social Media and Financial News Headlines
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Abstract
The stock market holds a significant role in shaping a nation's economic landscape and impacting the prosperity of businesses. It goes hand in hand with the country's development, closely mirroring market behaviour. While global and societal dynamics exert their influence, technological advancements stand as the primary driving force behind market trends. The fusion of social media and financial news headlines provides a valuable lens into the collective sentiment of the public. This study aims to craft an LSTM model for predicting stock volatility movements. This research delves into various facets of the stock market domain, introducing diverse inputs for comparison against the original market trends. These inputs comprise a rich tapestry of data, including information from social media networks and news headlines sourced from official publications. The goal is to gauge public sentiment as a potent factor for more precise predictions, diverging from the conventional transaction-based stock price forecasts. The outcomes have been promising. By leveraging public sentiment analysis, overall errors have been reduced, with a 43.86% drop in Mean Absolute Error (MAE) and an impressive 50.93% decrease in Root Mean Square Error (RMSE) compared to without sentimental analysis. These results signal a step forward in enhancing the accuracy of stock market predictions.
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