Enhancing Bitcoin Forecast Accuracy by Integrating AI, Sentiment Analysis, and Financial Models

Abstract

This study explores the application of advanced AI models—Long Short-Term Memory (LSTM), Prophet, and SARIMAX—in predicting Bitcoin prices. It assesses the impact of incorporating sentiment analysis from sources like Twitter and Yahoo, processed through Large Language Models. The research aims to understand how sentiment analysis, reflecting investor sentiments and market perceptions, can enhance the accuracy of these forecasting models. The paper investigates the potential synergies and challenges in improving predictive performance by integrating qualitative sentiment data with quantitative financial models. The analysis compares the models’ accuracy with and without sentiment inputs, utilizing historical Bitcoin price data and sentiment indicators. This study's motivation is the growing recognition of investor sentiment's impact on market fluctuations, particularly in the highly speculative and sentiment-driven cryptocurrency markets. While robust in handling quantitative data, many studies claim that traditional financial models often fail to incorporate market sentiments. This paper also contributes to financial forecasting literature by offering insights into the benefits and complexities of combining traditional econometric models with sentiment analysis, providing a unique understanding of market dynamics influenced by investor behavior. The findings suggest that sentiment analysis can significantly refine forecasting accuracy, underscoring the importance of incorporating human sentiment and market perceptions in predictive models.