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.