Asset Price Prediction

BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges ArXiv ID: 2411.06076 “View on arXiv” Authors: Unknown Abstract This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies. ...

Price predictability in limit order book with deep learning model ArXiv ID: 2409.14157 “View on arXiv” Authors: Unknown Abstract This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance. ...

Social Media Emotions and Market Behavior ArXiv ID: 2404.03792 “View on arXiv” Authors: Unknown Abstract I explore the relationship between investor emotions expressed on social media and asset prices. The field has seen a proliferation of models aimed at extracting firm-level sentiment from social media data, though the behavior of these models often remains uncertain. Against this backdrop, my study employs EmTract, an open-source emotion model, to test whether the emotional responses identified on social media platforms align with expectations derived from controlled laboratory settings. This step is crucial in validating the reliability of digital platforms in reflecting genuine investor sentiment. My findings reveal that firm-specific investor emotions behave similarly to lab experiments and can forecast daily asset price movements. These impacts are larger when liquidity is lower or short interest is higher. My findings on the persistent influence of sadness on subsequent returns, along with the insignificance of the one-dimensional valence metric, underscores the importance of dissecting emotional states. This approach allows for a deeper and more accurate understanding of the intricate ways in which investor sentiments drive market movements. ...