Financial Time Series

A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges ArXiv ID: 2406.11903 “View on arXiv” Authors: Unknown Abstract Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector. ...

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors ArXiv ID: 2401.05414 “View on arXiv” Authors: Unknown Abstract Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved. In this paper, we follow a causal perspective to systematically look into these three demons in finance. Specifically, we reexamine these issues in the context of causality, which gives rise to a novel and inspiring understanding of how the issues can be addressed. Following this perspective, we provide systematic solutions to these problems, which hopefully would serve as a foundation for future research in the area. ...

Copula-based deviation measure of cointegrated financial assets ArXiv ID: 2312.02081 “View on arXiv” Authors: Unknown Abstract This study outlines a comprehensive methodology utilizing copulas to discern inconsistencies in the behavior exhibited by pairs of financial assets. It introduces a robust approach to establishing the interrelationship between the returns of these assets, exploring potential measures of dependence among the stochastic variables represented by these returns. Special emphasis is placed on scrutinizing the traditional measure of dependence, namely the correlation coefficient, delineating its limitations. Furthermore, the study articulates an alternative methodology that offers enhanced stability and informativeness in appraising the relationship between financial instrument returns. ...