Machine Learning and Computational Methods for Financial Data Analysis

Dear Colleagues,

Recent advances in machine learning, artificial intelligence, and computational modeling have significantly reshaped the landscape of financial data analysis. Modern financial systems generate vast amounts of heterogeneous data, including numerical time series, textual disclosures, news, social media content, and other forms of unstructured information, that challenge traditional analytical frameworks. Emerging AI technologies, such as large language models (LLMs), generative AI, and advanced text-mining techniques, now offer powerful tools for extracting insights from complex and high-dimensional financial data.

This Special Issue aims to bring together cutting-edge research that develops, applies, and evaluates machine learning and computational approaches for financial data analysis. Topics of interest include modeling frameworks for structured and unstructured data, methodological innovations for processing large-scale datasets, and applications in asset pricing, risk management, portfolio optimization, market microstructure, financial forecasting, and algorithmic trading. We particularly welcome contributions that leverage natural language processing, LLMs, or multimodal learning to analyze textual or mixed-format financial information.

Both theoretical advancements and applied studies with real-world implications are encouraged. Review articles that synthesize recent progress or identify open challenges are also welcome. Our goal is to provide a high-quality forum for interdisciplinary research that advances the state of the art in machine learning for modern financial analytics.

Prof. Dr. Xinwei Cao
Guest Editor

Manuscript Submission Information

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