Major Issues in High-Frequency Financial Data Analysis: A Survey of Solutions

Lu Zhang and Lei Hua ()
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Lu Zhang: Department of Statistics & Actuarial Science, Northern Illinois University, DeKalb, IL 60115, USA
Lei Hua: Department of Statistics & Actuarial Science, Northern Illinois University, DeKalb, IL 60115, USA

Mathematics, 2025, vol. 13, issue 3, 1-40

Abstract: We review recent articles that focus on the main issues identified in high-frequency financial data analysis. The issues to be addressed include nonstationarity, low signal-to-noise ratios, asynchronous data, imbalanced data, and intraday seasonality. We focus on the research articles and survey papers published since 2020 on recent developments and new ideas that address the issues, while commonly used approaches in the literature are also reviewed. The methods for addressing the issues are mainly classified into two groups: data preprocessing methods and quantitative methods. The latter include various statistical, econometric, and machine learning methods. We also provide easy-to-read charts and tables to summarize all the surveyed methods and articles.

Keywords: nonstationarity; signal-to-noise ratios; asynchronous data; imbalanced data; intraday seasonality; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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