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

45 Pages Posted: 20 May 2024

See all articles by Lu Zhang

Lu Zhang

Northern Illinois University

Lei Hua

Northern Illinois University

Date Written: May 20, 2024

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 published since 2020 on recent developments and relatively 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: methods for data preprocessing and quantitative methods. The latter include various statistical, machine learning, and econometric 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

JEL Classification: G1, C1

Suggested Citation

Zhang, Lu and Hua, Lei, Major Issues in High-frequency Financial Data Analysis: A Survey of Solutions (May 20, 2024). Available at SSRN: https://ssrn.com/abstract=4834362 or http://dx.doi.org/10.2139/ssrn.4834362

Lu Zhang

Northern Illinois University ( email )

1425 W. Lincoln Hwy
Dekalb, IL 60115-2828
United States

Lei Hua (Contact Author)

Northern Illinois University ( email )

1425 W. Lincoln Hwy
Dekalb, IL 60115-2828
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
391
Abstract Views
766
Rank
144,645
PlumX Metrics