Abstract
The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six articles on machine learning, five articles based on traditional econometric analysis, and five articles on emerging markets. The large share of articles on the application of machine learning is in line with recent trends in finance research. This special issue provides a state-of-the-art overview of empirical finance from economic, financial, and technical points of view.
Highlights
This research area has experienced remarkable progress due to the development of information technology. This Special Issue focuses on the broad topic of empirical finance and includes many studies using financial data
The Special Issue is mainly divided into three types of analysis: the application of machine learning based on artificial intelligence, the application of the traditional econometric approach, and the analysis of emerging markets
Tivnan et al (2018) used trading data and quote data to provide various measures of Securities Information Processor (SIP) latency related to high-speed data feeds between exchanges
Summary
This Special Issue focuses on the broad topic of empirical finance and includes many studies using financial data. The Special Issue is mainly divided into three types of analysis: the application of machine learning based on artificial intelligence, the application of the traditional econometric approach, and the analysis of emerging markets. The first group includes Wang et al (2018); Liu et al (2018); Vezeris et al (2018); Xu et al (2018); Ptak-Chmielewska (2019) and Hamori et al (2018).
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