Applications of high-frequency data in finance: A bibliometric literature review

Abstract

This study aims to provide a bibliometric literature review (BLR) on the applications of high-frequency data in finance. To the best of our knowledge, this is the first BLR on this topic. It aims to map the evolution of the literature, identifying the leading sources of knowledge in terms of the most influential journals, articles, and authors. It also provides a chronological development of the conceptual and intellectual structures of the networks in this topical research area. Using the Scopus database, the study identifies 2920 articles on the application of high-frequency intraday data in finance. These had been published in 393 journals during the period from 1977 to 2019. A thorough content analysis of the 100 most influential papers (ranked based on average citations per year) is also provided concerning research attributes in terms of datasets, asset classes, country of analysis and the major themes and sub-themes of these papers. The Journal of Banking and Finance is the leading journal in terms of the number of publications, whereas the Journal of Finance is the leading journal in terms of citations received on this topic. Tim Bollerslev is the leading author in this area in terms of the total number of publications (36), total citations (7241) and h-index (30). The most cited article in terms of total citations and average citations per year is Andersen, Bollerslev, Diebold, and Labys (2003) titled “Modeling and forecasting realized volatility”, which has appeared in Econometrica. The majority of the top 100 surveyed papers are empirical (66%). Volatility modeling as a major theme is the front runner with 29% of the surveyed papers. The theme “Volatility modeling” has most often been studied with Realized Volatility. The Trade and Quote (TAQ) database and 5-minute interval data appear to be the most favored choices in terms of data usage in high-frequency finance research. 56% of the surveyed papers have used the data on stocks, with NYSE stocks being the most popular, while US financial markets are the most commonly studied markets (65%).

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Stored only when both papers are in the corpus — 37 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.