Machine Learning Econometrics in Asset Pricing
A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 3413
Special Issue Editor
Interests: econometrics; financial econometrics; risk management; financial analysis; international trade & development; agricultural marketing; prices
Special Issue Information
Dear Colleagues,
The prediction of stock returns has been a complex problem of frequent research interest in quantitative finance. Complexity is enhanced by the vast number of determinants (predictors) of stock returns and the strength of noise-to-signal ratio of predictors across firms. Financial models of stock returns use a small number of predictors, leaving out vast amounts of financial information that contributes to explaining variations in stock returns. The explosive growth in financial data has allowed analysts, investors, and researchers to use methods such as machine learning (ML) to capture features in high-dimensional data. Capital asset pricing models (CAPM) and machine learning cointegrate well, with CAPM providing a theory-consistent structure to predict asset returns while machine learning captures data features that improve model specification and predictability.
Prof. Dr. Hector O. Zapata
Guest Editor
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Keywords
- econometrics
- machine learning
- asset pricing
- computational methods
- financial shocks
- time series
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