Special Issue "Machine Learning for Empirical Finance"
Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 2368
Department of Economics, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
Interests: financial economics; machine learning; financial econometrics; portfolio theory
The application of statistical science to empirical finance has changed a great deal in the past ten years in response to technological advances and, in particular, to the development of machine learning methods. Financial markets are awash with big and complicated data, and researchers are trying to make sense out of it. Whereas traditionally scientists fit a few statistical models by hand, now they use sophisticated computational tools to search through a large number of models, looking for meaningful patterns and accurate predictions. Standard statistical methods for modelling financial data have been extended by machine learning models in many ways. Empirical finance models based on machine learning techniques now allow for more predictors than observations, incorporate non-linear relationships, accommodate interactions between predictors and, the presence of strong correlations. One of the main advantages of these novel models is the gain in predictive performance compared to standard statistical models and the ease of manipulation due to the availability of toolboxes and off-the-self routines that make their implementation straightforward even in large dimensions. The aim of this Special Issue is to obtain a deeper insight into these methods and their potential for applications in all fields of empirical finance.
Prof. Dr. Jose Olmo
Manuscript Submission Information
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- Machine learning
- Shrinkage methods
- Big data applications
- Empirical finance
- Portfolio theory