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Machine Learning for Empirical Finance
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Machine Learning for Empirical Finance

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 (28 February 2022) | Viewed by 3669

Special Issue Editor

Prof. Dr. Jose Olmo

E-Mail Website
Guest Editor
Department of Economic Analysis, Universidad de Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain;
Department of Economics, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
Interests: financial economics; machine learning; financial econometrics; portfolio theory

Special Issue Information

Dear Colleagues,

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
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine learning 
  • Shrinkage methods 
  • Big data applications 
  • Empirical finance 
  • Portfolio theory

Published Papers (1 paper)

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Review

22 pages, 1296 KiB  
Review
Neural Network Models for Empirical Finance
by Hector F. Calvo-Pardo, Tullio Mancini and Jose Olmo
J. Risk Financial Manag. 2020, 13(11), 265; https://doi.org/10.3390/jrfm13110265 - 30 Oct 2020
Cited by 4 | Viewed by 3153
Abstract
This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to [...] Read more.
This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context. Full article
(This article belongs to the Special Issue Machine Learning for Empirical Finance)
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