Advances in Machine Learning Applied to Financial Economics

Dear Colleagues,

Machine learning is ubiquitous in today’s society from web searches, object identification and text/speech translation to more sophisticated applications using generative artificial intelligence such as ChatGTP. Its far-reaching effect has influenced how financial mathematicians and economists conduct research complementing classical statistical approaches to the analysis of cross section and time series of returns. Machine learning applied to financial economics has become a hot topic in both academia and asset management industry reflected by the surge in the number of research articles on this topic, ranging from identification of patterns in returns and volatility to learning the efficient frontier, being published by both groups of participants. Recently, it has allowed for the establishment of improved asset pricing models, portfolio optimization and risk management techniques.

In light of recent attention to this topic, in this Special Issue, we seek advancements of machine learning techniques applied to the field of financial economics.  Contributions to the areas of, but not limited to, estimation of asset pricing models, financial decision making under uncertainty with economic and financial models, identification of latent factors, portfolio optimization and risk management, statistical methods for financial market data, and time series prediction all employing various forms of machine learning are solicited. We pay particular interest to how machine learning techniques are incorporated to serve as new methods to solve problems in finance.

Prof. Dr. Saejoon Kim
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• machine learning
• deep learning
• generative artificial intelligence
• representation learning
• asset pricing models and asset price dynamics
• arbitrage pricing
• option pricing
• equilibrium-based pricing
• high-frequency trading
• optimal asset allocation and portfolios
• factor models
• latent factors
• time series prediction
• risk management
• value at risk
• volatility estimation
• cross section of returns