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Mathematics
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Advances in Machine Learning Applied to Financial Economics
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Advances in Machine Learning Applied to Financial Economics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 3741

Special Issue Editor

Prof. Dr. Saejoon Kim

E-Mail Website
Guest Editor
Departments of Computer Science and Engineering, and Artificial Intelligence, Sogang University, Seoul 04107, Republic of Korea
Interests: machine learning; financial economics; asset pricing; factor models

Special Issue Information

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.

Keywords

  • 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

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Published Papers (2 papers)

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Research

21 pages, 543 KiB  
Article
Estimating Asset Pricing Models in the Presence of Cross-Sectionally Correlated Pricing Errors
by Hyuksoo Kim and Saejoon Kim
Mathematics 2024, 12(21), 3442; https://doi.org/10.3390/math12213442 - 4 Nov 2024
Viewed by 894
Abstract
In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a [...] Read more.
In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applied to Financial Economics)
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21 pages, 1997 KiB  
Article
Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization
by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma and Shuangzhe Liu
Mathematics 2024, 12(21), 3376; https://doi.org/10.3390/math12213376 - 29 Oct 2024
Viewed by 2509
Abstract
The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper [...] Read more.
The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applied to Financial Economics)
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