Mathematical Methods and Analysis in Risk and Financial Management

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 597

Special Issue Editor


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Guest Editor
Department of IEOR, Columbia University, New York, NY 10027, USA
Interests: machine learning; deep learning; financial engineering; optimization

Special Issue Information

Dear Colleagues,

This Special Issue on “Mathematical Methods and Analysis in Risk and Financial Management” aims to provide a comprehensive overview of the latest advances in machine learning methods and their applications in risk and financial management. The focus of this Special Issue is on the development and implementation of these models for the analysis of financial and economic risk in various financial and economic systems that cover credit risk, market risk, operational risk, and liquidity risk, among others.

Keywords related to this topic include data processing, machine learning, natural language processing, AI algorithms, expected loss, credit risk, market risk, operational risk, liquidity risk, financial and economic systems, mathematical models, computational tools, and financial engineering.

This Special Issue will be of great interest to researchers, practitioners, and scholars in the fields of finance, mathematics, economics, and other related fields who are concerned with the development and application of innovative mathematical methods for the analysis of financial and economic risk. It is expected to provide valuable insights into risk and financial management by showcasing the latest research, developments, and trends in this field.

Prof. Dr. Ali Hirsa
Guest Editor

Manuscript Submission Information

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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.

Published Papers (1 paper)

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Research

19 pages, 4148 KiB  
Article
Passive Aggressive Ensemble for Online Portfolio Selection
by Kailin Xie, Jianfei Yin, Hengyong Yu, Hong Fu and Ying Chu
Mathematics 2024, 12(7), 956; https://doi.org/10.3390/math12070956 - 23 Mar 2024
Viewed by 384
Abstract
Developing effective trend estimators is the main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield [...] Read more.
Developing effective trend estimators is the main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield the maximum final cumulative wealth in online portfolio selection tasks. This paper studies an online ensemble approach for online portfolio selection by leveraging the strengths of multiple trend estimators. Specifically, a return-based loss function and a cross-entropy-based loss function are first designed to evaluate the adaptiveness of different trend estimators in a financial environment. On this basis, a passive aggressive ensemble model is proposed to weigh these trend estimators within a unit simplex according to their adaptiveness. Extensive experiments are conducted on benchmark datasets from various real-world stock markets to evaluate their performance. The results show that the proposed strategy achieves state-of-the-art performance, including efficiency and cumulative return. Full article
(This article belongs to the Special Issue Mathematical Methods and Analysis in Risk and Financial Management)
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