Quantitative Finance with Mathematical Modelling

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1134

Special Issue Editors


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Guest Editor
School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
Interests: computational finance; computational intelligence; operation research; machine learning; reinforcement learning

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Guest Editor
Business School of Ningbo University, Ningbo, China
Interests: artificial intelligence finance; blockchain finance; big data finance; volatility research; financial market analysis; time series analysis
School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
Interests: evolutionary game theory; fuzzy logic; economic risk and benefit analysis; hyper-heuristics

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Guest Editor
Artificial Intelligence and Optimisation Research Group, School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
Interests: application of statistical models and machine learning to consumer credit risk, with particular interest in model risk; dynamic survival models and expected loss estimation; optimization with non-parametric learning algorithms; reliable prediction through conformal predictors

Special Issue Information

Dear Colleagues,

Mathematical methods and models are playing crucial roles in the field of quantitative finance to provide a crystal understanding of financial asset price behavior. Nevertheless, the application of mathematical methods and models in real financial market trading is high distinguishing from the mathematical theories themselves. As a consequence, the effectiveness of the mathematical methods and models work in reality will shed the new insights in future research on quantitative finance.

The purpose of this Special Issue is to contribute to close this gap by providing a collection of articles that illustrate the applicability of novel mathematical methods and to a wide range of topics in quantitative finance, including, among others, quantitative trading, algorithm trading, portfolio management, risk management, portfolio optimization, relationships among financial markets and among financial and commodity markets, green finance and financial risks.

Dr. Tianxiang Cui
Dr. Shusheng Ding
Dr. Jiawei Li
Dr. Anthony Graham Bellotti
Guest Editors

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Keywords

  • quantitative finance
  • risk management
  • portfolio management
  • time series analysis
  • Bayesian analysis
  • quantitative trading
  • algorithm trading
  • financial risk
  • portfolio optimization
  • game theory in finance

Published Papers (1 paper)

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Research

27 pages, 1873 KiB  
Article
Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction
by Zixue Zhao, Tianxiang Cui, Shusheng Ding, Jiawei Li and Anthony Graham Bellotti
Mathematics 2024, 12(5), 701; https://doi.org/10.3390/math12050701 - 28 Feb 2024
Cited by 1 | Viewed by 821
Abstract
Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) [...] Read more.
Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction. Full article
(This article belongs to the Special Issue Quantitative Finance with Mathematical Modelling)
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