Financial Risk Early Warning Model of Listed Companies Under Rough Set Theory Using BPNN

Cheng-ai, LI; Jin, Keyan; Zhong, Ziqi; Zhou, Ping; Tang, Kunzhi

doi:10.4018/jgim.300742

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Du & Shu (2022) focused on using Deep Learning and Bionic Algorithm to explore of financial market credit scoring and risk management. The results of in-depth research in this area include quality of big data marketing analytics (Haverila et al, 2022); financial risk early warning model (Li et al, 2022); intelligent employee retention system (Srivastava et al,2021); analyze the market risks of A+H shares by BPNN algorithm (Wu et al,2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Stock Trading

Zhao,

Deng,

et al. 2024

Journal of Organizational and End User Computing

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In this study, the authors explore how financial institutions make decisions about stock trading strategies in a rapidly changing and complex environment. These decisions are made with limited, often inconsistent information and depend on the current and future strategies of both the institution itself and its competitors. They develop a dynamic game model that factors in this imperfect information and the evolving nature of decision-making. To model reward transitions, they utilize a combination of t-Copula simulation of a non-stationary Markov chain, probabilistic fuzzy regression, and chaos optimization algorithms. They then apply deep q-network, a method from deep reinforcement learning, to ensure the effectiveness of the chosen strategy during ongoing decision-making. The approach is significant for both researchers across fields and practical professionals in the finance industry.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Stock Trading

Zhao,

Deng,

et al. 2024

Journal of Organizational and End User Computing

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“…Compared with traditional forecasting methods and machine learning approaches, deep learning can better learn, train on, and predict time series data. As a result, deep learning has been widely used by scholars across various fields (Li et al, 2021;Du & Shu, 2022;Liang et al, 2022). The application of deep learning to forecast the overall risk spillover value in energy finance is a novel analytical approach developed in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Financial Risk Management in China Using Complex Network Analysis

Fang,

Deng,

et al. 2023

Journal of Organizational and End User Computing

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Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.

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“…Financial frauds are difficult to detect manually since the instances of corporate frauds are always concealed (Zakolyukina, 2018; Amiram et al, 2020), particularly in the case of that fraud methods are getting diversified and complicated as corporate business expands and innovates continuously (Li et al, 2022; Yang & Wu, 2022). Fortunately, machine learning develops rapidly in recent years, providing efficient approaches to exploring the relationship between financial risks and the growing financial data (Du & Shu, 2022; Li et al, 2022; Wu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An intelligent detecting model for financial frauds in Chinese A‐share market

Sun,

Zeng,

et al. 2024

Economics & Politics

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Financial frauds can cause serious damage to financial markets but are hard to detect manually. In this study, we develop an intelligent detecting model to efficiently identify financial frauds by using XGBoost on raw financial data items in corporation financial statements. With listed companies in Chinese A‐share Market taken as samples, empirical results reveal that the proposed model works better than traditional models by a large margin in detecting fraud. Notably, the proposed model exhibits superior performance when used together with raw financial data items than with financial indicators. Moreover, the proposed model remains robust on outperformance in fraud detection when serial fraud cases are recoded, test periods are altered, more raw financial data are input, as well as other machine learning models–the AdaBoost and SVM–are selected as benchmark models. Our study enriches the application of machine learning in finance sector, and highlights the economic significance of raw financial data as the financial system's most fundamental components.

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Financial Risk Early Warning Model of Listed Companies Under Rough Set Theory Using BPNN

Cited by 7 publications

References 33 publications

Deep Reinforcement Learning for Adaptive Stock Trading

Deep Reinforcement Learning for Adaptive Stock Trading

Energy Financial Risk Management in China Using Complex Network Analysis

An intelligent detecting model for financial frauds in Chinese A‐share market

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