Network-based Early Warning System to Predict Financial Crisis

Dastkhan, Hossein

doi:10.2139/ssrn.3314820

Cited by 5 publications

(5 citation statements)

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“…And this weaker classifier is combined to establish strong classifiers using adaboost technique. In this Dastkhan (2021), they employed the forward looking condition value at risk (COVaR) as a market based systematic risk metric. A network depiction of asset exposure is introduced according to the value of CoVaR.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Pustokhina

Pustokhin

Mohanty³

et al. 2021

Ann Oper Res

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Financial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Pustokhina

Pustokhin

Mohanty³

et al. 2021

Ann Oper Res

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“…e influencing factors of risk are identified through the above process analysis of the supply chain finance mode of the commercial circulation industry. en, referring to the related literature on the construction of risk early warning index systems [35,36], we eliminated some difficult quantitative indicators. Finally, the core enterprise financing business qualifications, environmental conditions, the characteristics of the qualification, and the supply chain operating conditions are used to establish a risk early-warning index system that contains three levels and 29 indicators.…”

Section: Basic Structure Of the Index Systemmentioning

confidence: 99%

A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning

Yin

Qin²,

Hou

et al. 2022

Computational Intelligence and Neuroscience

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At present, there are widespread financing difficulties in China's trade circulation industry. Supply chain finance can provide financing for small- and medium-sized enterprises in China’s trade circulation industry, but it will produce financing risks such as credit risks. It is necessary to analyze the causes of the risks in the supply chain finance of the trade circulation industry and measure these risks by establishing a credit risk assessment system. In this article, a supply chain financial risk early warning index system is established, including 4 first-level indicators and 29 third-level indicators. Then, on the basis of the supply chain financial risk early warning index system, combined with the method of convolution neural network, the supply chain financial risk early warning model of trade circulation industry is constructed, and the evaluation index is measured by the method of principal component analysis. Finally, the relevant data of trade circulation enterprises are selected to make an empirical analysis of the model. The conclusion shows that the supply chain financial risk early warning model and risk control measures established in this article have certain reference value for the commercial circulation industry to carry out supply chain finance. It also provides guidance for trade circulation enterprises to deal with supply chain financial risks effectively.

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“…e implementation of those helped policymakers develops appropriate macro and micro policies to prevent systemic risks [29].…”

Section: Financial Warning Systemsmentioning

confidence: 99%

Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

2021

Discrete Dynamics in Nature and Society

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Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.

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Network-based Early Warning System to Predict Financial Crisis

Cited by 5 publications

References 0 publications

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning

Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

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