risk management
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2022 ◽  
Vol 30 (7) ◽  
pp. 0-0

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.


2022 ◽  
Vol 129 ◽  
pp. 37-44
Author(s):  
Valentina Bacciu ◽  
Costantino Sirca ◽  
Donatella Spano

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0

Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise risk management system. Base on the CCER (China Center for Economic Research) database, the early warning model for enterprise financial risk management containing five indices is proposed for enterprises. Through Logistic regression analysis, the design principle of the financial risk management system based on AI (Artificial Intelligence) technology is explained. The proposed system innovatively introduces the AI integrated learning method, optimizes objective function through XGBoost (eXtreme Gradient Boosting) algorithm, and trains the model through BP (Backpropagation) NN (Neural Network). Finally, following comparative analysis, the effectiveness of the proposed method is verified.


2022 ◽  
Vol 30 (7) ◽  
pp. 0-0

At present, most risk management work mainly relies on manpower, and manpower relies on the professional knowledge of relevant skilled workers to discover hidden safety risks in production activities. This article combines relevant big data theories and 4V characteristics to analyze and investigate safety production and big data, study data structure, data source and data type. Using 5W1H scientific big data and applications, this analysis method analyzes the theoretical basis, applications and beneficiaries of big data related to safety production, some of which are application links and important theoretical issues. Secondly, it studies the security risk management model based on big data, proposes a risk management model based on big data, the technical basis of big data and the idea of a three-dimensional model, and applies the systematic space method, which is reflected in three aspects of risk management. In the end, a risk identification model based on big data, a risk assessment classification model, and a risk early warning and pre-control model are defined.


10.1142/q0351 ◽  
2022 ◽  
Author(s):  
Tony Klein ◽  
Sven Loßagk ◽  
Mario Straßberger ◽  
Thomas Walther
Keyword(s):  

2022 ◽  
Vol 303 ◽  
pp. 114126
Author(s):  
Songge Deng ◽  
Peiyi Li ◽  
Yizhao Wu ◽  
Hao Tang ◽  
Shujun Cheng ◽  
...  
Keyword(s):  

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