Investment risk model based on intelligent fuzzy neural network and VaR

Zhang, Jiboning

doi:10.1016/j.cam.2019.112707

Cited by 26 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are three advantages of applying fuzzy NN to the investment risk assessment system: (a) FNN has strong parallel processing mechanisms; (b) it learns with the change in environment and can create its own rules; and (c) since it is a nonlinear modeling process, it can build its models quickly. In assessing the risk of crowdfunding platforms in China, a study found that when the sample size is small, the learning and training of Backpropagation NN is not accurate enough, while FNN can get more accurate training results (Zhang, 2020).…”

Section: The Tasks Of Ai In Finance and Financial Marketsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence techniques in finance and financial markets: A survey of the literature

2021

View full text Add to dashboard Cite

Both academic and nonacademic literature is evolving following the oscillating development of artificial intelligence (AI) and computing power's evolution in their application to finance and financial markets. The limits to economic growth encountered after the financial crisis and, successively, the recent pandemic bursts have posed new challenges to AI-related technologies. The surveyed publications have given hope in harvesting improved efficiency, new data, information, advisory and management services, risk mitigation, and some unanswered questions regarding negative impacts on sustainable growth and increasing economic welfare.

show abstract

Section: The Tasks Of Ai In Finance and Financial Marketsmentioning

confidence: 99%

“…Another study also found that fuzzy logic NNs (FNN) work better than backpropagation NNs. The difference between the two kinds of NNs is that knowledge acquisition is from experts in the fuzzy logic case but realized through algorithms in the latter case; the rules of (Zhang, 2020).…”

Section: Credit Scoring and Ratingmentioning

confidence: 99%

Artificial intelligence techniques in finance and financial markets: A survey of the literature

2021

View full text Add to dashboard Cite

show abstract

“…The error between the final output of the output layer and the target value can be written as equation (11).…”

Section: Risk Early Warning Model Of Internet Finance Based On Bpnnmentioning

confidence: 99%

“…The combination of big data technology and the innovative correlation analysis of various algorithms has made risk management more digitized and informationalized [10]. Zhang et al (2020) established a financial investment risk model based on intelligent fuzzy neural network [11]. Teles et al (2020) explored the credit risk prediction based on artificial neural network (ANN) and Bayesian network models [12].…”

Section: Introductionmentioning

confidence: 99%

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Liu

2021

IEEE Access

View full text Add to dashboard Cite

An innovative financial risk early warning model based on the Internet of Things (IoT) big data technology is proposed to maintain the long-term stable development of Internet finance. The Internet credit finance is introduced, a financial risk identification method based on the big data technology is proposed, and a risk assessment method for Internet finance based on back propagation neural network (BPNN) is put forwarded in this study. The actual Internet financial data is selected for verification and analysis. The results reveal that the identification and prediction model of Internet credit finance risk based on big data technology can realize risk analysis of online credit in the Internet credit finance, and can give corresponding credit ratings to new customers on credit platforms. The training error is the lowest when the number of hidden layer nodes is 14; and the training error is the smallest when the learning rate is 0.06; Based on the BPNN, it can accurately assess the financial risks of the Internet credit platform, and the accuracy of its risk level prediction has reached 100%. This study can provide a theoretical basis for the application of IoT big data technology and neural network models in the financial field, and also give an important reference for the risk management of Internet finance.

show abstract

“…For example, the deep neural network was used in [7] to evaluate the overseas investment risks of enterprises. Likewise, Zhang [8] used the fuzzy neural network model of artificial intelligence method to assess credit risk of enterprises.…”

Section: Introductionmentioning

confidence: 99%

Selection of Optimal Investment Variant Based on Monte Carlo Simulations

Janeková¹,

Fabianová²,

Kádárová³

2021

Int. j. simul. model.

View full text Add to dashboard Cite

This paper presents a methodology for the selection of an optimal investment variant using Monte Carlo simulation and OptQuest optimization. The decision-making process also includes risk analysis. Investment variants involve renewal and development of production equipment. Two approaches to investment decision making are introduced. The first approach is based on the analysis of the distribution function of Net Present Value (NPV), and the rule of mean value and coefficient of variation is used as the decision criterion for determining the profitability of investment variants. The second approach, based on the cumulative probability distribution of NPV, provides a comparative assessment of the investment variants using stochastic dominance rules. Both approaches lead to the choice of the same investment variant. In order to increase the profitability of the selected investment variant and reduce its risk, OptQuest optimization is subsequently implemented. The introduced approaches can be a useful support tool in investment decision-making.

show abstract

Investment risk model based on intelligent fuzzy neural network and VaR

Cited by 26 publications

References 14 publications

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Selection of Optimal Investment Variant Based on Monte Carlo Simulations

Contact Info

Product

Resources

About