<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of the rapid development of big data technology, developing an artificial intelligence economy is beneficial to China’s economic transformation, industrial upgrading, and enhancing international competitiveness. This paper first constructs a financial mathematical model of the AI economy based on the theoretical foundation of financial mathematics. The structure of the financial mathematical model mainly consists of three parts: portfolio model, capital asset pricing model, and financial derivatives pricing model. To address the problem of the unstable development of China’s AI economy, the theoretical framework of investment risk management is constructed, which can help investors intelligently implement investment risk management plans and implement investment risk management countermeasures and measures. Finally, to verify the performance of the risk investment optimization model and intelligent algorithm proposed in this chapter, three investment scales will verify the investment risk management capability based on the portfolio model. The results show that the convergence performance of the portfolio model is 0.2025, 0.4021, and 0.4391 for three investment sizes of 2, 4, and 9, and the penalty coefficients are 1.5, 28, and 0.2, respectively. At the beginning of the iteration, the investment risk is high, and the convergence speed of the portfolio model is higher than the capital asset pricing model and the financial derivatives pricing model. This study applies it to solve financial optimization problems, including portfolio, stock forecasting, risk management, and many other fields, and provides a better solution to the optimization decision problem of the portfolio.</p>
</abstract>

In the context of the rapid development of big data technology, developing an artificial intelligence economy is beneficial to China’s economic transformation, industrial upgrading, and enhancing international competitiveness. This paper first constructs a financial mathematical model of the AI economy based on the theoretical foundation of financial mathematics. The structure of the financial mathematical model mainly consists of three parts: portfolio model, capital asset pricing model, and financial derivatives pricing model. To address the problem of the unstable development of China’s AI economy, the theoretical framework of investment risk management is constructed, which can help investors intelligently implement investment risk management plans and implement investment risk management countermeasures and measures. Finally, to verify the performance of the risk investment optimization model and intelligent algorithm proposed in this chapter, three investment scales will verify the investment risk management capability based on the portfolio model. The results show that the convergence performance of the portfolio model is 0.2025, 0.4021, and 0.4391 for three investment sizes of 2, 4, and 9, and the penalty coefficients are 1.5, 28, and 0.2, respectively. At the beginning of the iteration, the investment risk is high, and the convergence speed of the portfolio model is higher than the capital asset pricing model and the financial derivatives pricing model. This study applies it to solve financial optimization problems, including portfolio, stock forecasting, risk management, and many other fields, and provides a better solution to the optimization decision problem of the portfolio.

Theoretical and practical research on mathematical modeling of economy and finance based on artificial intelligence

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the rapid development of big data technology, developing an artificial intelligence economy is beneficial to China’s economic transformation,...

Theoretical and practical research on mathematical modeling of economy and finance based on artificial intelligence

Online veröffentlicht: 16. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 24. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00199

Schlüsselwörter
Artificial intelligence economy, Financial mathematics, Portfolio models, Capital pricing models, Derivative pricing models

© 2023 Jie Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Theoretical and practical research on mathematical modeling of economy and finance based on artificial intelligence

Online veröffentlicht: 16. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 24. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00199

SchlüsselwörterArtificial intelligence economy, Financial mathematics, Portfolio models, Capital pricing models, Derivative pricing models

© 2023 Jie Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Artificial intelligence economy, Financial mathematics, Portfolio models, Capital pricing models, Derivative pricing models