The purpose of this study is to deeply discuss the application of financial engineering tools (FET) in portfolio optimization, and analyze its effect in detail. Through the comprehensive application of modern investment theory and advanced mathematical modeling technology, this study discusses the potential advantages of FET in improving portfolio efficiency, reducing risks and adapting to market fluctuations. This study focuses on the application of Kalman filtering (KF) algorithm in portfolio optimization. The algorithm provides a powerful and effective tool for investors by estimating and adjusting the market state in real time. The advantage of KF algorithm lies in dealing with noise, missing data and dynamic weight adjustment, thus improving the efficiency of portfolio, especially in the rapidly changing market environment. By adopting advanced risk measurement and model, investors can identify and measure the risk of portfolio more comprehensively and formulate more effective hedging and insurance strategies. This helps to reduce the overall risk level of portfolio and improve the robustness of asset allocation when market volatility increases. The application of FET in portfolio optimization provides investors with more comprehensive and accurate decision support.
References
[1]
Bouchaud, J.-P., & Potters, M. (2003). Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management. Cambridge University Press. https://doi.org/10.1017/CBO9780511753893
[2]
Buehler, H., Gonon, L., Teichmann, J., & Wood, B. (2019). Deep Hedging. Quantitative Finance, 19, 1271-1291.
[3]
Faia, R., Pinto, T., Vale, Z., & Corchado, J. M. (2021). Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market. Energies, 14, Article 3747. https://doi.org/10.3390/en14133747
[4]
Ferreira, L., Borenstein, D., Righi, M. B., & Teixeira, D. A. F. A. (2018). A Fuzzy Hybrid Integrated Framework for Portfolio Optimization in Private Banking. Expert Systems with Applications, 92, 350-362. https://doi.org/10.1016/j.eswa.2017.09.055
[5]
Han, C., & Vinel, A. (2022). Reducing Forecasting Error by Optimally Pooling Wind Energy Generation Sources through Portfolio Optimization. Energy,239, Article 122099. https://doi.org/10.1016/j.energy.2021.122099
[6]
Kallio, M., & Hardoroudi, N. D. (2018). Second-Order Stochastic Dominance Constrained Portfolio Optimization: Theory and Computational Tests. European Journal of Operational Research, 264, 675-685. https://doi.org/10.1016/j.ejor.2017.06.067
[7]
Nasini, S., Labbé, M., & Brotcorne, L. (2022). Multi-Market Portfolio Optimization with Conditional Value at Risk. European Journal of Operational Research,300, 350-365. https://doi.org/10.1016/j.ejor.2021.10.010
[8]
Sahamkhadam, M., Stephan, A., & Östermark, R. (2022). Copula-Based Black-Litterman Portfolio Optimization. European Journal of Operational Research,297, 1055-1070. https://doi.org/10.1016/j.ejor.2021.06.015
[9]
Santos-Alamillos, F. J., Thomaidis, N. S., Usaola-Garcia, J., Ruiz-Arias, J. A., & Pozo-Vazquez, D. (2017). Exploring the Mean-Variance Portfolio Optimization Approach for Planning Wind Repowering Actions in Spain. Renewable Energy, 106, 335-342. https://doi.org/10.1016/j.renene.2017.01.041
[10]
Shaverdi, M., & Yaghoubi, S. (2021). A Technology Portfolio Optimization Model Considering Staged Financing and Moratorium Period under Uncertainty. RAIRO—Operations Research, 55, S1487-S1513. https://doi.org/10.1051/ro/2020036
[11]
Silva, T., Pinheiro, P. R., & Poggi, M. (2017). A More Human-Like Portfolio Optimization Approach. European Journal of Operational Research, 256, 252-260. https://doi.org/10.1016/j.ejor.2016.06.018
[12]
Ye, T., Yang, Z., & Feng, S. (2017). Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints. Algorithms, 10, Article 100. https://doi.org/10.3390/a10030100
