Fund Performance Evaluation Based on Bayesian Model and Machine Learning Algorithm

被引：1

作者：

Li, Shuanbao ^{[1
]}

Qu, Shenming ^{[2
]}

机构：

[1] Henan Finance Univ, Sch Artificial Intelligence, Zhengzhou 450045, Peoples R China

[2] Henan Univ, Sch Software, Kaifeng 475001, Peoples R China

来源：

DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2022年 / 2022卷

基金：

中国国家自然科学基金;

关键词：

PLANT;

D O I：

10.1155/2022/2467521

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Based on Bayesian method, this paper constructs a model for estimating fund performance evaluation, and uses machine learning algorithm to construct a sampler that can sample on the basis of conditional distribution. Sampling is used for stress test, so as to give the closeness of all possible test results and data results. The results show that performance evaluation is affected by many factors, and the resistance to risk plays an important role in the whole performance evaluation. At the same time, the Bayesian model in machine learning can quickly and accurately approach the statistical results, which is of great significance for predicting performance evaluation.

引用

页数：11

共 32 条

[1] Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach [J].

Bradley, A. Allen ;

Habib, Mohamed ;

Schwartz, Stuart S. .

WATER RESOURCES RESEARCH, 2015, 51 (09) :7382-7400

[2] Environmentally adaptive processing for shallow ocean applications: A sequential Bayesian approach [J].

Candy, J. V. .

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2015, 138 (03) :1268-1281

[3] Can Ensemble Machine Learning Improve the Accuracy of Severe Maternal Morbidity Screening in a Perinatal Database? [J].

Cartus, Abigail R. ;

Naimi, Ashley, I ;

Himes, Katherine P. ;

Jarlenski, Marian ;

Parisi, Sara M. ;

Bodnar, Lisa M. .

EPIDEMIOLOGY, 2022, 33 (01) :95-104

[4] Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam [J].

Dang An Tran ;

Tsujimura, Maki ;

Nam Thang Ha ;

Van Tam Nguyen ;

Doan Van Binh ;

Thanh Duc Dang ;

Quang-Van Doan ;

Dieu Tien Bui ;

Trieu Anh Ngoc ;

Le Vo Phu ;

Pham Thi Bich Thuc ;

Tien Dat Pham .

ECOLOGICAL INDICATORS, 2021, 127

[5] Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study [J].

Eder, Stephanie Josephine ;

Steyrl, David ;

Stefanczyk, Michal Mikolaj ;

Pieniak, Michal ;

Martinez Molina, Judit ;

Pesout, Ondra ;

Binter, Jakub ;

Smela, Patrick ;

Scharnowski, Frank ;

Nicholson, Andrew A. .

PLOS ONE, 2021, 16 (03)

[6] A Bayesian approach to quantifying uncertainty from experimental noise in DEER spectroscopy [J].

Edwards, Thomas H. ;

Stoll, Stefan .

JOURNAL OF MAGNETIC RESONANCE, 2016, 270 :87-97

[7] Cooperative Radio Source Positioning and Power Map Reconstruction: A Sparse Bayesian Learning Approach [J].

Huang, Din-Hwa ;

Wu, Sau-Hsuan ;

Wu, Wen-Rong ;

Wang, Peng-Hua .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2015, 64 (06) :2318-2332

[8] Paying for Performance in Private Equity: Evidence from Venture Capital Partnerships [J].

Huther, Niklas ;

Robinson, David T. ;

Sievers, Sonke ;

Hartmann-Wendels, Thomas .

MANAGEMENT SCIENCE, 2020, 66 (04) :1756-1782

[9] SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability [J].

Iakovishina, Daria ;

Janoueix-Lerosey, Isabelle ;

Barillot, Emmanuel ;

Regnier, Mireille ;

Boeva, Valentina .

BIOINFORMATICS, 2016, 32 (07) :984-992

[10] Does Intellectual Capital Investment Improve Financial Competitiveness and Green Innovation Performance? Evidence from Renewable Energy Companies in China [J].

Liu, Shuang ;

Yu, Qi ;

Zhang, Liang ;

Xu, Jian ;

Jin, Zhenji .

MATHEMATICAL PROBLEMS IN ENGINEERING, 2021, 2021

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