论文信息 - Optimizing portfolio construction using artificial intelligence

Optimizing portfolio construction using artificial intelligence

The aim of this research is to enhance the practicability of Artificial Intelligence using Neural Network (NN) in the actual market. This paper generalized the standard Markowitz Theory's Efficient Frontier to mimic and optimise the portfolio construction and develop a neural network heuristic to better understand the chemistry of how Artificial Intelligence can construct optimal portfolio ability and provide advantages to all levels of investors.

Chan Kok Thim | Eric Seah

[1] Ralph Neuneier,et al. Active Portfolio-Management based on Error Correction Neural Networks , 2001, NIPS.

[2] Hans Kellerer,et al. Optimization of cardinality constrained portfolios with a hybrid local search algorithm , 2003, OR Spectr..

[3] D. Plikynas,et al. PORTFOLIO DESIGN AND OPTIMIZATION USING NEURAL NETWORK BASED MULTIAGENT SYSTEM OF INVESTING AGENTS , 2008 .

[4] W. Sharpe,et al. Mean-Variance Analysis in Portfolio Choice and Capital Markets , 1987 .

[5] Tsong-Yue Lai. Portfolio selection with skewness: A multiple-objective approach , 1991 .

[6] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[7] Yazid M. Sharaiha,et al. Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..

[8] Kin Keung Lai,et al. Neural network-based mean-variance-skewness model for portfolio selection , 2008, Comput. Oper. Res..