Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization
| Autor: | Wonbu Lee, Jooyoung Park, Keehoon Sung, Jungdong Lim, Kyungwook Park, Seunghyun Ji |
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| Rok vydání: | 2014 |
| Předmět: |
Logic
business.industry Computer science Active learning (machine learning) Probabilistic-based design optimization Probabilistic logic Online machine learning Machine learning computer.software_genre Computer Science Applications Relevance vector machine Computational Theory and Mathematics Computational learning theory Artificial Intelligence Signal Processing Reinforcement learning Artificial intelligence Portfolio optimization business computer |
| Zdroj: | International Journal of Fuzzy Logic and Intelligent Systems. 14:73-83 |
| ISSN: | 1598-2645 |
| Popis: | Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data. |
| Databáze: | OpenAIRE |
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