Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework

Didenko, Alexander and Demicheva, Svetlana (2013): Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework. Published in: Review of Business and Economics Studies , Vol. 1, No. 1 (September 2013): pp. 100-110.

Abstract

Modern Portfolio Theory assumes that decisions are made by individual agents. In reality most investors are involved in group decision-making. In this research we propose to realize group decision-making process by application of Ensemble Learning algorithm, in particular Random Forest. Predicting accurate asset returns is very important in the process of asset allocation. Most models are based on weak predictors. Ensemble Learning algorithms could significantly improve prediction of weak learners by combining them into one model, which will have superiority in performance. We combine technical fundamental and sentiment analysis in order to generate views on different asset classes. Purpose of the research is to build the model for Meucci Portfolio Optimization under views generated by Random Forest Ensemble Learning algorithm. The model was backtested by comparing with results obtained from other portfolio optimization frameworks.

• Item Type: MPRA Paper
• Language: English
• Keywords: Random Forest, Ensemble Learning, Meucci portfolio optimization, fundamental analysis, technical analysis
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis

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