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

Alexander Didenko () and Svetlana Demicheva

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Random Forest; Ensemble Learning; Meucci portfolio optimization; fundamental analysis; technical analysis (search for similar items in EconPapers)
JEL-codes: C02 C6 C61 (search for similar items in EconPapers)
Date: 2013-09
New Economics Papers: this item is included in nep-cmp
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Published in Review of Business and Economics Studies 1.1(2013): pp. 100-110

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Related works:
Journal Article: Application of Ensemble Learning for views generation in Meucci portfolio optimization framework (2013)
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