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Asset Allocation, Economic Cycles and Machine Learning

Abstract : A well-worked theory of macro-based investment decision is introduced. The theoretical influence of economic cycles on time-varying risk premiums is explained and exhibited. The importance of the turning points of the growth cycle, better known as the output gap, is outlined. To quickly and accurately detect economic turning points, probabilistic indicators are first created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization. Those indicators are robust, interpretable and preserve economic consistency. A more complex approach is then evaluated: ensemble machine learning algorithms, referred to as random forest and as boosting, are applied. The two key features of those algorithms are their abilities to entertain a large number of predictors and to perform estimation and variable selection simultaneously. With both approaches investment strategies based on the models achieve impressive risk-adjusted returns: timing the market is thus possible. At last, exploring a new way of capital allocation, a hierarchical clustering based asset allocation method is introduced. The empirical results indicate that hierarchical clustering based portfolios are robust, truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.
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Submitted on : Tuesday, September 11, 2018 - 4:48:14 PM
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Thomas Raffinot. Asset Allocation, Economic Cycles and Machine Learning. Economics and Finance. Université Paris sciences et lettres, 2017. English. ⟨NNT : 2017PSLED067⟩. ⟨tel-01872176⟩

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