Black–Litterman asset allocation model based on principal component analysis (PCA) under uncertainty

Ding, Lei

doi:10.1007/s10586-018-1864-1

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…This will allow us to obtain the financial asset allocations of a portfolio in the form of a linear combination of an independent factorial axis. This assumption can then, satisfy the principle of diversification (Lei 2019).…”

Section: B Independent Component Analysismentioning

confidence: 96%

Asset allocation by Unsupervised Learning

Lotfi¹,

Megzari²,

Bouhadi³

2021

R. Econ. and Finan.

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Modern portfolio theory is closely linked to the concept of diversification. As a result, the most important decision of investor is to make his allocation asset portfolio more and more efficient. Thus, for a given level of risk, the investor seeks to maximise the expected return and minimise the risk by constructing an optimal portfolio. In this paper, we seek to know how unsupervised learning can be used to define the asset allocation strategy. In this sense, we have carried out a comparative study between a so-called classical portfolio, which is based on the modern portfolio theory (i.e. a portfolio constructed on the basis of numerical optimisation) and a portfolio based on unsupervised learning. The aim of this comparison is to look for the best performing method that can give the best asset allocation. Our findings show that the optimal strategy for an ambitious investor lies to the unsupervised learning algorithms that allow a dynamic analysis of portfolio. However, the optimal strategy for a risk-averse investor is still the numerical optimisation approach.

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Section: B Independent Component Analysismentioning

confidence: 96%