Machine Learning and Factor-Based Portfolio Optimization

Conlon, Thomas; Cotter, John; Kynigakis, Iason

doi:10.2139/ssrn.3889459

Cited by 3 publications

(1 citation statement)

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“…Simpler benchmarks outperform portfolios with the minimum variance that use latent components produced from autoencoders and sparse approaches. It’s not only PCA and PLS that are examined; their corresponding regularized variants that penalize sparsity in the objective function are also taken into consideration [ 55 ]. It has been found that autoencoder-based and PCA-based covariance matrices outperform an equal-weighted portfolio in terms of mean absolute deviation and Risk Value and Conditional Risk Value correspondingly.…”

Section: Discussionmentioning

confidence: 99%

An analysis of machine learning risk factors and risk parity portfolio optimization

Wu¹,

Ahmad

Qureshi

et al. 2022

PLoS ONE

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Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.

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Section: Discussionmentioning

confidence: 99%