Risk-based strategies: the social responsibility of investment universes does matter

Bertrand, Philippe; Lapointe, Vincent

doi:10.1007/s10479-015-2081-4

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…Previous research shows that the impact of comovements on optimal weights depends on the general level of correlation between the assets of the investment universe (e.g., see Schumann 2013) and the specific composition of the investment universe (e.g., see Bertrand and Lapointe 2015). Therefore, we conduct our study on six investment universes (datasets) of various variance/correlation structure and dimensions; see Table 2.…”

Section: Setupmentioning

confidence: 99%

The impact of covariance misspecification in risk-based portfolios

et al. 2017

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Section: Setupmentioning

confidence: 99%

The impact of covariance misspecification in risk-based portfolios

et al. 2017

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“…The optimal weights in a portfolio depend on the general level of correlation between the assets of the investment universe Schumann (2013), and the specific composition of the investment universe Bertrand and Lapointe (2018). We try and capture different correlation and composition structures by creating five different universes as summarized in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

Jain

2019

Risks

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The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditional risk-based portfolios. For our analysis, we use the test for superior predictive ability on out-of-sample portfolio performance, to determine whether the observed excess performance is significant or if it occurred by chance. We find that when the covariance estimates are crude, inverse volatility weighted portfolios are more robust, followed by the machine learning-based portfolios. Minimum variance and maximum diversification are most sensitive to covariance misspecification. HRP follows the middle ground; it is less sensitive to covariance misspecification when compared with minimum variance or maximum diversification portfolio, while it is not as robust as the inverse volatility weighed portfolio. We also study the impact of the different rebalancing horizon and how the portfolios compare against a market-capitalization weighted portfolio.

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