When Bayes-Stein Meets Machine Learning: A Generalized Approach for Portfolio Optimization

Gounopoulos, Dimitrios; Platanakis, Emmanouil; Tsoukalas, Gerry; Wu, Haoran

doi:10.2139/ssrn.4229499

Cited by 3 publications

(1 citation statement)

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“…Recent studies on longer data find that mean-variance models might be superior to the naive approach for asset allocation, which is primarily because estimation errors are lower for asset classes than for individual assets. Furthermore, in-depth studies show that the basic Bayes-Stein framework cannot offer better out-of-sample performance [Board and Sutcliffe, 1994], but the generalized version, enhanced with the use of machine learning, can offer better out-of-sample performance than the 1/N strategy [Gounopoulos et al, 2022], which is also true for sophisticated portfolio techniques that control for estimation errors.…”

Section: Portfolio Optimization and Diversificationmentioning

confidence: 99%

Market-moving events and their role in portfolio optimization of generations X, Y, and Z

Iwanicz-Drozdowska,

Rogowicz,

Smaga

2023

International Journal of Management and Economics

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We examine how generations X, Y, and Z might react to market-moving events over short- and long-term horizons to maintain an optimal balance among risk, return, and investor preferences. To analyze various portfolio variants, we use data on selected global assets and several types of economic and non-economic events for 2000-2021H1, applying the mean-variance optimization procedure. According to our results, in optimal portfolios, fixed-income assets dominate and are the main driver of portfolio adjustments. Portfolios with short-term horizons with less risk-averse investors and those for generation Z are the most reactive to analyzed types of events. None of the events per se creates an extraordinary opportunity to increase returns. However, expansionary monetary policy generates the greatest potential for incremental returns. Our findings provide practical implications for investors on how to adjust their portfolios in response to significant market events.

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Section: Portfolio Optimization and Diversificationmentioning

confidence: 99%

Market-moving events and their role in portfolio optimization of generations X, Y, and Z

Iwanicz-Drozdowska,

Rogowicz,

Smaga

2023

International Journal of Management and Economics

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Portfolio Management with Alternative Investments

Platanakis,

Stafylas,

Sutcliffe

2023

The Palgrave Encyclopedia of Private Equity

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The diversification benefits of cryptocurrency factor portfolios: Are they there?

Han,

Newton,

Platanakis

et al. 2024

Rev Quant Finan Acc

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We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.

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When Bayes-Stein Meets Machine Learning: A Generalized Approach for Portfolio Optimization

Cited by 3 publications

References 60 publications

Market-moving events and their role in portfolio optimization of generations X, Y, and Z

Market-moving events and their role in portfolio optimization of generations X, Y, and Z

Portfolio Management with Alternative Investments

The diversification benefits of cryptocurrency factor portfolios: Are they there?

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