On the stability of portfolio selection models

Cesarone, Francesco; Mango, Fabiomassimo; Mottura, Carlo Domenico; Ricci, Jacopo Maria; Tardella, Fabio

doi:10.1016/j.jempfin.2020.10.003

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…According to Cesarone et al [1], the risk parity model is always the most stable in all the cases analysed with respect to the composition of the portfolio. In addition, minimum risk models are often more stable than maximum risk-gain models, and the minimum variance model is usually the preferred one.…”

Section: Introductionmentioning

confidence: 95%

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

2022

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Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model (AR) of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken.

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Section: Introductionmentioning

confidence: 95%

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

2022

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“…As described at the beginning of this section, in Problem (4) we use the sample covariance matrix for evaluating the portfolio variance. However, since the estimation of the covariance matrix represents a sensitive issue (see, e.g., Kondor et al 2007;DeMiguel et al 2009;Cesarone et al 2020a), in the following remark we provide some clarifications on this choice.…”

Section: The Mean-variance-var Modelmentioning

confidence: 99%

“…it/ docen ti/ cesar one/ DataS ets. htm, and have been used in other empirical analyses on portfolio selection(Carleo et al 2017;Cesarone et al 2020a;Benati and Conde 2022).…”

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confidence: 99%

Mean-Variance-VaR portfolios: MIQP formulation and performance analysis

2023

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Value-at-risk is one of the most popular risk management tools in the financial industry. Over the past 20 years, several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a mixed-integer quadratic programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the more binding optimal Mean-Variance-VaR portfolios seems to be generally better than that of the Equally Weighted and of the Mean-Variance-CVaR portfolios.

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“…The authors use five models for predicting stock returns, which improves the quality of stock selection for constructing an investment portfolio. Francesco Cesarone, Carlo Mottura, Jacopo M Ricci, and Fabio Tardella (Cesarone et al 2020) compared several popular investment portfolio selection models in terms of their sensitivity to noise. The results showed that the portfolios with the highest returns are the most sensitive to noise.…”

Section: Literature Reviewmentioning

confidence: 99%

Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation

Ivanyuk

2021

Economies

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The study aims to develop a dynamic model for the management of a strategic investment portfolio, taking into account the impact of crisis processes on asset value. A mathematical model of a dynamic portfolio strategy is developed, and guidelines for framing a long-term investment strategy based on the current state of the investment market are formalized. An efficient method of long-term ensemble forecasting to increase the accuracy of predicting financial time series is elaborated. A methodology for constructing and rebalancing a dynamic strategic investment portfolio based on a changing portfolio strategy that results from assessing the current market state and forecast is developed. The obtained strategic portfolio model has been estimated empirically based on historical data and its rate-of-return characteristics have been compared with those of the existing conventional models used in strategic investment.

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On the stability of portfolio selection models

Cited by 12 publications

References 33 publications

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Mean-Variance-VaR portfolios: MIQP formulation and performance analysis

Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation

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