Mean-Variance Optimization Using Forward-Looking Return Estimates

Bielstein, Patrick; Hanauer, Matthias X.

doi:10.2139/ssrn.3046258

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…Abdullah & Ishak (2021) corroborate these findings by illustrating the superiority of optimal portfolios over naive strategies during different financial periods, using the Markowitz mean-variance approach. Additionally, the integration of fundamental analysis with mean-variance optimization, as seen in Lyle & Yohn (2020) and Bielstein & Hanauer (2017), offers a robust framework for achieving high Sharpe ratios and outperforming traditional approaches.…”

Section: Previous Researchesmentioning

confidence: 99%

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

Oktavianus Yusan,

Riyadi

2024

IJMSA

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This paper examines the optimization of an Indonesian stock portfolio using two models: the Markowitz Model (Mean-Variance Model) and the Single Index Model. The data comprises historical returns of LQ 45 stocks from January 2016 to December 2021. The focus is on selecting stocks for the portfolio and determining their weights based on the two models. The study compares the performance of both optimized portfolios with the LQ45 Index benchmark, IHSG market, and each other using Sharpe and Treynor measurements. The paper tests whether the stock composition of the optimized portfolio from both models successfully and consistently generates a better performance in the future (1 January 2022 – 31 December 2022) and (1 January 2023 – 31 December 2023) compared to both LQ45 and IHSG. The results reveal a notable contrast in portfolio performance between 2022 and 2023. In 2022, both the Markowitz and Single Index portfolios exhibited remarkable returns, surpassing LQ 45 and IHSG. However, in 2023, both portfolios experienced substantial underperformance, with negative returns and unfavorable risk-adjusted metrics. These findings underscore the dynamic nature of financial markets and the need for continuous portfolio monitoring and adaptation. Investors are encouraged to reevaluate their portfolio strategies in response to changing market conditions. The study contributes valuable insights into the temporal variability of optimized portfolios and their sensitivity to evolving market dynamics.

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Section: Previous Researchesmentioning

confidence: 99%

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

Oktavianus Yusan,

Riyadi

2024

IJMSA

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“…Some studies have focused on the minimum variance portfolio (MVP), however, is only effective for mean-variance if it is assumed that the expected return is the same for all stocks, which is unlikely to hold. To address this shortcoming, Bielstein and Hanauer (2019) used the implied cost of capital (ICC) as a proxy for expected returns. The resulting expected return is used to measure in a maximum approximation of the Sharpe ratio, which then allows the portfolio to outperform the MVP robustly.…”

Section: Literature Review Mean-variance Portfolio Optimizationmentioning

confidence: 99%

“…Generally, the input estimation involves estimating the portfolio expected return and standard deviation. However, Bielstein and Hanauer (2019) claimed the difficulty to assess the expected return and its covariance matrix because both inputs are highly vulnerable to estimation errors. Thus, apart from concerning how much risk they are willing to bear due to the outcome uncertainty in the stock market, the investors also need to use an appropriate approach to improve the accuracy of the estimation in constructing the optimal portfolio.…”

Section: Introductionmentioning

confidence: 99%

Performance of Shariah-Compliant Equity Portfolio Using Model-Based Return and Risk Estimation

2020

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Establishing optimal allocation for different stocks in a portfolio via modern portfolio theory is highly depended on the accuracy of the return and risk estimation. For retail investors, technological advancement has made it possible for them to apply the complex estimation procedure for decision making. Therefore, this study aims to assess the mean-variance Shariah-compliant portfolio performance with model-based return and risk estimation. The methodology adopted is based on the implementation of ARMA and GARCH model, focused on the daily stock prices from year 2011 until 2018. Further, we used one-step ahead forecast for the best ARMA-GARCH model as well as an arithmetic mean and variance estimation to prepare the composition of diversified portfolio weights for top 10 constituent companies listed in FBM Hijrah Shariah (FBMHS) Index. We also measure out of sample performance in a constructed portfolio using Sharpe, Treynor and Jensen’s measures. The result shows that the stock allocation for the model-based portfolio is less diversified as compared to non-model-based portfolio. The composition of the model-based portfolio weight is capable of achieving high annual returns which can compensate for high risk. The out of sample portfolio performance of both techniques is capable to outperform the FBMHS Index.

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“…More recent research applies the entire battery of available forecasting techniques to produce forward-looking estimates. These include predictive regressions and implied cost of capital estimates for expected returns (see Bielstein and Hanauer, 2019) as well as classic heteroscedasticity models and option-implied moments for quantifying risk (see Gao and Nardari, 2018;Kyriacou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

Vinzelberg

Auer

2022

JRF

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PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

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Mean-Variance Optimization Using Forward-Looking Return Estimates

Cited by 3 publications

References 54 publications

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

Performance of Shariah-Compliant Equity Portfolio Using Model-Based Return and Risk Estimation

A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

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