Mean-variance optimization using forward-looking return estimates

Bielstein, Patrick; Hanauer, Matthias X.

doi:10.1007/s11156-018-0727-4

Rev Quant Finan Acc

2018

DOI: 10.1007/s11156-018-0727-4

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Mean-variance optimization using forward-looking return estimates

Patrick Bielstein

Matthias X. Hanauer

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Cited by 13 publications

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References 67 publications

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“…According to Xue, Di, & Zhang (2019),Qin, Kar, & Zheng (2016),Chen & Peng (2017), and Huang (2012) the security market is very complex and there are situations that historical data cannot be used to predict a security return and it is necessary to use expert's estimation Echterling, Eierle, & Ketterer (2015). affirm that a common method presented in financial literature to set the implied cost of capital is the usage of analyst forecasts Bielstein & Hanauer (2019). states that one of the practical difficulties ofMarkowitz's mean-variance portfolio optimization is to estimate the stock's expected return.…”

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confidence: 99%

Optimizing Zero Beta Portfolios: A Comparative Analysis of Robust and Normal Portfolio Methodologies

Gomes

2024

Rev. Contemp.

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When building a “zero beta portfolio”, neglecting the parameters’ uncertainty may harm the investor. This paper analyzes a way to build a zero beta portfolio that does not consider only the parameter points estimates, but also the beta and the expected return uncertainties. The stocks’ betas and their uncertainties are calculated using the Kalman Filter and the stocks’ expected returns and their uncertainties are calculated from analysts’ price and dividends estimations. The study applied two different methodologies to build a zero beta portfolio: one that maximizes the ratio between the expected return by the uncertainties of the parameters, called long-short robust portfolio; and another that simply maximizes the expected return, neglecting the uncertainties of the parameters, called as long-short normal portfolio. During the period analyzed, 2015-2022, compared to the long-short normal portfolio, the long-short robust portfolio had a higher realized return and a significantly lower standard deviation.

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confidence: 99%

Optimizing Zero Beta Portfolios: A Comparative Analysis of Robust and Normal Portfolio Methodologies

Gomes

2024

Rev. Contemp.

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Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model

Azevedo

Bielstein

Gerhart

2020

Rev Quant Finan Acc

Self Cite

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We propose a novel method to forecast corporate earnings, which combines the accuracy of analysts' forecasts with the unbiasedness of a cross-sectional model. We build on recent insights from the earnings forecasts literature to improve analysts' forecasts in two ways: reducing their sluggishness with respect to information in recent stock price movements and improving their long-term performance. Our model outperforms the most popular methods from the literature in terms of forecast accuracy, bias, and earnings response coefficient. Furthermore, using our estimates in the implied cost of capital calculation leads to a substantially stronger correlation with realized returns compared to earnings estimates from extant cross-sectional models.

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Dynamic interactions of actual stock returns with forecasted stock returns and investors’ risk aversion: empirical evidence interplaying the impact of Covid-19 pandemic

Abo Al Haija,

Lahyani

2023

Rev Quant Finan Acc

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Mean-variance optimization using forward-looking return estimates

Cited by 13 publications

References 67 publications

Optimizing Zero Beta Portfolios: A Comparative Analysis of Robust and Normal Portfolio Methodologies

Optimizing Zero Beta Portfolios: A Comparative Analysis of Robust and Normal Portfolio Methodologies

Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model

Dynamic interactions of actual stock returns with forecasted stock returns and investors’ risk aversion: empirical evidence interplaying the impact of Covid-19 pandemic

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