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Implied returns and the choice of a mean-variance efficient portfolio proxy
Titre du projet
Implied returns and the choice of a mean-variance efficient portfolio proxy
Description
Implied expected returns are the expected returns for which a supposedly mean-variance efficient portfolio is effectively efficient given a covariance matrix. We analyze the statistical properties of monthly implied expected return estimates and study their sensitivity to the choice of a mean-variance efficient portfolio proxy. Over the period January 1984 to December 2012 and for the universe of S&P 100 stocks we find that the largest gains are in terms of stability of the return forecasts. The use of a maximum diversification or equal-risk-contribution portfolio as proxy reduces significantly the cross-section and time series dispersion in the implied expected return forecasts and leads to a small improvement in forecast precision, compared to using a market capitalization, fundamental value or equal weighting scheme. For all proxies considered, the implied expected return estimates outperform the time series model based forecasts in terms of stability and forecast precision.
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Completed
Chercheurs
Boudt, Kris
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32827
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- PublicationAccès libreImplied expected returns and the choice of a mean-variance efficient portfolio proxy(2015)
; Boudt, KrisImplied expected returns are the expected returns for which a supposedly mean–variance efficient portfolio is effectively efficient, given a covariance matrix. The authors analyze the properties of monthly implied expected stock returns and study their sensitivity to the choice of mean–variance efficient portfolio proxy. For the universe of S&P 100 stocks over the period from 1984 to 2014, they find that using as risk-based portfolio proxy with respect to a market capitalization or fundamental value portfolio brings its biggest gains in return forecasts’ stability and precision. For all the proxies considered, they report that the implied expected returns outperform forecasts based on a time-series model in stability and precision. - PublicationAccès libreTesting the equality of modified Sharpe ratios(2015)
; Boudt, KrisThe modified Sharpe ratio is commonly used to evaluate the risk-adjusted performance of an investment with non-normal returns, such as hedge funds. In this note, a test for equality of modified Sharpe ratios of two investments is developed. A simulation study demonstrates the good size and power properties of the test. An application illustrates the complementarity between the Sharpe ratio and modified Sharpe ratio test for performance testing on hedge fund return data. - PublicationAccès libreQuantitative portfolio construction and systematic trading strategies using factor entropy pooling(2014)
; Colasante, MarcelloThe Entropy Pooling approach is a versatile theoretical framework to process market views and generalized stress-tests into an optimal "posterior" market distribution, which is then used for risk management and portfolio management. Entropy Pooling can be implemented non-parametrically or parametrically. The non-parametric implementation with historical scenarios is more suitable for risk management applications. Here introduce the parametric implementation of Entropy Pooling under a factor structure, which we name Factor Entropy Pooling. The factor structure reduces the dimension of the problem and linearizes the parameter space, allowing for fast computation of the posterior market distribution. We apply Factor Entropy Pooling to two portfolio construction problems. First, we use the Factor Entropy Pooling to construct the "implied returns", i.e. a market distribution consistent with a target optimal portfolio, such as maximum diversification/risk parity, or the CAPM equilibrium. Our approach improves on the implied returns a-la-Black-Litterman, and the ensuing distribution can be used as the starting point for further portfolio construction. Second, we use Factor Entropy Pooling to construct and backtest quantitative systematic trading strategies based on ranking views, or "portfolios from sorts". Unlike standard approaches, Factor Entropy Pooling closely ties to the actual empirical data.