I jointly treat two critical issues in the application of mean-variance portfolios, i.e., estimation risk and portfolio instability. I find that theory-based portfolio strategies known to outperform naive diversification (1/N) in the absence of transaction costs, heavily underperform it under...

Instead of data-mining methods, the author proposes a portfolio committee approach to portfolio selection. Because each optimal portfolio is a combination of three basic elements: strategy, covariance matrix, and risk type; therefore, the author first augments the combination to 250 optimal...

The quantitative practice of portfolio selection aims to select the in-sample optimal portfolio that is robust out of sample. However, at each estimation period, the conventional method is selection by solving a given objective function, without a learning mechanism, or training. This paper...

The purpose of this paper is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall's Tau by counting the proportion of simultaneous co-movements in series when their...

In this paper we develop a portfolio optimization strategy based on the extraction of option-implied distributions and the application of robust asset allocation. We compute the option-implied probability density functions of the constituents of the Euro Stoxx 50 Index. To obtain the...

This research investigates the influence of methodological choices in portfolio sorts on the size of the carbon premium. By analyzing more than 100,000 methodological paths, we find that variations in the construction of brown-minus-green portfolios create substantial non-standard errors. From...

We study the size and drivers of non-standard errors (Menkveld et al., 2021) in portfolio sorts across 14 common methodological decision nodes and 40 sorting variables. These non-standard errors range between 0.05 and 0.26 percent and are, on average, larger than standard errors. Supposedly...

In time series regressions with nonparametrically autocorrelated errors, it is now standard empirical practice to use kernel-based robust standard errors that involve some smoothing function over the sample autocorrelations. The underlying smoothing parameter b, which can be defined as the ratio...