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In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2015), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as out-of-sample, we highlight the additional role of cross-sectional skewness and cross-sectional kurtosis. We find cross-sectional skewness to deliver a significant contribution to the performance of cross-sectional volatility in the short run (less than 12 months forecasts), while cross-sectional skewness and cross-sectional kurtosis contribute significantly to the performance of cross-sectional volatility at horizons greater than 12 months. Furthermore, we document a clear benefit of including higher moments when disaggregating excess market returns along the value and size dimension. In this case, both cross-sectional skewness and cross-sectional kurtosis span the predictive quality towards large-cap and growth stocks. Overall, the addition of higher order cross-sectional moments significantly improves the predictive performance of cross-sectional volatility, a variable that is already regarded as having high predictive power with respect to the equity premium.

We make use of the Edelman Trust Barometer to test for the relationship between public trust and international equity returns. The data allows for a two-dimensional split as to differentiate between the levels of trust expressed by (1) the general public and (2) informed publics. We examine the relationship between public trust indices and subsequent equity returns and find that portfolios of international stock indices, weighted and ranked according to the general level of trust, outperform classical GDP and equal-weighted portfolios. Furthermore, we differentiate between trust associated to three global institutions: business, government and media, as well as on a regional level according to developed and emerging markets. We document varying degrees of importance with respect to trust levels associated to considered institutions and across regions. Overall, we interpret public trust as a source of alpha rather than a priced risk factor.

Recent findings by Fama and French (2012) suggest a decreasing pattern of value premia alongside an increase in firm size. Large trading volumes of value investors often restrict their investment possibilities to large, liquid stocks and raise a serious issue if large cap value premia are diminishing. Thereon, we test for the existence of value premia with respect to seven fundamental metrics subject to a large cap sample restriction. We provide evidence for the German stock market and observe statistically significant Carhart alphas across all tested value metrics between 1988 until 2012, even after accounting for the total market HML risk factor. Finally, we look at pre- and post-rebalancing months and observe significantly negative value premia before rebalancing and an opposite pattern for past rebalancing returns. Furthermore, we identify calendar effects for value premia when considering a 12-month holding period.

This study sheds light on the entanglement of market volatility, intra-index correlations, portfolio concentration and portfolio holdings overlap, with respect to three types of index weighting schemes. First of all, we show that a high degree of absolute holdings overlap is not a sufficient measure of the level of coincidence with a value-weighted benchmark index. Secondly, we take a closer look at portfolio concentration and market contagion. We monitor a pattern where index weighting schemes experience highly correlated returns after 2008, alongside a drop in relative portfolio holdings overlap during periods of increased market volatility. Finally, we are able to show that portfolio concentration is caused by a `flight to familiarity', where we define an asset to be familiar in the case of entailing low predictive estimation errors.

The direction of this study is not to derive a conclusion on the superiority of single strategies from a pure risk-return perspective, but rather provide a guideline on a more accurate and representative categorisation on index weighting schemes. We start of by defining three categories of index construction methods and, thereon, allocate 16 alternative index weighting schemes. Empirical evidence confirms commonalities amongst peers in terms of the distributions of returns, portfolio concentration, a range of risk metrics and a return attribution analysis according to traditional and newly introduced industry-based risk factors. Furthermore, we consider static and dynamic norm constraints to shift sampling approaches closer to the pre-defined benchmark, whilst determining the impact of a substitution from the classical value-weight point of reference to an equally-weighted benchmark portfolio.

The contribution of this study is twofold. First of all, we categorise index weighting schemes along the dimension of active share and show that our division of index weighting schemes into three categories -- namely enhanced-, full replication- and sampling approaches -- corresponds to the grouping of actual mutual funds, as shown by Cremers 2009. In the second part, we analyse the role of the general market environment, by means of five cross-sectional measures, on the degree of active weight tilts observable for considered index models. We identify clear differences regarding the sensitivity towards all of the considered metrics and, thereon, confirm the necessity of a separation of weighting schemes -- commonly catalogued under the umbrella term of 'indexing' -- given their stark differences in investment patterns.

A reboiler attached to a debutanizer column allowed lighter than normal hydrocarbons in a stream supplying equipment not designed to handle the light hydrocarbons. Models of pieces of plant equipment were developed based on frrst principles and simple mathematical representations of valve behavior. The models were capable of indicating fault periods and of indicating equipment that was not functioning properly. This is the first step in a fault detection system designed for use in an actual chemical plant.

During the last decade, Islamic securities named Sukuk recorded an exceptional course in number and size of new issuances. However, apart from religious motivations there is not much known on its economic merits compared to traditional asset classes, such as stocks, bonds and cash. We empirically analyse if Sukuk can serve as a portfolio diversifier, when adding to conventional mixed asset portfolios. Tests are conducted based on a minimum-variance portfolio and a naive 1/N portfolio strategy. We show that Sukuk could be used by asset managers to enhance a risk-adjusted portfolio performance.

This study investigates the efficiency of European capital markets by examining the profitability of four long-only momentum strategies, after transaction costs of 0.5%, from the perspective of a Swiss equity investor. The investment universe considered in this study includes securities denominated in CHF, EUR, and GBP. In a first step, momentum strategies are evaluated over a period of fifteen years without hedging exchange rate risk of EUR and GBP. In a second step, the performance of full- and optimal currency hedging strategies is assessed. Results show that momentum strategies, after having considered transaction costs, do not significantly outperform a simple buy-and-hold strategy in risk-adjusted terms. Nevertheless, currency hedging strategies do significantly reduce the return volatility of portfolios.

The intuitiveness and practicability of mean-variance portfolios largely depends on the accuracy of moment estimates, which are subject to large estimation errors and conditional on time. We propose a model accounting for factor dynamics in a Bayesian setting, in which the impact of estimation accuracy on the posterior distribution is endogenously derived from a linear predictive regression model. Thereby, we capture upside return potential for periods of high factor explained variance whilst constraining downside risk for periods of low predictive quality. Results are robust in a simulation and empirical setting.

Estimating expected returns on assets is tricky business. Errors are common, injecting downside risk into mean-variance portfolios. The authors of this research introduce a quantitative approach that accounts for estimation errors and better limits downside risk. It builds on the classic Black-Litterman Model , and practitioners can replicate it. Why should you read this report? Because the authors’ approach may work for you. “Even if you identify factors or parameters to estimate returns relatively well in the past, the predictive quality is likely to vary over time,” notes co-author Lars Kaiser , Research Assistant at the University of Liechtenstein .