Evaluating Style Analysis

In this paper we evaluate applications of (return based) style analysis. The portfolio and positivity constraints imposed by style analysis are useful in constructing mimicking portfolios without short positions. Such mimicking portfolios can be used e.g. to construct efficient portfolios of mutual funds with desired factor loadings if the factor loadings in the underlying factor model are positively weighted portfolios. Under these conditions style analysis may also be used to determine a benchmark portfolio for performance measurement. Attribution of the returns on portfolios of which the actual composition is unobserved to specific asset classes on the basis of return based style analysis is attractive if moreover there are no additional cross exposures between the asset classes and if fund managers hold securities that on average have a beta of one relative to their own asset class. If such restrictions are not met, and in particular if the factor loadings do not generate a positively weighted portfolio, the restrictions inherent in return based style analysis distort the outcomes of standard regression approaches rather than that the analysis is improved. The size of the distortions is illustrated by considering empirical results on style analysis of US mutual funds

Do Countries or Industries Explain Momentum in Europe?

The driving force behind the well-documented medium term momentum effect in stock returns is subject of much debate. Empirical papers that aim to find the determinants of this return continuation, seem to be almost exclusively restricted to US stock markets. Consequently, regional effects have received little attention in these analyses. This paper contributes to the discussion by investigating the presence of country and industry momentum in Europe and addressing the question whether individual stock momentum is subsumed by country or industry momentum.We examine these issues by introducing a portfolio-based regression approach, which allows to test hypotheses about the existence and relative importance of multiple effects using standard statistical techniques. While the traditional sorting techniques are not suited to disentangle a multitude of possibly interrelated effects (e.g. momentum, value, and size), our method can be used even when only a moderate number of stocks are available. Our results suggest that the positive expected excess returns of momentum strategies in European stock markets are primarily driven by individual stocks effects, while industry momentum plays a less important role and country momentum is even weaker. These results are robust to the inclusion of value and size effects

Currency Hedging for International Stock Portfolios

This paper tests whether hedging currency risk improves the performance of international stock portfolios. We use a generalized performance measure which allows for investor-dependencies such as different utility functions and the presence of nontraded risks. In addition we show that an auxiliary regression, similar to the Jensen regression, provides a wealth of information about the optimal portfolio holdings for investors for the non mean-variance case. This is analogous to the information provided by the Jensen regression about optimal portfolio holdings for the mean-variance case. Our empirical results show that static hedging with currency forwards does not lead to improvements in portfolio performance for a US investor that holds a stock portfolio from the G5 countries. On the other hand, hedges that are conditional on the current interest rate spread do lead to significant performance improvements. Also, when an investor has a substantial exogenous exposure to one of the currencies, currency hedging clearly improves his portfolio performance. While these results hold for investors with power utility as well as with mean-variance utility functions, the optimal hedge ratios for these investors are different

Testing for selectivity bias in panel data models

Discusses several tests to check for the presence of selectivity bias in estimators based on panel data. Introduction; Selectivity bias in the fixed and random effects estimators; Simple tests for selectivity bias; Specification of the response mechanism and the LM test for selectivity bias; Numerical results on the pseudo true values of the RE and FE estimators; More