We use Bayesian model averaging to analyze the sample evidence on industry return predictability within the U.S. stock market in the presence of model uncertainty. The posterior analysis shows the importance of in.ation and earnings yield in predicting industry returns. The analysis shows that the out-of-sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out-of-sample forecasting power of a naive iid forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.
We analyze the behavior of mutual fund managers with a special focus on the impact of prior performance. In contrast to previous studies, we do not focus solely on volatility as a risk measure, but also consider alternative definitions of risk and style. Using a dynamic Bayesian network, we are able to capture non-linear effects and to assign exact probabilities to the mutual fund managers' adjustment of behavior. In contrast to theoretical predictions and some existing studies, we find that prior performance has a positive impact on the choice of risk level, i.e., successful fund managers take on more risk in the following calendar year. In particular, they increase volatility, beta, and tracking error, and assign a higher proportion of their portfolio to value stocks, small firms, and momentum stocks. Overall, poor-performing fund managers switch to passive strategies.
We analyse time-varying risk premia and the implications for portfolio choice. Using Markov Chain Monte Carlo (MCMC) methods, we estimate a multivariate regime-switching model for the Carhart (1997) four factor model. We find two clearly separable Regimes with different mean returns, volatilities and correlations. In the High-Variance Regime, only value stocks deliver a good performance, whereas in the Low-Variance Regime, the market portfolio and momentum stocks promise high returns. Regime-switching induces investors to change their portfolio style over time depending on the investment horizon, the risk aversion and the prevailing regime, e.g., value investing seems to be a rational strategy in the High-Variance Regime, momentum investing in the Low-Variance Regime. An empirical out-of-sample backtest indicates that this switching strategy can be profitable, but overall the forecasting ability for the regime-switching model seems to be weak compared to the iid model.
We present a simple new explanation for the diversification discount in the valuation of firms. We demonstrate that, ceteris paribus, limited liability of equity holders is sufficient to explain a diversification discount. To derive this result, we use a credit risk model based on the value of the firm's assets. We show that a conglomerate can be regarded as an option on a portfolio of assets. By splitting up the conglomerate, the investor receives a portfolio of options on assets. The conglomerate discount arises because the value of a portfolio of options is always equal to or higher than the value of an option on a portfolio. The magnitude of the conglomerate discount depends on the number of business units and their correlation, as well as their volatility, among other factors.
Using a complete sample of US equity options, we find a positive,
highly significant relation between stock returns and lagged implied
volatilities. The results are robust after controlling for a number of
factors such as firm size, market value, analyst recommendations and
different levels of implied volatility. Lagged historical volatility is - in
contrast to the corresponding implied volatility - not relevant for stock
returns. We find considerable time variation in the relation between
lagged implied volatility and stock returns.
This paper proposes a new copula-based approach to test for asymmetries in the dependence structure of ¯nancial time series. Simply splitting observations into subsamples and comparing conditional correlations leads to spurious results due to the well-known conditioning bias. Our suggested framework is able to circumvent these problems. Applying our test to market data, we statistically con¯rm the widespread notion of signicant asymmetric dependence structures between daily changes of the VIX, VXN, VDAXnew, and VSTOXX volatility indices and their corresponding
equity index returns. A maximum likelihood method is used to perform a likelihood ratio test between the ordinary t-copula and its asymmetric extension. To the best of our knowledge, our study is the ¯rst empirical implementation of the skewed t-copula to generate meta skewed student t-distributions. Its asymmetry leads to signi¯cant improvements in the description of the dependence structure between equity returns and implied volatility changes.
The four risk factors controlling for the market, size, value, and momentum effect have become a state-of-the-art framework for various applications in financial markets research. However, previous work shows that these broadly recognized risk factors are country-specific. For these reasons, this paper develops and analyses these factors for the Swiss stock market from January 1990 to December 2005, building on a high quality dataset and taking into account specific characteristics of the Swiss stock market. We find a negative size premium of -0.67% p.a. and a positive value premium of 2.35% p.a. Both, however, show a time-varying character. The momentum effect is the most pronounced with a premium of 10.33% p.a. The results are robust and validated by a comparison to data from the US. Furthermore, we find that the explanatory power of the factors is high, confirming their relevance to the Swiss stock market.
Using a Switzerland-specific Carhart model, we study the risk-adjusted performance of actively and passively managed mutual funds investing in Swiss stocks from 1989 to 2007. We also compare the performance of actively managed funds to passively managed funds instead of comparing them to a theoretical index. For a sample of 160 funds with 13'672 monthly observations we find that active as well as passive funds significantly underperform indices on an aggregated basis. However, active large-cap funds significantly underperform and active Small-&Mid-Cap-funds significantly outperform the index. Further, we find that the average manager of an active Swiss equity fund systematically overweights small-cap, value, and low-momentum stocks. When directly comparing active to passive funds, active funds significantly underperform by -1.1% p.a. on average. While active institutional funds can almost keep up with the performance of passive funds, active retail funds cannot and drive the substantial underperformance observed for active funds. Finally, active funds perform better before the millennium than thereafter. This robust result supports the hypothesis of ongoing efficiency increases in the Swiss stock market.
