Identifying the relative importance of stock characteristics

There is no consensus in the literature as to which stock characteristic best explains returns. In this study, we employ a novel econometric approach better suited than the traditional characteristic sorting method to answer this question for the UK market. We evaluate the relative explanatory power of market, size, momentum, volatility, liquidity and book-to-market factors in a semiparametric characteristic-based factor model which does not require constructing characteristic portfolios. We find that momentum is the most important factor and liquidity is the least important based on their relative contribution to the fit of the model and the proportion of sample months for which factor returns are significant. Overall, this study provides strong evidence to support that the momentum characteristic can best explain stock returns in the UK market. The econometric approach employed in this study is a novel way to assess relevant investment risk in international financial markets outside U.S. Moreover, multinational institutions and investors can use this approach to identify regional factors in order to diversify their portfolios.

The direction of media influence: Real-estate news and the stock market

This paper uses a novel identification strategy to test the influence of news media on the stock market. Because the stock market does not impact the media coverage of the housing market, a relationship between real-estate news and shares of companies engaged in the housing market is attributable media influence. I find that the content of reporting exhibits a significant relationship with stock returns, and the amount of news with the number of trades. These relationships exist even after controlling for known risk factors, housing market performance and intra-week correlation. This finding is consistent with the function of the media as a source of information and sentiment in financial markets.

The Adaptiveness in Stock Markets: Testing the Stylized Facts in the DAX 30

By testing a simple asset pricing model of heterogeneous agents to characterize the power-law behavior of the DAX 30 from 1975 to 2007, we provide supporting evidence on empirical findings that investors and fund managers use combinations of fixed and switching strategies based on fundamental and technical analysis when making investment decisions. By conducting econometric analysis via Monte Carlo simulations, we show that the autocorrelation patterns, the estimates of the power-law decay indices, (FI)GARCH parameters, and tail index of the model match closely the corresponding estimates for the DAX 30. A mechanism analysis based on the calibrated model provides further insights into the explanatory power of heterogeneous agent models.