This paper examines the role of pricing errors in linear factor pricing models, allowing for observed strong and semi-strong factors, and latent weak factors. It focusses on the estimation of ∅k = λk − μk which plays a pivotal role, not only in the estimation of risk premia but also in...

This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I introduce an affine jump-diffusion model, which accounts for both the factor structure of asset returns and that of the variance of idiosyncratic returns. The estimation is...

We analyse interactions of simultaneous shifts in comprehensive balance sheet items annually and identify common (latent) factors, which are consistent across years. Five factors are interpreted to reflect five major decisions in businesses: Financial Flexibility, Short-term Credit, Long-term...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

An asset pricing model is customarily evaluated by how well it explains means of returns. But how well the model explains fluctuations of returns is similarly important though often overlooked in the literature. We derive “efficient” factors that combine both objectives and turn out to...

The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in...

The study proposed a six-factor asset pricing model to explain global returns. The study employed the global version of the six-factor model, besides Carhart four-factor and Fama–French five-factor models, to test the integrated international asset pricing hypothesis. Fama-MacBeth two-step...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

Abstract We propose new methodology to construct arbitrage portfolios by utilizing information contained in firm characteristics for both abnormal returns and betas (and, therefore, smart-beta risk premiums). Our methodology gives maximal weight to risk-based interpretations of characteristics'...

The paper evaluates the out-of-sample predictive potential of machine learning methods in the cross-section of international equity index returns using firm fundamentals and macroeconomic predictors. The relatively small number of equity indices in the cross-section compared to the multitude of...