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This paper aims to hunt for the driving force behind the accrual anomaly and revisit the risk versus mispricing debate.

In sorts of stock returns on abnormal and normal accruals, the authors find that abnormal accruals are the driving force behind the accrual anomaly. The authors then construct characteristic-balanced portfolios from dependent sorts of stock returns on the abnormal accrual characteristic and a related factor-mimicking portfolio to test whether the accrual anomaly is due to risk or mispricing (Daniel and Titman, 1997; Davis et al., 2000).

Similar to Hirshleifer et al. (2012), the authors find that the accrual anomaly is due to mispricing and that the measure of accruals used in Hirshleifer et al.’s study (2012) is a very broad measure of accruals. The authors therefore recommend the use of abnormal accruals in future research.

The results suggest that there are limits to arbitrage or behavioral biases with regard to the trading of low-accrual firms. Showing that the accrual effect is driven by the level of abnormal accruals, the findings of this study strongly challenge the rational risk explanation proposed by the extant literature.

The purpose of this paper is to analyze the implications of the risk versus characteristic debate from the perspective of a mean-variance investor.

Expected returns and the variance-covariance matrix are estimated based on various characteristic and risk models and evaluated for the purpose of mean-variance portfolios.

Return estimates from characteristic models are most informative to investors. Risk-factor models provide the most informative estimates of the risk. A mean-variance investor should rely on combinations of the two model types.

Although the risk vs characteristic debate is a binary academic debate, our findings from an investor's perspective suggest to make use of the best of both worlds.

In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors.

To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum.

The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample.

The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.

Credit market models and the microstructure theory of the ratings market suggest that information provided by credit rating agencies becomes more relevant in recessions when agency costs are high and less relevant in expansions when agency costs are low. The purpose of this paper is to empirically test these hypotheses with regard to equity markets.

The authors use business cycle identification algorithms to map rating events (credit rating changes and watchlist inclusions) to business cycle phases and apply the event study methodology. The results are backed by cross-sectional regressions using a variety of control variables.

The authors find that the relevance of information provided by credit rating agencies for equity prices heavily depends on the level of agency costs. Furthermore, the authors detect a “flight-to-quality” during recessions in the speculative grade segment and a weakened relevance of rating events in expansions in the investment grade segment.

This paper is the first to empirically analyse how equity investors perceive credit rating changes and watchlist inclusions over the business cycle. In the empirical analysis, the authors use a large sample of about 25,000 rating events in all Organisation for Economic Co-operation and Development markets. The presented results underline that credit ratings address the agency problem in financial markets and can thus be regarded as useful for risk management or regulation.