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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.

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.

This empirical study aims to examine the association between gender-diverse boards and corporate carbon emissions and estimates the effect of board gender diversity on stock price reactions to climate activism. This study contributes to the inconclusive literature on the link between gender-diverse boards and firms' financial performance by examining a single and plausibly isolated channel of association (i.e. attention to climate change).

The authors use parametric and non-parametric panel data techniques to examine the association between gender-diverse boards to corporate carbon emission. The system generalized methods of moments (SYS-GMM) estimator is used to address endogeneity concerns. The authors use the event study methodology to examine difference in stock price reactions to climate activism.

The results show that high board gender diversity is associated with lower corporate carbon emissions and higher stock returns to climate activism.

This is the first study to isolate public attention to climate change as a relevant channel through which gender-diverse boards have an impact of firms' financial performance. This study is timely and important due to the immediate threat of global warming and the recent introduction of mandatory board gender quotas in many countries around the world.

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.

The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies.

The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance.

The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account.

The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective.

The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

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.

García Lara et al. (2011) argue that there is a conservatism-related priced risk factor in US stock returns. To put this to the test, the authors aim to analyze whether the conditional conservatism effect comes from the loading on a conditional conservatism-related factor-mimicking portfolio (systematic risk) or the conservatism characteristic itself.

The authors form characteristic-balanced portfolios from dependent sorts of stocks on the firm’s degree of conservatism and the firm’s loading on the conservatism-related factor-mimicking portfolio as proposed by Daniel and Titman (1997) and Davis et al. (2000).

The tests indicate that it is the conditional conservatism characteristic rather than the factor loading that explains the cross-sectional differences in average stock returns. Consequently, they do not find evidence for a conservatism-related priced risk factor.

This finding suggests that investors misvalue the conservatism characteristic and casts doubt on the rational risk explanation as proposed by García Lara et al. (2011).

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

This paper aims to analyze the impact of conjectural “too-big-to-fail” (TBTF) guarantees on big and small US financial institutions’ stock prices during the 2008-2009 banking crisis.

The paper analyzes shocks to stock market investors’ expectations of government aid to banks in distress and respective spillover effects using an event study approach. We focus on three major events in late 2008, namely, the Lehman bankruptcy, the Citigroup bailout and the first announcement of the Capital Purchase Program (CPP) by the US Government.

The authors found significant differences in market reactions to the respective events between small and large banks. For both the Lehman and the CPP event, abnormal returns on big banks’ stocks are negative, while small banks’ stocks tend to generate positive abnormal returns. In contrast, large banks strongly outperform small banks in the case of the Citigroup bailout. Results for a control group of non-financial firms indicate that this behavior may be specific to the banking industry. The authors observed significant spillover effects to both competitors and non-competitors of Lehman and Citigroup, and concluded that while the Lehman event shook the widely held belief in an implicit TBTF subsidy to large banks, the TBTF doctrine was reinstated shortly thereafter.

This paper shows that conjectural TBTF guarantees are priced in by equity investors. While government aid to large banks in distress may prevent negative effects on the stability of the financial system, it may also create negative externalities by putting small banks at a competitive disadvantage. The findings suggest that US and European regulators’ recent policy measures directed at establishing reliable bank resolution schemes should be a step in the right direction to level the playing field for small and large financial institutions.

The purpose of this paper is to study the information content of about 3,300 global bank rating changes before and after the Lehman bankruptcy in September 2008 to assess if differences in stock market reactions for small and big banks emerge.

The analysis of the stock market reactions of rating changes (upgrades and downgrades) and bank’s size (small and big) is conducted by an event study approach.

The authors find that while upgrades are not associated with significant abnormal bank stock returns, downgrades have a significantly negative effect. This result holds for both small and big banks, while negative abnormal returns are considerably stronger for the former. For small banks, the authors observe an increase in negative cumulative abnormal returns post-Lehman. The lack of a reaction to large banks’ rating downgrades in the narrow [−1,+1] event window indicates that their stock prices may, to some extent, be insulated from negative rating information even post-Lehman, which the authors attribute to an implicit “too big to fail” subsidy anticipated by equity investors.

This paper provides insights to the differences in the information content of changes in small and big banks’ credit rating on stock returns that is unrelated to the well-known size effect. Compared to small banks, big banks seem to some extent be insulated from negative rating changes even post-Lehman – contributing to the on-going too big to fail debate.