Relying on the Stambaugh, Yu, and Yuan (2015) mispricing score and on 45 countries between 1994 and 2013, I document economically meaningful and statistically significant cross-sectional stock return predictability around the globe. In contrast to the widely held belief, mispricing associated with the 11 long/short anomalies underlying the composite ranking measure appears to be at least as prevalent in developed markets as in emerging markets. Additional support for this conjecture is obtained, among others, from tests for biased expectations based on the behavior of anomaly spreads surrounding earnings announcements as well as from within-country variation in development.

Are anomalies strongest when investor sentiment or limits of arbitrage are considered to be greatest? We empirically explore these theoretically deducted predictions. We first identify, categorize, and replicate 100 long-short anomalies in the cross-section of expected equity returns. We then comprehensively study their interaction with popular proxies for time-varying market-level sentiment and arbitrage conditions. We find a powerful (relatively weak) role of the variation in proxies for sentiment (arbitrage constraints). In this context, the predictive power of sentiment is mostly restricted to the short leg of strategy returns. Our insights collectively suggest that the dynamics of sentiment combined with the base level (and not primarily the variations) of limits to arbitrage provide at least a partial explanation for inefficiencies.

Recent literature shows that investors’ revealed beliefs often point to the use of comparatively simple valuation approaches or heuristics rather than complex models with several dimensions of systematic risk to price assets. Against this background, we comprehensively analyze how different stock-level performance measures affect media tone in firm-specific articles in several major markets. While the realized risk-adjusted abnormal returns of all tested models are positively related to media sentiment, the CAPM-adjusted return as well as the raw stock return have the strongest impact in direct comparisons. Overall, the results are most consistent with the conjecture that, on average, reporting tends to be influenced more by straightforward valuation approaches than by risk adjustments derived from multi-factor asset pricing models. Further largely supportive evidence comes from return decompositions, subsample tests, reporting about mutual funds as well as from survey results.

Extensive research has revealed that alphabetical name ordering tends to provide an advantage to those positioned in the beginning of an alphabetical listing. This article is the first to explore the implications of this alphabetic bias in financial markets. We find that US stocks that appear near the top of an alphabetical listing have about 5–15% higher trading activity and liquidity than stocks that appear toward the bottom. The magnitude of these results is negatively related to firm visibility and investor sophistication. International evidence and fund flows further indicate that ordering effects can affect trading activity and liquidity.

Exploiting regional holidays in Germany as a source of exogenous cross-sectional variation in investor attention, we provide evidence that the well-known local bias at the individual level materially affects stock turnover at the firm level. Stocks of firms located in holiday regions are temporarily strikingly less traded than otherwise very similar stocks in non-holiday regions. This negative turnover shock survives comprehensive tests for differences in information release. It appears particularly pronounced in stocks less visible to nonlocal investors and for smaller stocks disproportionately driven by retail investors. Our findings contribute to research on local bias, trading activity, and investor distraction.

Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 241 cross-sectional anomalies in 39 stock markets. We find, based on more than two million anomaly country-months, that the United States is the only country with a reliable post-publication decline in long-short returns. Collectively, our meta-analysis of return predictors suggests that barriers to arbitrage trading can create segmented markets and that anomalies tend to represent mispricing instead of data mining.

Relying on 2.2 million articles from forty-five national and local U.S. newspapers between 1989 and 2010, we find that firms particularly covered by the media exhibit, ceteris paribus, significantly stronger momentum. The effect depends on article tone, reverses in the long run, is more pronounced for stocks with high uncertainty, and is stronger in states with high investor individualism. Our findings suggest that media coverage can exacerbate investor biases, leading return predictability to be strongest for firms in the spotlight of public attention. These results collectively lend credibility to an overreaction-based explanation for the momentum effect.

We evaluate the relative and absolute performance of competing factor-based asset pricing models in international regions and globally. Our holistic analysis controls for model transaction costs and incorporates both right-hand-side tests (based on maximum squared Sharpe ratios) and left-hand-side tests (individual return predictors, composite mispricing proxies). The overall view of the tests shows that recently proposed models tend to perform better than classical models, but otherwise perform comparably. This finding, the performance of the models in some of the LHS tests as well as further results collectively suggest the need for new powerful asset pricing models for global equity markets.