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Previous work has identified a number of promising proxies for investor distraction in the time series. To verify that our findings are indeed representative of a more general attention-based phenomenon, we construct four alternative distraction proxies inspired by a literature review, and then explore their explanatory power in our setup.

DellaVigna and Pollet (2009) show that the market underreacts more to earnings an- nouncements made on Fridays. They interpret the apparent slow information diffusion as evidence for investor distraction caused by the upcoming weekend. Louis and Sun (2010) extend their analysis to the case of merger announcements. Even for these large corpo- rate events, the market reaction on Fridays is muted, as indicated by lower abnormal trading volume and less pronounced abnormal stock returns. Louis and Sun (2010) also provide anecdotal evidence suggesting that market participants tend to be most attentive on Mondays. We thus construct a Monday (Friday) dummy as a low (high) distraction proxy.

Hirshleifer et al. (2009) argue that the number of distracting stimuli should matter. They show that the immediate (delayed) market reaction to earnings announcements is weaker (stronger) in moments where more same-day announcements compete for investors’ at- tention. Following their line of reasoning, we rely on data from I/B/E/S to compute the daily number of earnings announcements for each stock market in our sample. As for our baseline distraction proxies, we then assign decile ranks to these values. We do so

18_{Attention shifts in period t, i.e. the difference between high and low distraction days (weeks), are roughly estimated to}

separately for each year and for each country. For each DLC, we then define the final distraction proxy simply as the sum of the daily decile ranks of the two countries under consideration. In other words, the proxy can take on values between 2 (low distraction) and 20 (high distraction).

Karlsson et al. (2009) uncover an phenomenon they dub “ostrich effect”: In down market periods, investors tend to “put their heads in the sand” and to pay less attention to their investments. Hou et al. (2009) demonstrate that this individual behavior also matters for market outcomes such as abnormal returns after corporate news. These findings motivate us to construct an investor distraction proxy, which takes on a value of one (zero) if the three months cumulative return for both stock markets under consideration is (not) negative.

For each of these distraction proxies, we mirror our baseline analysis by running pooled regressions at the daily, and, where applicable, also at the weekly level. The multivariate regressions contain the same controls as in tables 4.6 and 4.7. We exclude our baseline

distraction proxies, however, to study the role of the alternative proxies in isolation.19

Findings are broadly consistent with a limited attention story. For each proxy, each return computation frequency, and each regression specification, the sign of the coefficients is as expected. Both their economic magnitude and statistical significance are less pronounced

that the impact of our baseline distraction proxies.20_{However, despite being conceptually}

quite different, each distraction proxy appears to have at least some explanatory power. Together, these findings suggest that investor attention does matter.

19_{In unreported results we find that additionally including our baseline distraction proxy sets in the regressions does}

not change any inferences: Neither the level of their economic importance and statistical significance, nor the role of the alternative distraction proxies are materially affected. Depending on the distraction proxy set employed, the multivariate analysis suggests that combined investor attention variables can account for a daily return deviation between 35 and 45 basis points. These values correspond to slightly more than 30% to slightly more than 40% of the average standard deviation of daily return deviations.

20_{Results for the proxy based on I/B/E/Sdata are similar if we focus on earnings announcement after 1994 to assure data}

Table 4.9: The Impact of Alternative Investor Distraction Proxies

This table shows results from pooled regressions of twin stock return deviations on alternative proxies for investor distraction as well as on several control variables, as described in detail in the text. Return deviations are computed as the absolute value of the difference between the currency-adjusted daily or weekly log returns of the twins. In panel A, the distraction proxy is computed as the sum of two country-specific distraction proxy decile ranks, as obtained from yearly sorts of the number of daily earnings announcements in the stock market under consideration. In panel B, the distraction proxy is computed as a dummy variable which takes on a value of of one (zero) if the three months cumulative domestic market return for both countries under consideration is (not) negative. In panel C (D), the distraction proxy is a dummy variable which takes on a value of one on Fridays (Mondays) and zero otherwise. All regressions contain firm-fixed effects. To control for heteroscedasticity and autocorrelation, t-statistics (in parentheses) are calculated with standard errors adjusted by the method of Newey and West (1987). Statistical significance at the ten, five and one-percent levels is indicated by *, **, and ***, respectively.

Panel A: Competing events (Prediction: positive sign)

Frequency Daily Daily Weekly Weekly

Regression framework Univariate Multivariate Univariate Multivariate

Coefficient 0.53*** 0.36** 0.07 0.59**

t-value (2.78) (2.17) (0.25) (2.07)

p-Value 0.006 0.030 0.803 0.038

Adj. R2 0.12 0.19 0.13 0.17

Panel B: Down market periods (Prediction: positive sign)

Frequency Daily Daily Weekly Weekly

Regression framework Univariate Multivariate Univariate Multivariate

Coefficient 26.99*** 3.98 24.25*** 5.80

t-value (8.64) (1.40) (5.53) (1.38)

p-Value 0.000 0.161 0.000 0.168

Adj. R2 0.13 0.19 0.14 0.17

Panel C: Fridays (Prediction: positive sign)

Frequency Daily Daily

Regression framework Univariate Multivariate

Coefficient 3.61* 2.81

t-value (1.90) (1.48)

p-Value 0.057 0.138

Adj. R2 _{0.12 0.19}

Panel D: Mondays (Prediction: negative sign)

Frequency Daily Daily

Regression framework Univariate Multivariate

Coefficient -4.69** -6.61***

t-value (-2.51) (-3.66)

p-Value 0.012 0.000