Definición de Competencia

The analysis so far has focused on returns on portfolios. We will now show that the same caveats apply in regression setting as well. We follow the methodology from Green et al. (2017) to identify independently significant signals. Table 1.10 presents anomalies that are significant in Fama and MacBeth (1973a) panel regressions of individual stock returns on rescaled anomalies.23 _{All the signals are pooled in the regressions, as follows:}

ri,t =β0+

M

X

j=1

βjxi,j,t−1+i,t. (1.4)

for a given month t and number of signals M. xi,j,t−1 is the signal for anomaly j and

stocki that was available just before the start of month t. Raw fundamental signals are transformed into cross-sectional quantiles among all the stocks in a given region before

the regressions are run to limit the effect of outliers. We also remove binary variables and signals where the variance inflation factor is higher than 7.24 _{We consider simple ordinary}

least squares (OLS) regressions (E) and the value-weighted weighted least squares (WLS) regression (V). The weight in the value-weighted WLS regression is proportional to market cap of individual stocks in each cross-section and should therefore limit the effect of small capitalization stocks. The regressions use all stock-month observations from July 1963 to December 2016 in the US and from July 1990 to December 2016 elsewhere. All the standard errors are HAC adjusted, as in Newey and West (1987), with 12 lags. We present the results for all the available stocks (All) and the restricted all-but-microcaps stocks with sizes larger than the bottom decile in the NYSE (Large). U stands for all signals found to be significant while A stands for those that remain significant after a correction for a false discovery rate (FDR) at 5%.

The FDR correction is very important since one would tend to find one significant signal in 20 individual tests even if all of them are insignificant in reality. The FDR adjustment follows Benjamini and Yekutieli (2001) and proceeds by first sorting p-values from the smallest to the largest so thatp1 ≤p2. . .≤pi. . .≤pM. FDR adjusted p-values

are determined with backward induction where pF DR M =pM P 1≤j≤M 1 j and pF DR_i = min ( pF DR_i+1 , pi M i X 1≤j≤M 1 j ) (1.5)

The adjusted p-values pF DR

i are then significant with an FDR of 5% if they are smaller

than 5%.

The results for the US look staggering. There is only one common signal out of the 8 that is significant with FDR adjustment for Compustat for the full universe of stocks and OLS regressions. This does not change for all-but-microcaps stocks, with one in 5 signals being common. Value-weighting helps as it selects only one significant signal that is common across all the specifications for both the databases. The one commonly significant anomaly is the earnings predictability of Francis et al. (2004), which is surprisingly not related to any commonly used factor. Omitting FDR correction does not change the inference and there are still huge differences. This suggests that it is virtually impossible to select independently significant signals in the same country using different datasets.

The difference in the selected anomalies across the databases in the US then translates to large discrepancies for the international sample. It is apparent that some of the signals are common for the regions, but the variability is again great. Jacobs and M¨uller (2017a) conducted a similar exercise in international markets and found only a few signals that would be significant across all the regions. Our analysis here suggests that this result is a consequence of the imperfect coverage of Datastream in the individual regions. It serves as an important caveat that the population of stocks in individual regions and its coverage by data vendors has a substantial impact on research findings and anyone working with

24_{The exclusion of signals is done iteratively, and we primarily discard signals that would not be} significant for any specification in the US.

Does the Source of Fundamental Data Matter?

international data should be aware of it.

Table 1.10:

Independently Significant Signals

The table shows signals that independently predict the returns on individual stocks in different regions. We measure predictability by significance of coefficients in the Fama and MacBeth (1973a) regressions. We regress the returns on past quantiles of fundamental signals across all stocks in the given region and month. We then focus on the t-statistics on the time-series mean of these coefficients. We report all signals with t-statistics larger than 2 (U) and those with p-values smaller than 5% after adjusting the original p-values for FDR (A). The regressions are either equal-weighted (E, standard OLS) or value-weighted (V, WLS with weights given by market cap). We compare the selected signals for CRSP & Compustat with those for Datastream for either the all-but-microcaps universe of stocks or for the full sample of stocks. The full sample (All) includes all available stocks, while the all-but-microcaps universe (Large) is restricted to stocks with capitalizations larger than that of bottom decile of the NYSE. The sample starts in July 1990 and ends in December 2016. The list of anomalies is provided in Appendix A.

Compustat Datastream

USA USA Europe Japan Asia Pacific

All Large All Large All Large All Large All Large

E V E V E V E V E V E V E V E V E V E V EPr A A A A A A A A A A A A - - U - U - U - CBOP A U U U U U A U A - A U - - U - - - - - NOA - U U U A U A U A - - - U - A - - - SP U U A U A - U - A - - - U - A - - - RDM A - A - U - U - A - U - A - U - - - - - ChNOA A - A - - - U - A U U U - - - - PY U - A - A - - - U - U - - - - BM A - - - U - - - A A A A - - - - A U U U WWI U - - - A - - - U - - - - U - U A - - - CM - - - - A - - - U - - - A - - - OL A U U U - - - - SaGr A - U - - - U - - - U - - - - GriI U - - - A - - - - GrLTNOA A - - - U - - - AT - - - - A - - - U U U - - - - CEI5Y - - - U - A U A - - - U - U U U U ChGMChS U - U - - - U - - - A - - - EP - - - U - U A - U - - - U - - - NEF - - - - U - - - U - - - A - - - SuGr - - - A - U - U - - - - Acc U U - - - U - U U - - - U - - - ChNNCOA U U U U - - - - U - - - U U U POA U - U - - - U - - - U U NPY U - - - U - U - - - - AGr U - - - U - - - U - - - - ICh U - - - U - - - - ES - - - U - U - - - - OC U - - - - TAN U - - - - ChNCOL U - - - - ChFL U - - - - FSc - - - - U - - - - HR - - - - U - - - - Lvrg - - - - U - - - - ChCOL - - - - U - - - - ChPPEIA - - - U - - U - - - - EM - - U - - - U - - - - ChiAT - - - U U U U - - - - NOACh - - - U - - - U - - - - TXFIN - - - U - - - - AL - - - U U - - - - EC - - - U - - - - IR - - - U - - - - OPtE - - - U U U ChNNCWC - - - U - U ICBE - - - U U U CDI - - - U - - - - HI - - - U NDF - - - U

1.6

Fundamental Coverage and Expected Returns