RECICLADO Y SU IMPACTO EN LOS ESTUDIANTES”

This section reports on investigation of the underlying economic rationale for country return predictability by investigating the sources of return predictability for each country in the More-Com group.

We test Hypothesis 2, which supports the arguments of cash flows and discount rates as sources of country return predictability by following two approaches. In the first approach, we test whether return predictability is due to a capacity to predict news related to cash flows (or news related to discount rates) in returns, by following the approach proposed by Bakshi et al. (2014).

Table 4.2 presents results for the predictive regression, with news related to cash flows and news related to discount rates (denoted by and , respectively) for

individual countries in the More-Com group. Panel A and Panel B report results with predictors as returns for the Less-Com group; Panel C reports results with the US return as a predictor; whereas, Panel D and E report results for predictive regression with predictors as returns for the Non-US group.

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Regarding EW methods, among the eight countries for which returns can be predicted by the Less-Com group, six have statistically positive estimated coefficients for and

. These countries, in which the estimated coefficients (either or ) are

significant at 10% level or better under the EW method, include Austria, Belgium, Greece, Indonesia, Korea, and Mexico. On the other hand, there are five countries in the More-Com group that have statistically significant estimated coefficients for and

under the VW method. Compared to the EW method, predictors under the VW method display insignificant results in the Korean market. In addition, regardless of using either the EW or VW method, when significant, the estimated coefficients for (denoted by )

are much larger in absolute value than that for These results, for

country return predictability, are consistent with previous findings in group return predictability. Colombia is the only country that exhibits significantly negative coefficients ( = -0.47) under the predictive regression model with the US return as a main predictor.

Overall, the results in Table 4.2 suggest that cash flows and discount rates channels are the sources of return predictability for the majority of countries in the More-Com group. These findings not only support Hypothesis 1, but also affirm the essential roles of cash flows and discount rates as two main predictive channels of return.

In the second approach to test Hypothesis 2, we examine the relation between the predictability of country stock return and economic fundamentals, based on the approach proposed by Bakshi et al. (2014). Table 5.1 presents the relationship between the capacity to predict future stock returns for individual countries in the More-Com group (denoted by ) and capacity to predict economic fundamentals for stock returns for these countries (denoted by ). Specifically, Panel A and Panel B reports results with predictors as returns for the Less-Com group; Panel C reports results with the US returns as predictors; and Panel D and Panel E present results with predictors as returns for the Non-US group.

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We report in Figure 2, which accompanies Table 5.1, the predictive slope coefficients against for all ten countries in the More-Com group for the full sample from 1994-2013. The slope coefficients for all predictors are statistically positive and significant at 1% level. These results suggest that a higher ability to predict economic fundamentals leads to a higher ability to predict stock returns. For instance, in Panel A, Indonesia has the highest (0.75) and also has the highest (0.77). At the other extreme, among countries that exhibit significant slope coefficients in Panel A, Belgium has the lowest (0.32) and at the same time the lowest (0.19). Our results, therefore, provide cross-country evidence to support the growing literature that documents the gradual diffusion of information as an explanation for return predictability (Hong & Stein, 1999; Rapach et al., 2013; Bakshi et al., 2014).

Furthermore, we suggest two additional tests to examine the validity of Hypothesis 2. In the first additional test, we compute estimated coefficients ( and ) for every country in the More-Com group for every five years in the full sample. If returns for the Less-Com group can predict returns for countries in the More-Com group by forecasting its economic fundamentals in the full sample, do these processes work for every five years in the sample? Our first test provides an answer to this claim. For the second additional test, we use a pooled sample that integrates the estimated coefficients for every five years into one sample, and thus, each country in the More-Com group has four estimated and in the full sample30. This test overcomes the limitation of the previous two tests, since the number of observations in regression model (22) is above 30, and hence, the test’s results have statistical meaning. In addition, this test provides additional out of sample test to examine the validity of Hypothesis 2.

We report the estimated results for two additional tests in Appendix H. Along with Appendix H, Appendix H1 and Appendix H2 plot the predictive slope coefficients against

30

We estimate two coefficients ( and ) for 10 countries in the More-Com group for each five year period in the full sample of 20 years (1994-2013). Each country has four estimated coefficients in the full sample of 20 years, and hence, 10 countries have a total of 40 observations.

for all investigated countries, for every five years, and for the pooled sample, respectively. Overall, according to Appendix H1, the results from the Pooled sample are highly consistent with the previous findings, in the sense that the ability to predict stock return is positively related to the ability to predict economic fundamentals. These results are highly significant at the 1% level. However, Appendix H2, which reports the coefficients obtained from every five years model, provides mixed results. Specifically, the slope coefficients estimated from the first two five-year periods (1994-1998 and 1999- 2003), which are positive and statistically significant, suggests that a higher ability to predict economic fundamentals leads to a higher ability to predict stock return. The estimated slopes obtained from the last two 5-year period (2004-2008 and 2009-2013) are all positive, but insignificant. These results may suggest that the 5-year period may be not long enough to exhibit a significantly positive relationship between ability to predict stock return and ability to predict its economic fundamentals.

In brief, empirical tests provide strong evidence to support the validity of Hypothesis 2, which documents the cash flows and discount rates as the sources of return predictability.

In search for the sources of return predictability for individual countries in the More-Com group, Hypothesis 3 suggests that the return predictability of the Less-Com group is due to its ability to capture variation in risk premiums, as measured by volatility. We adopt the approach suggested by Bakshi et al. (2014) to estimate an E-GARCH (1,1) model, that includes the lagged excess return of the More-Com group in the volatility equation as an exogenous predictor. Our focus is on , which captures the relation between returns of

the Less-Com group and estimated variance. A significantly positive would indicate a

positive relation between return of the Less-Com group and expected volatility, and thus, support Hypothesis 3. We report the estimated results for individual countries in the More-Com group in Table 6.2.

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Table 6.2 reports estimated results for an E-GARCH (1,1) model for all 10 countries in the More-Com group. Obviously, the estimated are either negative or insignificant in all

investigated markets. Thus, these results suggest that there is no evidence for the volatility-based channel of return predictability. We, therefore, do not accept Hypothesis 3, that the return predictability of the Less-Com group for individual countries in the More-Com group is driven by time-varying risk premiums.