*4.1. Sub-Period Analysis*

Since China's market underwent non-tradable stock reform in 2005, market conditions, such as market regulations, rules, trading mechanisms, participants, and trading volume, have changed drastically. To investigate the effect of the policy change on our strategy, we conduct tests on sub-periods, dividing our whole sample period at mid-2005 into two periods of nearly equal length. We repeat our analysis and obtain the daily average return data for the two sub-periods, 1 July 1995 to 30 June 2005 and 1 July 2005 to 30 June 2015. To examine the difference between the two subsamples, we conduct a two-sample mean test. First, we check the normality of the two subsamples using the Kolmogorov–Smirnov test, which is suitable for big data like ours. Panel A of Table 5 shows that all of the *p*-values for the sample differences are smaller than 0.01, which indicates that the samples do not follow a normal distribution.

We then conduct the Wilcoxon rank-sum test to check the difference between the two sub-periods after determining that they do not follow a normal distribution. Panel B of Table 5 reports the test results. Most of the *p*-values are smaller than 0.1, ranging from 0.0005 to 0.3607. The *p*-value of the TLS portfolio is 0.0118, which is very small and rejects the null hypothesis. The two sub-samples are significantly different from each other in the TLS strategy.

Next, we run regressions with the CAPM, FF3F, and LIQ4F models to check the robustness of the risk-adjusted returns. Because the results are similar, we only report the LIQ4F model alphas and betas in the sub-periods in Table 5 to save space.

Overall, we find that the two sub-periods' results are similar to those for the whole period. All of the alphas of the deciles are positive, and almost all of them are significant (portfolio 10 is marginally significant at 10%). As in the previous findings, the coefficients of market excess return and SMB are negative. The TLS strategy alphas are large and highly significant for both sub-periods; however, the result for the latter sub-period is better than the former for both the deciles and the TLS. Specifically, the second sub-period alpha of the TLS is 19.32 points, while that of the former period is 11.12. The results sugges<sup>t</sup> that the MA and TLS strategies work better in the most recent period, which has undergone non-tradable share reform to push more non-tradable shares to become tradable shares in the market. As many more shares are traded in the market, the market becomes more efficient, which suggests that technical analysis should become less profitable in the market. However, our results seem to contradict this inference. These results are interesting and need further investigation, which is beyond the scope of our study.



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**Table 5.** *Cont.*

2015. Panel A presents the results of the Kolmogorov–Smirnov test which checks the normality of the two subsamples. Panel B presents the results of the Wilcoxon rank-sum test which checks the difference between the two sample periods after knowing they do not follow normal distributions. Panel C presents results of the time-series regressions with the LIQ4F model for the two sub-periods. *t*-test statistics are presented in the parentheses.
