Abstract
This paper addresses the question of how to model the process of abnormal returns on individual stocks. It postulates a framework, where abnormal returns are generated by a process which features two autoregressive components, one stock-specific and one related to network effects. This process deviates from customary ones in that the parameters are specific to each stock/firm, that the autoregressive process is explicitly modelled instead of using cumulative abnormal returns over a pre-specified window, and that network effects are present. Abandoning either one of those deviations is rejected by data on Chinese stocks in 2018 and 2019, an episode which is significant for an abnormal stock-market returns analysis, as it was characterized by numerous tariff-setting events related to the “trade war” between the USA and China.
Highlights
A recent strand of research in economics bridges the interests in financial and international economics by considering responses of stock-market prices to shocks in the Related analyses proceed in two steps
The F-statistic is the normalized difference in residual sums of squares of the respective model under the null and the benchmark model normalized by the difference in residual degrees of freedom relative to the benchmark model’s residual sum of squares normalized by its residual sum of squares In Panel B of Table 5 we report results on the F-tests of the firm-specific parameters model against ones with parameters that are common across sectors and event windows
The model under the null indicates which parameters are restricted relative to the benchmark model, where the parameters are unrestricted
Summary
A recent strand of research in economics bridges the interests in financial and international economics by considering responses of stock-market prices to shocks in the. The abnormal returns are used (as such or accumulated over a number of days, dubbed cumulative abnormal returns) and regressed on liberalization or deliberalization indicators of which trade–agreement membership, investment–agreement membership, tariffs, and other variables are leading examples of The latter estimation is typically done in a relatively short time window after a policy event. Assuming that the parameters associated with these stock-specific responses are common to all stocks in China is rejected by the data, as is the assumption that either own dynamic or network dynamic effects are absent These results suggest that future work focused on the explanation of abnormal returns at stock markets should pay greater attention to stock-specific aspects as well as network effects.
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