Robust monitoring machine:a machine learning solution for out‑of‑sample R_(2)‑hacking in return predictability monitoring

The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.

Real effects of greenhouse gas disclosures

This study investigates the valuation and real effects of the mandatory disclosure of greenhouse gas(GHG)emission costs from the perspective of“double materiality.”We consider a firm with a Cobb-Douglas production function that combines GHG-related and non-GHG-related investments to produce short-term and long-term returns.In particular,the GHG-related investment entails short-term and long-term social costs of GHG emissions,including corporate costs and negative externalities.We demonstrate how the mandatory disclosure of the long-term costs of GHG emissions affects capital market valuations and corporate investment decisions relative to a non-disclosure regime.The social welfare in an accounting regime hinges on three parameters:the persistence of the short-term investment return,the ratio of the productivity of GHG-related investment to that of non-GHG-related investment,and the social cost parameter for GHG emissions.Our findings suggest that disclosing the long-term costs of GHG emissions may be detrimental to social welfare.Specifically,the non-disclosure regime results in higher social welfare than the disclosure regime for high values of these parameters.

Corrigendum to“Real effects of greenhouse gas disclosures”[China Journal of Accounting Research,17(2),(2024)100360]

The authors regret not including the acknowledgment in their article.The authors would like to apologise for any inconvenience caused.

Troubled by unequal pay rather than low pay:The incentive effects of a top management team pay gap

We examine the relationships with firm performance of the internal pay gap among individual members of the top management team(TMT) and the compensation level of TMT members relative to their industry peers. We find that pay gap is positively related to firm performance and that this positive relation is stronger when the TMT pay level is higher than the industry median. However, we do not observe such effects in Chinese state-owned enterprises(SOEs),in which both the executive managerial market and compensation are government-regulated. We also document that cutting central SOE managers' pay level can increase firm value, whereas doing so for local SOE managers has the opposite effect. Our findings have important implications for research on TMT compensation as well as for policy makers considering SOE compensation reform.