This study examines the impact of employee stock ownership plans(ESOPs)on stock-price informativeness in Chinese stock markets.Its findings indicate that firms implementing ESOPs experienced an average 11.89 percent i...This study examines the impact of employee stock ownership plans(ESOPs)on stock-price informativeness in Chinese stock markets.Its findings indicate that firms implementing ESOPs experienced an average 11.89 percent increase in stock-price informativeness.The plans improved stock-price informativeness through increased external attention and supervision.An event study shows that ESOPs gave rise to an announcement effect,driven by anticipated performance improvements and the novelty associated with ESOPs.A mechanism analysis demonstrates that the implementation of ESOPs attracted market attention,and the increased market supervision resulting from this mitigated the moral hazards of management associated with ESOPs.Plans with more positive signals exerted a greater influence.Notably,ESOPs that prioritized management incentives gained more recognition in the market.As the incentive effects of ESOPs were weaker than those of equity incentive plans and the ESOPs lost novelty over time,the annual announcement effect diminished gradually.These findings underscore the necessity of strengthening ESOP incentives for continued optimization of priceefficiency.展开更多
Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors ca...Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for SPF.Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches.This study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches.First,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features.Then,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock prices.The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual information.The complex structure enables this network to capture more stochasticity within the stock price.The method does not change when applied to stock data or Forex data.Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.展开更多
基金support from the National Social Science Fund of China(No.21BJY079).
文摘This study examines the impact of employee stock ownership plans(ESOPs)on stock-price informativeness in Chinese stock markets.Its findings indicate that firms implementing ESOPs experienced an average 11.89 percent increase in stock-price informativeness.The plans improved stock-price informativeness through increased external attention and supervision.An event study shows that ESOPs gave rise to an announcement effect,driven by anticipated performance improvements and the novelty associated with ESOPs.A mechanism analysis demonstrates that the implementation of ESOPs attracted market attention,and the increased market supervision resulting from this mitigated the moral hazards of management associated with ESOPs.Plans with more positive signals exerted a greater influence.Notably,ESOPs that prioritized management incentives gained more recognition in the market.As the incentive effects of ESOPs were weaker than those of equity incentive plans and the ESOPs lost novelty over time,the annual announcement effect diminished gradually.These findings underscore the necessity of strengthening ESOP incentives for continued optimization of priceefficiency.
文摘Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for SPF.Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches.This study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches.First,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features.Then,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock prices.The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual information.The complex structure enables this network to capture more stochasticity within the stock price.The method does not change when applied to stock data or Forex data.Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error.
