Empirical Study on Relevancy of Mandatory and Voluntary Accounting Changes—Based on Chinese Listed Companies in 1999 and 2001＂

Based on the samples of mandatory accounting changes （MAC） and voluntary accounting changes （VAC） of our country in 1999 and 2001, this research explores the relevancy between MAC and VAC and its specific performance. The results of this research are shown as follows： （1） The timing relationship exists between MAC and VAC, and the frequency of VAC rises significantly in MAC year; （2） The relevancy exists in the earnings effects between MAC and VAC, and the combined effects which MAC and VAC have on earnings are in direct correlation with the earnings effects of MAC in the same year; （3） The supervisory factors of the securities market together with MAC influence the direction of VAC. Different from Pincus and Wasley＇s conclusion, when MAC is used to decrease profits, the part of VAC in our country counterbalances the effots of MAC; the listed companies with a special purpose will be against the direction of MAC and apply to VAC with a particular purpose.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.