Recently,funds and corporations have adopted environmental/social/governance(ESG)-related criteria as increasingly important criteria when evaluating investments.The ESG scores of mutual funds,which pool money from ma...Recently,funds and corporations have adopted environmental/social/governance(ESG)-related criteria as increasingly important criteria when evaluating investments.The ESG scores of mutual funds,which pool money from many individuals,are,therefore,significant as a sign of social commitment and deliver long-term results for investors deciding in which fund to invest.However,this scoring process is not transparent,and computing ESG scores requires an extensive review and monitoring of accountancy data.In this study,Machine Learning(ML)was employed to predict six ESG scores of 3192 mutual funds by comparing the performance of the models built solely using 13 variables of ESG-controversial sectorial involvement and 37 variables of financial data.In both cases,three classification and three regression algorithms were applied to the 72 simulations.The results demonstrate an acceptable fit of the Random Forest and Gradient Boosting algorithms in regression exercises(R2 values of 60–80%)and good prediction capabilities for classification(accuracies of approximately 70–80%).The models obtained similar performance when predicting ESG scores either from financial data or ESG-controversial sectorial involvement,illustrating how financial variables are good predictors of ESG scores as data on direct involvement in non-ESG funds’activities.Thus,a relationship between the financial performance of a fund and its ESG score exists and is predictable by ML,saving time and resources.展开更多
Quantitative study of the impact of Shanghai World Expo on green GDP （GGDP） is significant for deploying sustainable development policy in China. The formula of GGDP is： GGDP = GDP--the loss of natural resources en...Quantitative study of the impact of Shanghai World Expo on green GDP （GGDP） is significant for deploying sustainable development policy in China. The formula of GGDP is： GGDP = GDP--the loss of natural resources environment relegation cost ＋ comprehensive utilized value of waste. Based on this, the study employed vector autoregressive （VAR） model to predict the value of GGDP and other economic variables on condition that there was no Shanghai World Expo. Then Influence of Expo is defined as the rate of increase of GGDP. The result demonstrated that Shanghai World Expo had accelerated the growth rate of GGDP vastly with the elimination of effect of Beijing Olympics. Additionally, the quantitative analysis between GGDP and other economic variables suggested GGDP would replace GDP to evaluate the development of economy. Finally, the paper proposed that incidents like World Expo can enhance the level of influence for a country and that post-impact of Shanghai Expo should continue to be used to promote GGDP and that GGDP should serve as one of the indicators in assessment of political achievement.展开更多
文摘Recently,funds and corporations have adopted environmental/social/governance(ESG)-related criteria as increasingly important criteria when evaluating investments.The ESG scores of mutual funds,which pool money from many individuals,are,therefore,significant as a sign of social commitment and deliver long-term results for investors deciding in which fund to invest.However,this scoring process is not transparent,and computing ESG scores requires an extensive review and monitoring of accountancy data.In this study,Machine Learning(ML)was employed to predict six ESG scores of 3192 mutual funds by comparing the performance of the models built solely using 13 variables of ESG-controversial sectorial involvement and 37 variables of financial data.In both cases,three classification and three regression algorithms were applied to the 72 simulations.The results demonstrate an acceptable fit of the Random Forest and Gradient Boosting algorithms in regression exercises(R2 values of 60–80%)and good prediction capabilities for classification(accuracies of approximately 70–80%).The models obtained similar performance when predicting ESG scores either from financial data or ESG-controversial sectorial involvement,illustrating how financial variables are good predictors of ESG scores as data on direct involvement in non-ESG funds’activities.Thus,a relationship between the financial performance of a fund and its ESG score exists and is predictable by ML,saving time and resources.
文摘Quantitative study of the impact of Shanghai World Expo on green GDP （GGDP） is significant for deploying sustainable development policy in China. The formula of GGDP is： GGDP = GDP--the loss of natural resources environment relegation cost ＋ comprehensive utilized value of waste. Based on this, the study employed vector autoregressive （VAR） model to predict the value of GGDP and other economic variables on condition that there was no Shanghai World Expo. Then Influence of Expo is defined as the rate of increase of GGDP. The result demonstrated that Shanghai World Expo had accelerated the growth rate of GGDP vastly with the elimination of effect of Beijing Olympics. Additionally, the quantitative analysis between GGDP and other economic variables suggested GGDP would replace GDP to evaluate the development of economy. Finally, the paper proposed that incidents like World Expo can enhance the level of influence for a country and that post-impact of Shanghai Expo should continue to be used to promote GGDP and that GGDP should serve as one of the indicators in assessment of political achievement.
