Based on provincial panel data in China from 2008 to 2019, this research takes the issuance of China's green bond as a quasi-natural experiment to explore whether China's regional green finance development pro...Based on provincial panel data in China from 2008 to 2019, this research takes the issuance of China's green bond as a quasi-natural experiment to explore whether China's regional green finance development promotes local green innovation by using the multi-period DID model. The results show that the regional green financial development can promote local green innovation, and the rapid growth of the green bond market driven by policy does improve environmental sound technology innovation. The promotion of regional green finance development to local green innovation is related to the funds allocation of green credit,but not to the issuance scale of green bonds, according to further analysis, because China's development pattern can lead to a lack of endogenous market power and low credit resource allocation efficiency. In addition, the issuance of green bonds can effectively promote the allocation of green credit funds, thus enhancing the local green innovation level, but it can't reduce local carbon emissions through promoting green innovation. Therefore, the government should strengthen the green finance implementation assessment mechanism, taking into account the heterogeneity of regions and enterprises, complete the green finance monitoring and disclosure system, and increase the rate of green technology conversion.展开更多
Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep lear...Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.展开更多
Several real estate enterprises in China(hereinafter referred to as housing enterprises)rely on overseas financing to meet their financing needs,but it is fraught with challenges such as high financing costs.Premised ...Several real estate enterprises in China(hereinafter referred to as housing enterprises)rely on overseas financing to meet their financing needs,but it is fraught with challenges such as high financing costs.Premised on the internationalization of finance,combined with the background of“staying and not speculating”and establishing a long-term mechanism for real estate market,based on the investigation of the financing motives of real estate enterprises,combined with a large amount of data,the present study examines the current situation and predicament of overseas financing of housing enterprises.It proposes four feasible countermeasures to promote sustainable development of real estate enterprises overseas financing including building a special financing system to reduce the cost,expanding various financing channels,strengthening the supervision of overseas bond financing,and reducing the loss devaluation of RMB internally and externally.展开更多
基金supported by Hebei Province Philosophy and Social Science Project (Grant No.HB22YJ021)Hebei Province Social Science Development Research Project (Grant No.20220202156)。
文摘Based on provincial panel data in China from 2008 to 2019, this research takes the issuance of China's green bond as a quasi-natural experiment to explore whether China's regional green finance development promotes local green innovation by using the multi-period DID model. The results show that the regional green financial development can promote local green innovation, and the rapid growth of the green bond market driven by policy does improve environmental sound technology innovation. The promotion of regional green finance development to local green innovation is related to the funds allocation of green credit,but not to the issuance scale of green bonds, according to further analysis, because China's development pattern can lead to a lack of endogenous market power and low credit resource allocation efficiency. In addition, the issuance of green bonds can effectively promote the allocation of green credit funds, thus enhancing the local green innovation level, but it can't reduce local carbon emissions through promoting green innovation. Therefore, the government should strengthen the green finance implementation assessment mechanism, taking into account the heterogeneity of regions and enterprises, complete the green finance monitoring and disclosure system, and increase the rate of green technology conversion.
基金funded by the Natural Science Foundation of Fujian Province,China (Grant No.2022J05291)Xiamen Scientific Research Funding for Overseas Chinese Scholars.
文摘Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.
基金The financial support from the Program for the Key Research Projects of FinancialApplication in Shandong Province[2019-JRZZ-09].
文摘Several real estate enterprises in China(hereinafter referred to as housing enterprises)rely on overseas financing to meet their financing needs,but it is fraught with challenges such as high financing costs.Premised on the internationalization of finance,combined with the background of“staying and not speculating”and establishing a long-term mechanism for real estate market,based on the investigation of the financing motives of real estate enterprises,combined with a large amount of data,the present study examines the current situation and predicament of overseas financing of housing enterprises.It proposes four feasible countermeasures to promote sustainable development of real estate enterprises overseas financing including building a special financing system to reduce the cost,expanding various financing channels,strengthening the supervision of overseas bond financing,and reducing the loss devaluation of RMB internally and externally.
