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
As financial derivatives have exploded like bombs,one ofter another,capital injections by the U.S.and European governments are becoming gradually ineffective.These rescue measures will fail to revese the banding crisi...As financial derivatives have exploded like bombs,one ofter another,capital injections by the U.S.and European governments are becoming gradually ineffective.These rescue measures will fail to revese the banding crisis,and even worse,may plunge the global economy from deflation into a cycle of inflation during recession.Ultimately,economic collapse and hyperinflation may occur simultaneously.In response to this grave possibility,China should unite first stakeholders in demanding the U.S.government strictly distinguish two kinds of debts in its rescue package:The first are bonds such as U.S.pension funds,3A grade bonds issued by Fannie Mae and Freddie Mae,and U.S.government bonds held by other countries,These are creditor’s rights,which should be guaranteed with top priority.The second kind are debts deriving from the speculation at financial institutions such as highly leveraged derivatives,which have reached astronomical figures.Attempts to rescue such bad debts will only lead to hyperinflation.展开更多
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.
文摘As financial derivatives have exploded like bombs,one ofter another,capital injections by the U.S.and European governments are becoming gradually ineffective.These rescue measures will fail to revese the banding crisis,and even worse,may plunge the global economy from deflation into a cycle of inflation during recession.Ultimately,economic collapse and hyperinflation may occur simultaneously.In response to this grave possibility,China should unite first stakeholders in demanding the U.S.government strictly distinguish two kinds of debts in its rescue package:The first are bonds such as U.S.pension funds,3A grade bonds issued by Fannie Mae and Freddie Mae,and U.S.government bonds held by other countries,These are creditor’s rights,which should be guaranteed with top priority.The second kind are debts deriving from the speculation at financial institutions such as highly leveraged derivatives,which have reached astronomical figures.Attempts to rescue such bad debts will only lead to hyperinflation.
