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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 187-224页

作者： Weisi Chen Walayat Hussain Francesco Cauteruccio Xu Zhang School of Software Engineering Xiamen University of TechnologyXiamen361024China Peter Faber Business School Australian Catholic UniversityNorth Sydney2060Australia Department of Information Engineering Polytechnic University of MarcheAncona60121Italy

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.

关键词： Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism finance

Preface for recent advances in forward performance processes

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Probability, Uncertainty and Quantitative Risk 2024年第1期9卷 1-12页

作者： Marek Musiela 8 Chuter Street McMahons Point2060 NSWSydneyAustralia

1.Introduction Forward performance measurement concept was developed in the context of investment.The idea grew out of desire to propose a framework for investment performance measurement which can run in parallel to another very important idea of modern finance,namely,arbitrage free valuation of derivatives.One may say that the two areas,namely investment and valuation,have little to do with one another.However,in our minds,there were many fundamentally important common ingredients to build on.One of us,the author of this preface,worked before on problems related to arbitrage free valuation.The other,Thaleia Zariphopoulou,worked on questions related to optimization and control.Clearly,between the two of us we had a complementary and relevant set of skills to develop the said idea.

关键词： finance investment performance

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基于大语言模型的糖尿病管理:潜力与展望

Science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st century.In 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications screening.Continuous self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and finances.The emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

Artificial intelligence applications in finance:a survey

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Journal of Management Analytics 2023年第4期10卷 676-692页

作者： Xuemei Li Alexander Sigov Leonid Ratkin Leonid A.Ivanov Ling Li Department of Information Technology and Decision Sciences Strome College of BusinessOld Dominion UniversityNorfolkVAUSA Department of Electronic Machinery University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Russian Technological University MoscowRussian Federation Russian Academy of Sciences MoscowRussian Federation Russian Academy of Engineering MoscowRussian Federation

finance is in our daily life.We invest,borrow,lend,budget,and save money.finance also provides guidelines for corporation and government spending and revenue collection.Traditional statistical solutions such as regression,PCA,and CFA have been widely used in financial forecasting and analysis.With the increasing interest in artificial intelligence in recent years,this paper reviews the Artificial Intelligence(AI)techniques in the finance domain systematically and attempts to identify the current AI technologies used,major applications,challenges,and trends in finance.It explores AI-related articles in finance in IEEE Xplore and EI compendex databases.Findings suggest AI has been engaged in finance in financial forecasting,financial protection,and financial analysis and decision-making areas.Financial forecasting is one of the main sub-fields of finance affected by AI technology.The major AI technology used is supervised learning.Deep learning has gained popular in recent years.AI could be used to address some emerging topics.

关键词： machine learning artificial intelligence finance

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

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Journal of Software Engineering and Applications 2024年第1期17卷 23-42页

作者： Gunika Dhingra Siddansh Chawla Vijay K. Madisetti Arshdeep Bahga School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Cybersecurity and Privacy Georgia Institute of Technology Atlanta USA Cloudemy Technology Labs Chandigarh India

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

关键词： Large Language Models (LLMs) Smaller Language Models (SLMs) finance Networking Supervisor Model Scoring Function

The Peach Blossom Fan and the Late Ming Historical Imagination of the Qing People

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China Book International 2024年第1期 88-91页

作者： You Shujun History Department at Zhejiang University

This book aims to offer a comprehensive view through long-term research,using straightforward and lively language.It introduces various aspects of the Qing era,including the extent of imperial power,the Banner System,the relationship between the Inner Cabinet and the Grand Council,local social management,the imperial examination system,local gentry,judicial examination and justice,imperial finances,military expenditures,women's rights,social status,and the changes in privileges and social status of the Banner people.

关键词： language. Banner finance

Blockchain and digital finance

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Financial Innovation 2022年第1期8卷 2611-2614页

作者： Wei Xu Daning Hu Karl Reiner Lang J.Leon Zhao Renmin University of China BeijingChina Southern University of Science and Technology ShenzhenChina City University of New York New York CityUSA The Chinese University of Hong Kong ShenzhenChina

Blockchain technology and its applications in various business domains have attracted great attention from researchers and practitioners in recent years.finance,which is arguably the most promising and well-known application domain,has been significantly transformed into digital finance by various novel and open technological and business innovations rooted in blockchain technology,such as decentralized finance and crypto-currency.Digital finance innovations like digital payments,crowdfunding,supply chain finance,and robo-advising have made significant progress.The main goal of this special issue is to deepen and broaden our understanding of the impacts,values,and challenges brought by blockchain technology and digital finance.

关键词： finance transformed innovation

Energy Companies’Green finance Strategies in the Context of Carbon Neutrality

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China Oil & Gas 2023年第3期30卷 22-29页

作者： He Xi Xing Yue He Xuan CNOOC Energy Economics Institute

In September 2020,Chinese President Xi Jinping solemnly announced the goal of“carbon peaking and carbon neutrality”.Carbon neutrality and green transition requires not only political promotion and fiscal support,but also social capital investment in green industries.Green financing is also an effective way to promote the low-carbon development of China’s energy companies.In this paper,on the basis of defining green finance,we reviewed the policies of green finance in China,described various tools of green finance,summarized green finance business of energy companies,and made case studies.This research combined macro policy and market,and micro industry and enterprise.

关键词： finance companies investment

Coupling coordination and evolution of finance,economy and ecological environment:The case of Silk Road provinces

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Ecological Economy 2022年第4期18卷 274-286页

作者： WANG Hai-gang LIU Ke ZHANG Fan School of Economics and Management Shaanxi University of Science and TechnologyXi'an 710021CHINA

Based on the comparative analysis of the coupling mechanism between finance,economy and ecological environment,this paper uses the coupling coordination degree and grey prediction model to measure the comprehensive evaluation value and coupling coordination degree of the financial,economic and ecological environment composite system of five provinces along the Silk Road in China from 2010 to 2019,and analyzes the evolution law of the coupling and coordinated development of finance,economy and ecological environment from the perspective of system coordinated development.The results show that:(1)The development of the financial and economic systems of the provinces along the Silk Road shows a relatively continuous and stable good trend,while the development of the ecosystem shows more obvious fluctuations.(2)In general,the overall level of the comprehensive evaluation of each system in the provinces along the route has shown a trend of improvement,but the level is not high.(3)During the investigation by provinces,it is found that the coordination level of the coupling coordination degree of finance,economy and ecological environment of each province is in three stages:moderate disorder,mild disorder,and imminent imbalance.However,Shaanxi Province has changed from imminent imbalance to barely coordination earlier,which indicates that there is regional heterogeneity in the three coupling coordination relationships among regions.(4)According to the calculation of the gray GM(1,1)model,the coupling coordination degree in Shaanxi Province will maintain a good upward trend and reach a intermediate coordination stage in 2023,while the other provinces will enter the barely coordination and primary coordination stages respectively with a slightly slower growth rate.Finally,based on the research conclusions,this paper puts forward some effective policy recommendations for the coordinated development of regional finance,economy and ecological environment,such as dredging the diffusion channels of regional financial resources,

关键词： finance ecological environment coupling coordination degree grey forecasting Silk Road