Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Construction and Optimization of a Financial Early Warning System Based on Big Data and Deep Learning Technology

New technologies such as big data,artificial intelligence,mobile internet,cloud computing,Internet of Things,and blockchain have brought about significant changes and development in the financial industry.Predicting the financial situation of enterprises,reducing the probability of uncertainty risks,and reducing the likelihood of financial crises have become important issues in enterprise financial crisis warning.In view of the issues in enterprise financial early warning systems such as lag,low accuracy,and high warning costs in data analysis,a financial early warning system based on big data and deep learning technology has been established,taking into account the different situations of listed and non-listed companies.This carries significance in improving the accuracy of enterprise financial early warning and promoting timely and effective decision-making.

Exploring the 2023 Reformation of Financial Systems in Higher Education Institutions and Examining the Practical Aspects of its Execution

As China’s economy has experienced rapid growth,the economic landscape for higher education institutions’financial systems has become increasingly intricate.Consequently,there has been a heightened demand for enhanced financial management in these institutions.Notably,in 2012,the country’s Ministry of Finance and Ministry of Education introduced a new financial system for higher education institutions,and in 2022,the Ministry of Finance publicly revised this system to further standardize financial practices within higher education institutions,align with financial management requirements,and foster the development of these institutions.This paper offers an extensive examination of the updated financial system for higher education institutions compared to the previous version,delving into aspects such as the financial management framework,professionalization criteria for financial directors,enhancements in managing the proceeds from technology and research achievements,and the incorporation of management accounting reports.Additionally,it provides a comprehensive analysis of the challenges faced by financial management in higher education institutions and outlines strategies for addressing these issues.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

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

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.

Impact of US Financial Sanctions on the Dollar's International

In 2022,the United States stepped up its sanctions on Russia.Most notably,it restricted the flow of the Russian Central Bank's foreign exchange(forex)assets,using financial administrative power as a source of strategic leverage.This move should have reduced the appeal of US dollar assets but in reality has not accelerated as expected the decline of the greenback as a store of value.The US dollar's share of global forex reserves increased instead of decreased during 2022 and 2023.Despite the rise of economic costs caused by tightened US financial sanctions,countries that recognize the geopolitical role of the United States have further accepted the dollar's international status;their continued willingness to live with the dollar's“security premium”has given a fillip to the US dollar in the short term,boosting its appeal as a reserve currency.Meanwhile,de-dollarization of forex reserves has yet to reach a sufficient scale,thus falling short of significantly challenging the dollar's reign.From a longer-term perspective,as economic and security conditions shift,countries that accept the dollar's international role or seek de-dollarization may change their choices.As a result,four possible scenarios may arise:(i)the preeminence of the US dollar remains unthreatened;(ii)the international monetary system splits into blocs;(iii)the international monetary system fragments;and(iv)the dollar loses its throne.The author believes that the last scenario is the most likely outcome.

Access to Financial Services——An Analysis from the Human Rights Perspective

There is a broad connection between finance and human rights,with finance having both positive and negative impacts on human rights.Everyone has a need for access to financial services.Documents in both the international human rights and international finance fields address the relationship between financial services and human rights.Among financial services,microcredit and inclusive finance have the closest connection to human rights and potentially the greatest impact on human rights.Access to financial services promotes economic,social,and cultural rights as well as the rights of specific groups.The conditions for access to financial services to promote human rights require the state to assume obligations to recognize,respect,protect,and fulfill the need for individuals to access financial services,and to ensure the availability,accessibility,acceptability,and adaptability of basic financial services.Access to financial services has played a significant role in China’s comprehensive victory in the battle of poverty alleviation,providing valuable experience for the international community in poverty eradication,achieving sustainable development goals,and protecting and promoting human rights.

Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology,the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management.Particularly within the context of economic globalization,informatization,and digital transformation,enterprises find themselves navigating a rapidly evolving market environment by intensifying competition.To enhance efficiency and competitiveness,many enterprises have embraced the financial sharing model to streamline financial management processes,curtail costs,and bolster the execution of corporate strategies.This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management.Drawing upon this analysis and aligning with the needs of enterprise financial management,the article proposes ideas for optimizing management practices,aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks.This contribution seeks to provide valuable insights for practitioners in the field.

Exploration of Financial Management Curriculum Reform in Vocational College

As the core course of finance and accounting major,the study of financial management knowledge plays a positive role in promoting students’understanding of the whole financial work and enriching financial knowledge.This paper analyzes the necessity of financial management curriculum reform,from the design ideas,teaching content,teaching mode,and teaching methods of several aspects of vocational college financial management curriculum reform work carried on the preliminary thinking,hoping to improve the teaching quality of this course will be helpful.

Innovative Strategies for Financial Investment Management Strategies of Enterprises in the New Era

With the in-depth development of economic globalization and the continuous emergence of new technologies,the importance of enterprise financial investment management strategy is becoming more and more prominent.The traditional financial investment management strategy can no longer meet the needs of enterprises in the new era and needs to be innovated with the times.For enterprises,the effectiveness and scientificity of financial investment management strategy will directly affect the competitiveness and long-term development of enterprises.Therefore,this study chooses the innovation based on the financial investment management strategy of enterprises in the new era as the research theme,aiming at exploring the innovative strategy to adapt to the needs of enterprises in the new era.

A Probe into the High-Quality Development Path of China’s Financial Industry

Under the new economic normal,the financial industry plays an important role in the high-quality development of the national economy,which is conducive to promoting the development of a socialist market economy with Chinese characteristics,improving the financial system,laying a good foundation for the“Internet+”economic development,and promoting the stability and prosperity of the market economy.China’s financial industry should be based on the new economic normal,strengthen the risk control of the financial industry,and ensure the stability of the financial market;Further improve the inclusive finance business,alleviate the capital problems of small and medium-sized enterprises,and activate the vitality of the financial industry;Promote fintech innovation,adapt to market changes and technological progress of The Times,and improve the comprehensive competitiveness of China’s financial industry;Improve the financial supervision system,strengthen the supervision of the financial industry,standardize the development of the financial industry,and promote the high-quality development of the financial industry.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

浙江大学升级校园POS终端方案

POS是一种多功能终端,使用起来安全、快捷、可靠,在校内各种场所广泛使用,承担着提供可靠保障和优质服务的硬件设备重任。但是,传统的校园POS终端难以满足师生对校内支付及身份验证场景使用的便捷性、智能化需求。不同品牌不同型号POS终端的通信协议不同,单独管理成本较高,业务配置复杂,同时校内业务模式和应用场景也在不断创新,终端维护变得越来越困难。

基于社会数据的卷烟零售客户行为分析与云POS预警系统优化

在当今数字化和信息化的时代,营销工作的成功与否往往取决于数据的精准应用和科学管理。卷烟行业作为一个竞争激烈的市场,营销工作不仅需要面对激烈的市场竞争,还需要适应新时代数字化发展的要求。为了应对上级对营销工作全面树立数据思维的要求,以及实现“营销精准施策攻坚战”的目标,卷烟行业积极推动云POS系统的发展与应用。本文旨在研究基于社会数据的卷烟零售客户行为分析与云POS预警系统优化,期望可以为卷烟行业实现高质量的营销发展提供重要的理论和实践指导,也可以为卷烟营销提供更加科学、高效、精准的数据支持。

无人机低空摄影测量POS数据关键信息提取的程序化实现

无人机低空摄影具有机动灵活、经济便捷的技术优势,它以高分辨率轻型数字遥感设备为机载传感器、以计算机视觉技术、导航定位技术为技术支撑,具有对地快速实时巡查、测量能力。随着我国城市化、工程建设的逐步加快,摄影测量的重要性凸显,对数据的处理能力和处理效率的要求越来越高。先从无人机低空摄影测量技术的发展与优势进行叙述,再对摄影测量POS(GPS/IMU)数据的程序化处理实现方法和应用实例进行验证说明,最后得出可以通过程序化提取POS关键信息方法能够大大提高数据处理效率。

A Financial Approach to Evaluate an Optimized Combined Cooling, Heat and Power System

Iran’s removing subsidy from energy carrier in four years ago leads to spike electricity price dramatically. This abrupt change increases the interest on distributed generation (DG) because of its several benefits such as lower electricity generation price. In Iran among all type of DGs, because of wide natural gas network infrastructure and several incentives that government legislated to support combined cooling, heat and power (CCHP) investors, this type of technology is more prevalent in comparison with other technologies. Between existing CCHP technologies, certain economic choices are to be taken into account. For different buildings with different load curves, suitable size and operation of CCHP should be calculated to make the project more feasible. If CCHP does not well suited for a position, then the whole energy efficiency would be plunged significantly. In this paper, a model to find the optimal size and operation of CCHP and auxiliary boiler for any users is proposed by considering an integrated view of electricity and natural gas network using GAMS software. Then this method is applying for a hospital in Tehran as a real case study. Finally, by applying COMFAR III software, useful financial parameters and sensitivity analysis are calculated.

Chaotic dynamic behavior analysis and control for a financial risk system

According to the risk management process of financial markets,a financial risk dynamic system is constructed in this paper.Through analyzing the basic dynamic properties,we obtain the conditions for stability and bifurcation of the system based on Hopf bifurcation theory of nonlinear dynamic systems.In order to make the system＇s chaos disappear,we select the feedback gain matrix to design a class of chaotic controller.Numerical simulations are performed to reveal the change process of financial market risk.It is shown that,when the parameter of risk transmission rate changes,the system gradually comes into chaos from the asymptotically stable state through bifurcation.The controller can then control the chaos effectively.

The Difficulties and Mechanism Innovation of Constructing China's Rural Multiplex Financial System

This paper expounds the necessity of constructing China's rural multiplex financial system as follows.First,overcome the bottleneck effect arising from the shortage of capital in the process of development of rural small and medium-sized enterprise,and increase farmers' income;second,provide capital support for rural modern industrial development,and promote rural economic development;third,rationally allocate capital,master the money flow and coordinate urban-rural development.The paper also profoundly analyzes the difficulties of constructing China's rural multiplex financial system as follows.First,the existing rural financial system structure is irrational and the function is unsound;second,non-governmental financial market is disorderly and the organization is unsound;third,the rural financial system is simple and the overhaul of rural credit cooperatives system has not yet been done;fourth,the construction of rural system of financial laws and regulations is laggard,and the construction of rural multiplex financial system lacks good social environment.The specific thoughts,measures and countermeasures of constructing mechanism innovation of China's rural multiplex financial system are put forward as follows.First,increasingly perfect legal system and provide legal support for constructing China's rural multiplex financial system;second,quicken the overhaul of rural credit cooperatives system and perfect its function in China's rural multiplex financial system;third,strengthen the degree of support and development,and change the situation of simple structure of rural financial system;fourth,reinforce administrative supervision and perfect public services,and standardize the rural financial market order.

Financial Sustainability of the Japanese Medical Payment System: Analysis of the Japanese Medical Expenditure for Educational Hospitalization of Patients with Type 2 Diabetes

Japanese medical expenditures have increased rapidly in recent years and are predicted to continue rising. Indeed, it remains uncertain whether the current Japanese medical system can be sustained. In this paper, we analyzed the medical expenditures for educational hospitalization of patients with type 2 diabetes. A dataset of 6173 patients from 36 hospitals was used in the analysis. The sample period was from July 2008 to March 2012. We analyzed the medical expenditures in two ways. First, we analyzed the length of hospital stay (LOS). Next, we analyzed the daily expenditure per patient. The Box-Cox transformation model was used in the first analysis and the ordinary least squares method in the second. Comorbidities and complications prolonged LOS and increased daily expenditures. The LOS was significantly different among hospitals. On the other hand, the differences in daily expenditures among hospitals were relatively small, such that LOS was the main determinant of medical expenditures. Previous studies suggested that LOS could be shortened without degradation of medical quality. Moreover, LOS could be controlled by introducing proper critical paths and improving and standardizing educational programs. Hence, it would be possible to control the medical expenditures for this disease.

Systemic risk management and investment analysis with financial network analytics:research opportunities and challenges

Recent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic,social,and technical mechanisms of the increasingly interconnected global financial system.Such a system largely relies on the interconnectedness of various financial entities such as banks,firms,and investors through complex financial relationships such as interbank payment networks,investment relations,or supply chains.A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories,as well as develop business applications.Moreover,with the advance of big data related technologies,and the availability of huge amounts of financial and economic network data,advanced computing technologies and data analytics that can comprehend such big data are also needed.We referred this approach as financial network analytics.We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk,as well as developing intelligent business applications like banking advisory systems.In this paper,we review the existing research about financial network analytics and then discuss its main research challenges from the economic,social,and technological perspectives.