Using Python to Analyze Financial Big Data

As technology and the internet develop,more data are generated every day.These data are in large sizes,high dimensions,and complex structures.The combination of these three features is the“Big Data”[1].Big data is revolutionizing all industries,bringing colossal impacts to them[2].Many researchers have pointed out the huge impact that big data can have on our daily lives[3].We can utilize the information we obtain and help us make decisions.Also,the conclusions we drew from the big data we analyzed can be used as a prediction for the future,helping us to make more accurate and benign decisions earlier than others.If we apply these technics in finance,for example,in stock,we can get detailed information for stocks.Moreover,we can use the analyzed data to predict certain stocks.This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level,helping to protect them from potential losses.

Value of big data to finance:observations on an internet credit Service Company in China

Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.

Research on the Application of Big Data Technology in the Integration of Enterprise Business and Finance

With the advent of the era of big data,traditional financial management has been unable to meet the needs of modern enterprise business.Enterprises hope that financial management has the function of improving the accuracy of corporate financial data,assisting corporate management to make decisions that are more in line with the actual development of the company,and optimizing corporate management systems,thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration,can better improve and develop themselves.Based on the investigation of enterprises and universities,this article analyzes the problem of accounting talent training from both the demand and supply ends,and puts forward some suggestions for the teaching reform of accounting integration with big data in financial colleges and universities,and strives to promote the integration of business and finance.The optimal allocation of enterprise resources will gradually enhance the market competitiveness of enterprises,and explore the application strategies of big data technology in the integration of enterprise business and finance.

Research on the Modern Precision E-commerce Marketing Model under the Big Data and Pattern Recognition Background

In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp the user consumption pattern and the basis of the electric business enterprise through the use of big data can be personalized, accurate and intelligent advertising push service, service mode for the creation of more interesting and effective. Under this basis, electricity companies can also pass the assurance of pair of big data, looking for better increase user stickiness, development of new products and services, the ways and methods to reduce operational costs and accordingly, we propose the novel perspectives on the corresponding issues for the systematic level enhancement that provides the novel methodology of precision e-commerce marketing.

Application of Big Data Analysis Technology in Cross-Border E-Commerce

Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.

Spatial Structure of China's E-commerce Express Logistics Network Based on Space of Flows

The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.

Paths for Upgrade and Transformation of E-commerce of China's Fresh Agricultural Products Based on Whole Industry Supply Chain

China's e-commerce market scale of fresh agricultural products has reached 1000 billion yuan,and the prospect is bright.However,in the face of such problems as quality control,storage and distribution,and consumption habits,the fresh product e-commerce has great difficulty in development.Internet generates large data,and data are the resources and the core of the Internet and the foundation for survival and development of Internet.This paper analyzed the current situation of the development of fresh agricultural products,and discussed the plight of fresh product e-commerce in scale,profit,non-standard,and network environment.On the basis of the whole industry supply chain,it came up with paths for upgrade and transformation of the fresh product e-commerce with the big data as the core in the context of Internet environment.

数字普惠金融促进小微企业融资的理论机理及省级证据

数字普惠金融是普惠金融发展的新方向,为小微企业融资创造了新模式和新方法。然而,数字普惠金融如何推动小微企业融资的内在机理和实现机制的研究仍显不足。文章以斯蒂格利茨-维斯信贷配给模型(S-W模型)为基础框架,引入数字普惠金融两大核心优势——大数据和大样本供需方,通过分析两大核心优势对S-W模型中的假设条件、资金供求双方行为、均衡结果的影响,剖析数字普惠金融改善小微企业信贷配给的理论机理。同时通过研究各省小微信贷可获得性与数字普惠金融发展指数及其分项指数之间的关系,进一步验证数字普惠金融促进小微企业融资中大数据和大样本供需方实现机制特点,及不同类型省份异质性差异。研究结论显示,大数据和大样本对于改善小微企业融资都存在显著性正向影响,发达省份相比欠发达省份在大样本方面存在明显优势,但在大数据应用方面优势并不明显。未来进一步推动小微企业融资,发达省份主要是提升大数据应用技术并向欠发达省份溢出,欠发达省份主要是补齐大样本供需方短板,由此形成全面、协调、共享、可持续发展的局面。

科技金融政策、业绩预期差距与企业大数据应用——基于不同期望参照水平的检验

在新一代信息技术和各产业不断融合发展的背景下,如何促进大数据应用成为企业战略决策研究的关键环节.基于企业行为理论和业绩反馈理论,将科技金融试点政策纳入企业探索式创新的前因要素模型,并深入探讨业绩预期差距的调节作用.利用2007—2020年中国A股上市公司数据,基于2011年和2016年多批次科技金融试点政策的自然实验采用倍差法分析发现:科技金融政策具有创新效应,显著提高了试点区域内企业的大数据应用水平;行业业绩期望落差扩大时,企业通过战略变革恢复市场支配力的动机更为强烈,能够正向调节科技金融政策与企业大数据应用之间的关系;历史业绩期望落差扩大时,组织处于不明显的“损失”状态,内部的战略调整动机相对不足.因此,应继续深化改革,推动科技金融政策不断延伸和拓展.同时,在政策推行过程中,企业应进一步完善公司治理结构,充分发挥针对业绩期望落差的内部响应机制.

大数据时代企业业财融合的路径探讨

在大数据浪潮的席卷下,企业业财融合已成为促使企业转型升级、提升核心竞争力的关键路径。基于此,文章深入剖析了大数据时代背景下,企业业财融合的重要性,揭示了当前企业业财融合过程中面临的挑战,并详细阐述了实现企业业财有效融合的多元化路径,以期为企业实践提供更全面的指导。

大数据背景下企业财务信息化建设研究

在大数据时代,社会信息化进程快速推进,为了适应不断变化的市场环境,在日益激烈的市场竞争中生存和发展,企业开始加大信息化建设投入,特别是财务信息化建设的投入,希望借助信息化赋能企业财务工作,推动企业的高质量发展。本文通过对大数据背景下企业财务信息化建设模块分布情况、财务信息数据采集和处理能力等现状进行分析,指出企业财务信息化建设存在财务信息未能互联互通、共享性不足以及安全风险等问题,从科学规划财务信息化建设发展战略、构建完善的财务化系统、提高财务人员财务信息化综合素养等方面提出了对策建议,旨在帮助企业明确财务信息化建设的方向、目标、内容,提高企业财务信息化建设的效率和质量。

大数据建设赋能数字普惠金融发展研究——基于“国家级大数据综合试验区”的准自然实验

大数据背景下,如何拓宽数字普惠金融发展路径,进而强化其作用价值,成为重要且具有深远意义的研究话题。为此,基于2011—2020年中国286个地级及以上城市的面板数据,以“国家级大数据综合试验区”为准自然实验构建双重差分模型进行实证检验。研究发现,大数据建设对数字普惠金融发展具有显著促进效应,且经过反事实检验、安慰剂检验、随机性检验等一系列稳健性检验后,结论依旧可靠;异质性分析表明,在地级市、区位优势较好、金融结构指数高的城市组别中,大数据建设对数字普惠金融发展具有更为明显的促进效果;机制分析表明,大数据建设通过金融科技进步效应以及信任强化效应促进数字普惠金融发展。文章揭示了大数据建设赋能数字普惠金融发展的内在逻辑,能够为相关部门制定科学有效的数字普惠金融发展方案提供经验参照。

大数据时代企业供应链融资模式创新研究——以海南中小微企业为例

中小微企业融资难、融资贵、融资慢一直是我国中小微企业健康发展的一道难题。近年来国家虽然出台众多政策扶持,但由于地理和外在条件的各种原因,海南省的中小微企业“融资难、融资贵”与岛外相比更为突出。传统的供应链融资很难解决中小微企业资金问题,原因是传统供应链融资需要交易主体征信和固定的业务关系。传统的供应链融资需要从人际信任走向数字信任,将企业的经营业务数字化和信息化,真实、客观、全面地反映出供应链运营中的状况和各经营主体在其中的作用。然而,不管是以核心企业为主的供应链融资还是以物流企业为主的“1+N”供应链融资模式,都没有降低企业的融资成本。但随着大数据、人工智能所代表的数智时代技术的创新,以数据为基础充分发挥人工智能的运算和分析能力,成为真正解决“融资难、融资贵”的有效途径。

大数据背景下企业财务信息化建设

从大数据分析的四个基本功能入手,阐释大数据技术对企业财务信息化建设的重要作用。根据企业在财务信息实时管理、财务信息数据价值挖掘、财务信息化人才建设、财务数据风险处理等方面遇到的问题,针对性地提出健全硬件设施、科学划分信息类别、深度挖掘数据价值、定期培训和考核信息化财务人员、完善财务信息风险管控机制等措施,以实现企业数据信息可视化和财务信息实时管理。

基于大数据信息赋能的企业业财融合风险评价研究

研究旨在探讨大数据信息赋能背景下企业财务与风险融合挑战。并建立了风险评估体系。通过指标选取和模型概述,研究设计了神经网络判别模型处理分类问题。数据获取方面。通过问卷训查收集1469份有效问卷,进行信度与效度检验。结果与分析部分描述了样本特征、线性回归结果和神经网络模型分析。描述性统计显示不同内外部风险因素的影响情况,线性回归表明技术创新和社会责任等因素对业财融合风险具有显著影响。神经网络模型分析显示在训练和验证集上表现良好,但测试集存在拟合不足问题。混淆矩阵和ROC曲线展示了模型的分类能力和性能。基于此分析结果,企业应注重数据整合与分析、风险评估与管理、智能决策支持等方面来有效应对财务与风险融合挑战。这些举措将有助于企业更好地理解市场趋势、客户需求以及潜在风险因素,并及时做出相应决策,保持竞争优势。

大数据在金融行业风险评估中的应用与实践

大数据技术正推动金融风险评估的现代化,使金融机构能更精确地识别、评估和管理风险.它通过整合传统与非传统数据源,如社交媒体和交易模式,增强了信贷风险评估的全面性.在交易欺诈识别方面,大数据通过实时监控和智能规则引擎,能有效预防和识别欺诈行为,保护金融机构和客户的利益.此外,大数据在互联网金融风险监测中也起到了关键作用,通过构建复杂风险模型和行为分析,能及时预警潜在风险.然而,大数据的应用也带来了数据安全、隐私保护以及模型准确性和可靠性的挑战.金融机构需优化数据治理,提升数据质量与分析能力,并加强合规与技术保障,确保大数据技术的有效运用和风险控制.

基于大数据视域下的企业业财一体化策略研究

研究以大数据视域下的企业业财一体化策略为主题,旨在探索企业在大数据时代中如何实现业务和财务的融合。通过研究已有策略与转型案例,明确大数据技术的应用可为企业提供更多数据来源和分析手段,讨论大数据视角下业财一体化策略的可行性和优势,并提出一些实施建议,以帮助企业在日常经营中更好地应用大数据,并提高业务和财务绩效的整体水平。

大数据时代下医院管理会计应用现状与改进对策分析

大数据时代下,我国医疗卫生系统进入了新的发展阶段。医院每天产生大量医疗信息数据,大数据与管理会计结合应用成为实现医院精细化管理的重要手段。管理会计是医院财务管理的重要组成环节,不仅能够提高医院市场竞争力和科学化发展,还可以在一定程度上降低医院成本,促进医院的创新和科学化发展。文章以大数据为背景,研究目前医院管理会计中存在的问题,并提出有针对性的改进措施,以期提高医院财务管理水平和综合管理能力。

论大数据时代集团公司如何推进业财融合

随着信息技术日益成熟,企业管理工作与信息技术愈发紧密,企业管理模式发生了翻天覆地的变化。传统财务管理模式在企业管理工作中开始出现局限性,难以符合时代发展趋势,为此需要采取合理的手段实现企业财务管理转型,以满足当前企业财务管理需求。文章首先阐述了大数据时代集团公司业财融合内容和意义,分析了大数据时代下集团企业业财融合存在的问题,并提出了相应的优化方案,以期为集团企业推进业财融合工作提供一些参考建议。

银行业数字化转型重点问题研究——数字时代下的客户体验演变与转型路径

“加快数字化发展、建设数字中国”战略是推动商业银行数字化转型的重要机遇,本文首先分析了数字时代商业银行转型升级的背景和内在驱动因素,结合国内外多家不同类型商业银行在数字化转型方面的实践探索,总结出“渠道+组织”“市场+伙伴”“人才+技术”三种数字化转型路径;同时分析了数字时代的客户体验发展趋势,并从实际出发总结了商业银行数字化转型需要加强的六大创新能力,以及基于“数字运营”“数据驱动”的线上金融生态构建和客户体验提升。商业银行应积极把握数字时代带来的发展机遇,选取符合自身实际的数字化转型路径,提升市场竞争力,扩展知识边界,提高在客户获取、数据协同、产品创新等方面的能力,以更高效、便捷的金融服务为切入点,办好人民满意金融。