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.

Research on the Path of Transition from Financial Accounting to Management Accounting Under the Background of Big Data

Driven by the wave of big data,the traditional financial accounting model faces an urgent need for transformation,as it struggles to adapt to the complex requirements of modern enterprise management.This paper aims to explore the feasible path for transitioning enterprise financial accounting to management accounting in the context of big data.It first analyzes the limitations of financial accounting in the era of big data,then highlights the necessity of transitioning to management accounting.Following this,the paper outlines the various challenges that may arise during this transition and,based on the analysis,proposes a series of corresponding transition strategies.These strategies aim to provide theoretical support and practical guidance for enterprises seeking a smooth transition from financial accounting to management accounting.

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.

Research on the Teaching Reform of Higher Vocational Financial Management Course with the Application of Big Data

With continuous development of modern big data technology,higher vocational financial management teachers should actively seek ways and means of reform.Teaching reform of higher vocational financial management course can be done by integrating modern teaching,understanding the students’academic performance,and comprehensively transforming the teaching methods.These methods can optimize and ensure the comprehensive quality of students,and improve the quality of higher vocational financial management course.

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.

Economic Growth and Interaction between Financial and Industrial Structure - Empirical Analysis on Provincial Panel Data

In recent years, China's economic growth speed has been slowing down, leading to the problems of overcapacity and unbalanced regional economic development, and the mismatch between industrial and financial structure is becoming intense. Therefore, this paper, starting with the relationship among economic growth, industrial structure and financial structure, summarizes the research by the former scholars. On this basis, by using data of 31 provincial panel data in China from 2007 to 2016, the article aims to find out the relationship between the industrial structure and economic growth, the relationship between the financial structure and economic growth and the relationship between the interaction of financial and industrial structure and economic growth. Finally, the corresponding policy recommendations are obtained following the systematical empirical conclusions. The conclusions of this paper are as follows:(1) developing indirect financing mode can effectively drive China's economic growth.(2) continuing to develop the second industry can play a catalytic role in the economic growth in most areas of China.(3) the interaction between the financial structure and the industrial structure can promote the economic growth significantly. However, the matching effect of the financial structure and industrial structure in China has not been completely formed, and the industrial upgrading should be guided to be structurally reformed through the policy.

The Relationship between Financial Development and Industrial Restructuring --Based on Panel Data of 17 Areas of Shandong

Based on the Panel Data of Shan dong Province of 17 regions, by estimating varying coefficient models, studies the relationship between financial development and industrial structure.The results show that, on the whole, the financial development indicators-the scale and the efficiency can effectively promote industrial restructuring in all regions of Shandong province, but its role has obvious differences in different regions, so it can provide a basis for economic decision-making and financial policy advice.

The Study on the Economic Application of Financial Big Data

Big Data has become the focus of the Internet industry. With the development of various industries becoming more sophisticated, long-term accumulation of operational data, customer data displays exponential growth trend. The rapid development of financial big data both poses opportunities and challenges for financial industry and economic growth. This article explains the economic application of big data based on the perspective of economics.

Reflections on Applications of International Standards for Financial Data Exchange in China Securities and Futures Markets

1.1. Development of international data exchange standards in securities field Securities market involves a large number of participants, like investors, securities companies, exchanges, clearingcorporations and so on. Businesses among the participants are completed via data exchange. Therefore, the data exchange protocols serve an important factor to determine and promote the sate and rapid development of the securities market.

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

Impact of CEO attributes on corporate reputation,financial performance,and corporate sustainable growth:evidence from India

This article investigates the impact of CEO attributes on corporate reputation,financial performance,and corporate sustainable growth in India.Using static panel data methodology for a sample of NSE listed leading 138 non-financial companies over the time-frame 2011 to 2018,we find that CEO remuneration and tenure maintains significant positive associations with corporate reputation,while duality and CEO busyness are found to be associated with corporate reputation negatively.The results also show that female CEOs and CEO remuneration are associated with corporate financial performance positively,whereas CEO busyness,as expected,holds a significant negative relationship with corporate financial performance.Moreover,the results demonstrate that CEO age is associated with corporate sustainable growth negatively,while tenure appears to have a significant and positive association with corporate sustainable growth.The results are robust to various tests and suggest that in the Indian context,demographic and job-specific attributes of CEOs exert significant influence on corpo-rate reputation,financial performance,and corporate sustainable growth.The empirical findings would provide a basis for the shareholders and companies to identify areas of consideration when appointing CEOs and determining their roles and responsibilities.

Investigation on the Quality of Financial Enterprises＇ XBRL Formatted Financial Report and Its Market Effect

The application of extensible business reporting language （XBRL） has been widely used across the globe in recent years. Many countries already made XBRL mandatory in their accounting system. The financial industry is normally the first XBRL network report implementer in these countries and it has its own industry classification standard. However, there are many issues been reported on the quality of XBRL formatted financial statement in the financial industry, such as project omission, project misstatements, account omission, and amount misstatements. This paper has conducted an empirical research on the quality of XBRL financial statement in the financial industry based on all samples available from the Shanghai stock exchange and analyzed the market effect of these issues for over six year window period. A web presentation quality evaluation criteria evaluation model was used in the investigation. Evidences indicate that the number of errors in report item omissions was the biggest problem in XBRL formatted financial reports comparing with other types of errors, while this affected the growth rate of company, unexpected rate of return, and the adoption of XBRL. Suggestions on solving these issues are then provided after comparing with the data point methodology （DPM） implementation for XBRL financial reporting in European countries, especially the World Bank.

Evaluation of the Effects of Corporate Governance on Financial Reporting Quality

Corporate governance is designed to stimulate the investment environment and to create a stable financial situation in the capital markets by increasing the level of reliability,transparency,and accountability at the firm level.This study aims to examine whether corporate governance leads to higher quality financial reporting.This research has been performed using companies listed on Borsastanbul(BIST).For this purpose,two samples from the publicly held companies on BIST,which are included in the Corporate Governance Index and which are not included in this index,have been formed.Thus,we examined whether there is any difference between the financial reporting quality of the companies listed in Borsastanbul Corporate Governance Index and the financial reporting quality of the enterprises that are not included in this index.Since the quality of financial reporting is a multi-dimensional concept,it can be evaluated by different measurement methods focusing on different dimensions in the literature.One of these approaches used to measure the quality of financial reporting is the quality of earnings.The evaluation of the financial reporting quality of the enterprises included in the BIST Corporate Governance Index and the enterprises not included in the index were evaluated through different methods to compare two different samples in the context of the earnings quality approach.Panel data analysis was used to evaluate the financial reporting quality of the two samples by means of earnings quality methods.The data related to the models used in the assessment of financial reporting quality were obtained from the Public Disclosure Platform(KAP)and Equity RT database.The research covers 72 enterprises,36 of which are in the Corporate Governance Index and 36 of which are not in the Corporate Governance Index.

The Influence of Financial Technology on the Business of Commercial Banks and Measure Suggestions

With the continuous development and improvement of financial technology,commercial banks are facing huge impacts and challenges brought about by financial technology,but what follows is a huge opportunity for the transformation of commercial banks.Therefore,this research analyzes the four aspects of the impact of financial technology on commercial banks,and explores the challenges that financial technology brings to commercial banks’development strategies,traditional businesses,and business processes.For the measures taken,commercial banks need to improve the financial technology-related infrastructure,and improve the main functions of supervision technology and the transformation of cultural values.This research provides theoretical basis and implementation suggestions for the transformation of commercial banks through theoretical research.

公立医院数据资产入表财务管理路径与框架研究

目的在遵循国家行政事业性国有资产管理相关法律法规和财政部《关于加强行政事业单位数据资产管理的通知》的前提下,探索公立医院数据资产财务管理的路径。方法按照《政府会计准则》和数据资产相关业务模式的特性,借鉴企业数据资产入表的路径,构建公立医院数据资产入表、会计核算、报告全流程财务管理框架。结果通过构建公立医院数据资产财务管理框架,切实保障了公立医院国有资产的安全与完整,规范了医院数据资产的财务管理。结论医院数据资产入表的财务管理路径与框架的创新构建,对后续出台相关政策、开展相关研究具有借鉴意义。

数字普惠金融服务小微企业融资研究--以全国首个小微企业数字征信实验区为例

数字普惠金融将数字技术与普惠金融深度融合,为纾解小微企业融资困境提供了可行的新路。根据对全国首个小微企业数字征信实验区的案例剖析和区内588家小微企业的问卷调查结果,小微企业融资行为符合普惠金融“短、小、频、急”的典型特征,融资方式以信用贷款为主,资金用途多样,但是信息和信用不对称制约其融资可得性。实验区以服务小微企业为中心,构建综合金融服务体系、全国首个区块链数字征信平台、金融科技创新监管试点“三位一体”的运行模式,充分发挥了数字普惠金融的赋能效应。但是,要复制、推广实验区的建设经验,仍面临一些制约因素,因此要完善相关法律法规,稳步扩大金融科技创新监管试点,深化数字技术和数据要素融合应用,探索构建全国互联互通的数字征信平台和数字普惠金融“元宇宙”,以期在更大范围更好地服务小微企业融资和实体经济高质量发展。

欧盟金融数据治理:框架、重点与启示

加强金融数据治理,促进金融数据顺畅流动和高效使用,是经济高质量发展的内在要求。经过多年发展,欧盟积累了较为丰富的数据治理经验,并逐步建立起较为完善的数据治理体系。前期,欧盟强调个人数据保护;但在过去的3~4年,数字经济立法更加强调消除数据流动障碍,促进数据共享。目前,欧盟在数据权利界定、权利保护、跨境流动等方面已建立了较为完善的法律体系,并形成了从欧盟到成员国再到金融业最后到单一金融机构的统一性、垂直性和专业化的金融数据治理体系。我国可学习欧盟经验,加强对国家数字经济和数字金融发展的顶层设计,以法制建设为核心完善金融数据治理的长效机制,强化金融数据跨境安全流动机制,加强数据流动应用和价值挖掘,高质量发展数字市场和数字服务,提升数字金融的国际竞争力,推进金融强国建设。

大数据背景下造纸企业经营风险识别与管理优化研究

近年来,大数据技术的快速发展颠覆了造纸企业传统的风险控制模式,大数据技术在企业经营管理中实现降本增效的趋势越发明显。基于此,简述了造纸企业经营风险的种类与控制现状,探究了大数据时代给造纸企业经营风险控制带来的积极效应与消极影响,结合某些造纸企业经营实例进行经营风险的识别与分析,并基于大数据背景提出造纸企业经营风险识别与管理优化策略,以期建立科学、有效的经营风险预防与管控体系,提升造纸企业经营管理水平。