An extended sparsemax-linearmoving model with application to high-frequency financial data

There continues to be unfading interest in developing parametric max-stable processes for modelling tail dependencies and clustered extremes in time series data.However,this comes with some difficulties mainly due to the lack of models that fit data directly without transforming the data and the barriers in estimating a significant number of parameters in the existing models.In thiswork,we study the use of the sparsemaxima ofmovingmaxima(M3)process.After introducing random effects and hidden Fréchet type shocks into the process,we get an extended maxlinear model.The extended model then enables us to model cases of tail dependence or independence depending on parameter values.We present some unique properties including mirroring the dependence structure in real data,dealing with the undesirable signature patterns found in most parametricmax-stable processes,and being directly applicable to real data.ABayesian inference approach is developed for the proposed model,and it is applied to simulated and real data.

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.

Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?

High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.

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.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

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.

Forecasting High-Frequency Long Memory Series with Long Periods Using the SARFIMA Model

This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

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.

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。

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。