Data Science in Finance and Economics最新文献

{"title":"How does the digital economy affect industrial eco-efficiency? Empirical evidence from China","authors":"Lu Liu, Ming Liu","doi":"10.3934/dsfe.2022019","DOIUrl":"https://doi.org/10.3934/dsfe.2022019","url":null,"abstract":"\u0000 Industry is a sector with large energy consumption and pollutant emissions. Improving industrial eco-efficiency is crucial to energy conservation and pollution reduction. The digital economy has developed rapidly in recent years. However, there is a lack of research on the specific relationship between the digital economy and industrial eco-efficiency. This study measured the industrial eco-efficiency of 30 provinces in China from 2010 to 2020, through a super-efficiency slack-based measure (SBM) considering desirable outputs. By constructing a two-way fixed effect model and a panel quantile model, this study explored the effects of the digital economy on industrial eco-efficiency on a national scale. Furthermore, this study conducted grouping regression and investigated the heterogeneous impacts of the digital economy on industrial eco-efficiency. Finally, this study built a spatial Durbin model to explore the spatial effects of digital economy on industrial eco-efficiency. According to the empirical results, this study yielded the following conclusions. First, the digital economy has a significantly positive effect on industrial eco-efficiency at the national scale, with diminishing marginal returns. Second, the effects of the digital economy on industrial eco-efficiency are significantly heterogeneous on a regional scale. For eastern regions, the effects of the digital economy on industrial eco-efficiency are significantly positive, while they are negative for western regions. Third, the spillover effect of the digital economy on industrial eco-efficiency is not significant in China, indicating that there is digital isolation.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122403458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended dynamic mode decomposition for cyclic macroeconomic data","authors":"J. Leventides, E. Melas, C. Poulios","doi":"10.3934/dsfe.2022006","DOIUrl":"https://doi.org/10.3934/dsfe.2022006","url":null,"abstract":"We apply methods from the Koopman operator theory, Extended Dynamic Mode Decomposition and machine learning in the study of business cycle models. We use a simple non-linear dynamical system whose main merit is that in the appropriate parameter space sector predicts intrinsically business cycles which in the phase space are structurally stable limit cycles. Our objective is to approximate this system with a finite dimensional linear model which is defined on some augmented state space. We approximate so the trajectories of the system and obtain an alternative non-perturbative description of the system which can be used for prediction and control. This approach can also be applied to other models as well as to real data.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of time-varying risk on stock returns: an experiment of cubic piecewise polynomial function model and the Fourier Flexible Form model","authors":"Fangzhou Huang, Jiao Song, N. Taylor","doi":"10.3934/dsfe.2021008","DOIUrl":"https://doi.org/10.3934/dsfe.2021008","url":null,"abstract":"\u0000 With fast evolving econometric techniques being adopted in asset pricing, traditional linear asset pricing models have been criticized by their limited function on capturing the time-varying nature of data and risk, especially the absence of data smoothing is of concern. In this paper, the impact of data smoothing is explored by applying two asset pricing models with non-linear feature: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are performed on US stock returns as an experiment. The traditional beta coefficient is treated asymmetrically as downside beta and upside beta in order to capture corresponding risk, and further, to explore the risk premia attached in a cross-sectional context. It is found that both models show better goodness of fit comparing to classic linear asset pricing model cross-sectionally. When appropriate knots and orders are determined by Akaike Information Criteria (AIC), the goodness of fit is further improved, and the model with both CPPF and FFF betas employed showed the best fit among other models. The findings fill the gap in literature, specifically on both investigating and pricing the time variation and asymmetric nature of systematic risk. The methods and models proposed in this paper embed advanced mathematical techniques of data smoothing and widen the options of asset pricing models. The application of proposed models is proven to superiorly provide high degree of explanatory power to capture and price time-varying risk in stock market.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The risk-return relationship in South Africa: tail optimization of the GARCH-M approach","authors":"Nitesha Dwarika","doi":"10.3934/dsfe.2022020","DOIUrl":"https://doi.org/10.3934/dsfe.2022020","url":null,"abstract":"\u0000 The risk-return relationship is of fundamental significance in the field of economics and finance. It is used to structure investment strategies, allocate resources, as well as assist in the construction of policy and regulatory frameworks. The accurate forecast of the risk-return relationship ensures sound financial decisions, whereas an inaccurate one can underestimate risk and thus lead to losses. The GARCH-M approach is one of the foremost models used in South African literature to investigate the risk-return relationship. This study made a novel and significant contribution, on a local and international level, as it was the first study to investigate GARCH-M type models with different innovation distributions. This study analyzed the JSE ALSI returns of the South African market for the sample period from 05 October 2004 to 05 October 2021. Results revealed that the EGARCH (1, 1)-M with the Skewed Student-t distribution (Skew-t) is optimal relative to the standard GARCH, APARCH and GJR. However, the EGARCH-M Skew-t failed to capture the financial data's asymmetric, volatile and random nature. To improve forecast accuracy, this study applied different nonnormal innovation distributions: the Pearson Type Ⅳ distribution (PIVD), Generalized Extreme Value distribution (GEVD), Generalized Pareto distribution (GPD) and Stable. Model diagnostics revealed that the nonnormal innovation distributions adequately captured asymmetry. The Value at Risk and backtesting procedure found that the PIVD, followed by Stable, outperformed the Extreme Value Theory distributions (GEVD and GPD). Thus, investors, risk managers and policymakers would opt to use the EGARCH-M in combination with the PIVD when modelling the risk-return relationship. The main contribution of this study was to confirm that applying GARCH type models with the conventional and normal type distributions, to a volatile emerging market, is considered ineffective. Therefore, this study recommended the exploration of other innovation distributions, that were not included in the scope of this study, for future research purposes.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131884083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theory and application of artificial intelligence in financial industry","authors":"Yuxin Li, Jizheng Yi, Huanyu Chen, Duanxiang Peng","doi":"10.3934/dsfe.2021006","DOIUrl":"https://doi.org/10.3934/dsfe.2021006","url":null,"abstract":": Artificial Intelligence (AI) is deemed to be the commanding point of science and technology in the next era. In recent years, with the enhancement of computer computing power, the improvement of the quantity and quality of big data, and the important breakthroughs in many research fields such as machine learning and speech recognition, AI technology has developed rapidly and has been widely used in all walks of life. In the financial industry, the application of AI technology in risk control, marketing, customer service, transaction, operation, and product optimization of financial institutions is becoming increasingly mature, and some new business models have been created. Starting from the application status and significance of AI in the international financial field, this paper expounds on the application, status quo, and development trend of AI in the financial industry. Then, in view of the risks and practical challenges existing in the development process of AI, based on the reality of international financial development, this paper summarizes the measures to promote the in-depth, healthy, and sustainable development of AI in the financial market. This paper aims to let readers understand the development status of AI in the financial field, and also provide theoretical reference for scholars in this field.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Frequency Trading with Machine Learning Algorithms and Limit Order Book Data","authors":"M. Mangat, E. Reschenhofer, Thomas Stark, C. Zwatz","doi":"10.3934/dsfe.2022022","DOIUrl":"https://doi.org/10.3934/dsfe.2022022","url":null,"abstract":"In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be used for intraday trading. For our empirical analysis, we first get 50 raw features from the LOBSTER message file and order book file of the iShares Core S & P 500 ETF for the time period from 27.06.2007 to 30.04.2019 and then construct 18 higher-level features (aggregated to 5 minutes frequency) which serve as predictors. Using straightforward specifications for the machine learning procedures and thereby avoiding excessive data snooping, we find that these procedures are unable to find high dimensional patterns in the order book that could be used for trading purposes. The observed significant predictability is mainly due to the inclusion of only one variable, namely the last price change, and is probably too small to ensure profitability once transaction costs are taken into account.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133531498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient state estimation strategies for stochastic optimal control of financial risk problems","authors":"Yue Yuin Lim, S. Kek, K. Teo","doi":"10.3934/dsfe.2022018","DOIUrl":"https://doi.org/10.3934/dsfe.2022018","url":null,"abstract":"In this paper, a financial risk model, which is formulated from the risk management process of financial markets, is studied. By considering the presence of Gaussian white noise, the financial risk model is reformulated as a stochastic optimal control problem. On this basis, two efficient computational approaches for state estimation, which are the extended Kalman filter (EKF) and unscented Kalman filter (UKF) approaches, are applied. Later, based on the state estimate given by the EKF and UKF approaches, a linear feedback control policy is designed from the stationary condition. For illustration, some parameter values and the initial conditions of the financial risk model are used for the simulation of the stochastic optimal control problem. From the results, it is noticed that the UKF algorithm provides a better state estimate with a smaller value of the sum of squared errors (SSE) as compared to the SSE given by the EKF algorithm. Thus, the estimated output trajectory has a high accuracy that is close to the real output. Moreover, the control effort assists in estimating the state dynamics at the minimum cost. In conclusion, the efficiency of the computational approaches for optimal control of the financial risk model has been well presented.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116126717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research of daily stock closing price prediction for new energy companies in China","authors":"Qian Shen, Yifan Zhang, Jiale Xiao, Xuhua Dong, Zifei Lin","doi":"10.3934/dsfe.2023002","DOIUrl":"https://doi.org/10.3934/dsfe.2023002","url":null,"abstract":"Nowadays, China is developing new energy industries to reduce carbon emissions to meet the challenge of world climate change, so investors can consider to invest in stocks of Chinese new energy companies to gain income. In order to study how to forecast stock closing prices of new energy companies in China, we have chosen 12 representative companies, and first used autoregressive univariate time series models to predict the trends of the stock closing prices in the next month. The results show that Seasonal Autoregressive Integrated Moving Average model has the best out-of-sample trend prediction effect. Second, we use multivariate time series forecasting models to predict the stock closing prices of each day through external variables. The results show that Temporal Convolutional Attention Neural Networks has the best effect of out-of-sample prediction. We recommend that investors who are interested in investing in new energy companies in China first use the Seasonal Autoregressive Integrated Moving Average model to predict the short-term stock closing price trend in the future, and then use the Temporal Convolutional Attention Neural Networks model to predict the stock closing price on the next day to decide whether to invest.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123096241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text-Based Chatbot in Financial Sector: A Systematic Literature Review","authors":"Hana Demma Wube, Sintayehu Zekarias Esubalew, Firesew Fayiso Weldesellasie, Taye Girma Debelee","doi":"10.3934/dsfe.2022011","DOIUrl":"https://doi.org/10.3934/dsfe.2022011","url":null,"abstract":"\u0000 Text-based chatbots are implemented in the financial sector to enhance the relationship between the customer and services provided by the sector, and also to address external challenges and customer requirements. The chatbot technology in the financial sector serves to examine customers' frequently asked questions and the representation of the process using machine learning. In light of this, this study presents a comprehensive systematic literature review of articles focused on text-based chatbots in the financial sector. It describes the understanding of chatbots in the financial sector in terms of implementation, adoption intention, attitude toward use and acceptance; it also describes how people experience chatbots, specifically in terms of perception, expectation and trust, as well as how they are engaging and emotionally motivated; management of the security and privacy vulnerabilities of the chatbots; and identifies the potential strategies that can hinder the efficient, successful evolution of chatbots in the financial sector. Finally, the main findings regarding the use of text chatbots in the financial sector are presented; additionally, the open issues in current research are highlighted and a number of research opportunities that can be pursued in the future are suggested.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there any heterogeneous impact of mandatory disclosure on corporate social responsibility dimensions? Evidence from a quasi-natural experiment in China","authors":"Shuanglian Chen, Cunyi Yang, Khaldoon Albitar","doi":"10.3934/dsfe.2021015","DOIUrl":"https://doi.org/10.3934/dsfe.2021015","url":null,"abstract":"\u0000 The corporate social responsibility (CSR) report is an important carrier of non-financial information disclosure of enterprises and an important bridge of communication between enterprises and interested parties. Compulsory disclosure has promoted the improvement of CSR levels to some extent. While, for interested parties, their attention to various dimensions of CSR has significant differences, which leads to the heterogeneous impact of mandatory disclosure policy on its different dimensions. Through regression discontinuity design model (RDD), as well as using quasi-natural experiments of Chinese mandatory disclosure policies issued in 2008, we are going to get the following conclusions by analyzing the heterogeneous impact of mandatory disclosure on CSR with the environment (CER), social (SOC) and economic (ECO) three-dimension on the basis of verifying that mandatory disclosure policy has a positive impact on CSR. (1) The effects of mandatory disclosure on the three dimensions of CSR are heterogeneous, that is, the significant effects and directions are significantly different in the three dimensions. (2) The heterogeneity of mandatory disclosure on CSR is reflected in the changing trend, and there is no significant difference at the turning point of the trend. (3) The heterogeneity of the impact mechanism of mandatory disclosure on CSR is reflected in the different mediating variables of policy on different dimensions impact, that is, the mediating variables of CER and ECO are the environmental disclosure information and return on assets. (4) The impact of mandatory disclosure on different dimensions of CSR is heterogeneous when the nature of industries and property rights are different.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126077399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}