Data Science in Finance and Economics最新文献_第4页

A review of data mining methods in financial markets 金融市场数据挖掘方法综述

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021020

Haihua Liu, S. Huang, Peng Wang, Zejun Li

{"title":"A review of data mining methods in financial markets","authors":"Haihua Liu, S. Huang, Peng Wang, Zejun Li","doi":"10.3934/dsfe.2021020","DOIUrl":"https://doi.org/10.3934/dsfe.2021020","url":null,"abstract":"Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"29 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":"115256676","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}

引用次数: 4

Research on measurement and disequilibrium of manufacturing digital transformation: Based on the text mining data of A-share listed companies 制造业数字化转型的测度与不均衡性研究——基于a股上市公司文本挖掘数据

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023003

Chong Li, Guoqiong Long, Shuai Li

{"title":"Research on measurement and disequilibrium of manufacturing digital transformation: Based on the text mining data of A-share listed companies","authors":"Chong Li, Guoqiong Long, Shuai Li","doi":"10.3934/dsfe.2023003","DOIUrl":"https://doi.org/10.3934/dsfe.2023003","url":null,"abstract":"\u0000\u0000Quantitative analysis of digital transformation is an important part of relevant research in the digital field. Based on the annual report data of China's manufacturing listed companies from 2011 to 2019, this study applies cloud computing to the mining and analysis of text data, and uses the Term Frequency-Inverse Document Frequency method under machine learning to measure the digital transformation index value of manufacturing enterprises. The results show that: (1) On the whole, the current pace of digital transformation of manufacturing enterprises continues to accelerate, and the digital transformation of manufacturing has gradually spread from the eastern coastal areas to the central and western inland areas. (2) In horizontal comparison, among the five types of \"ABCDE\" digital modules constructed, artificial intelligence develops the fastest, cloud computing index value is second, and block chain value is the smallest. In vertical comparison, the leading provinces such as Beijing, Guangdong, and Shanghai have certain stability and a solid leading position, and there are occasional highlights in the central and western provinces. (3) In terms of polarization distribution, the digitalization of the manufacturing industry has obvious multi-peak patterns, showing the phenomenon of multi-polarization of digital services. The eastern region has both aggregate advantages and equilibrium disadvantages. (4) In terms of industry differences, the level of digital transformation in the high-end manufacturing industry is significantly higher than that in the mid-end and low-end industries. On the ownership attributes of enterprise digital transformation, private enterprises are the highest, followed by foreign-funded enterprises, and state-owned enterprises are the lowest. This research provides theoretical enlightenment and factual reference for manufacturing enterprises to carry out digital activities.\u0000\u0000","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":"128293004","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}

引用次数: 0

Different GARCH models analysis of returns and volatility in Bitcoin 对比特币收益率和波动率的不同 GARCH 模型分析

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021003

Changlin Wang

引用次数: 3

Cross-validation research based on RBF-SVR model for stock index prediction 基于RBF-SVR模型的股票指数预测交叉验证研究

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021001

F. Zhou

引用次数: 2

A topological based feature extraction method for the stock market 基于拓扑的股票市场特征提取方法

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023013

Chen Chang, Hongfei Lin

引用次数: 0

Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession 利用机器学习进行破产预测，并将其应用于COVID-19经济衰退的案例

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021010

Aditya Narvekar, D. Guha

引用次数: 3

Daily LGARCH model estimation using high frequency data 基于高频数据的每日LGARCH模型估计

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021009

Xiaoling Chen, Xingfa Zhang, Yuan Li, Q. Xiong

引用次数: 0

Estimating credit default probabilities using stochastic optimisation 用随机优化方法估计信用违约概率

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021014

Dominic Joseph

引用次数: 1

Expanding insurability through exploiting linear partial information 利用线性部分信息扩大保险范围

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022001

P. Zweifel

引用次数: 2

Correlation structure networks of stock market during terrorism: evidence from Pakistan 恐怖主义时期股票市场的相关结构网络:来自巴基斯坦的证据

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021007

B. Memon, H. Yao

引用次数: 2