Data Science in Finance and Economics最新文献

The Cowles–Jones test with unspecified upward market probability 未指明市场向上概率的考尔斯-琼斯检验

Data Science in Finance and Economics Pub Date : 2023-01-01 DOI: 10.3934/dsfe.2023019

Markus Haas

引用次数: 0

Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models 运用FIAPARCH、HYGARCH和FIGARCH模型量化股票市场的风险价值

Data Science in Finance and Economics Pub Date : 2023-01-01 DOI: 10.3934/dsfe.2023022

Moses Khumalo, Hopolang Mashele, Modisane Seitshiro

{"title":"Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models","authors":"Moses Khumalo, Hopolang Mashele, Modisane Seitshiro","doi":"10.3934/dsfe.2023022","DOIUrl":"https://doi.org/10.3934/dsfe.2023022","url":null,"abstract":"<abstract>The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk. This research study was designed to investigate the effectiveness of the fractionally integrated asymmetric power autoregressive conditional heteroskedasticity contrasted with long-memory GARCH-type models, such as the fractionally integrated generalized autoregressive conditional heteroskedasticity and the hyperbolic generalized autoregressive conditional heteroskedasticity for producing the measure of market risk known as the value at risk. These long-memory GARCH-type models assume that the distributions of the index returns follow normal, student-$ t $, skewed student-$ t $ and generalized error distributions. The historical closing price time series of the Johannesburg Stock Exchange all share, the mining and the banking indices are considered. The value at risk and its backtesting for short and long trading positions on the different confident levels are computed and they correspond to the right and left quantiles of the return distributions, respectively. The results reveal that FIAPARCH with a standard student-$ t $ distribution is an appropriate model for producing a robust value at risk in the context of mining and banking indices. Alternatively, FIGARCH with the assumed skewed student-$ t $ distribution model is a good fit to produce a value at risk for the Johannesburg Stock Exchange All Share Index.</abstract>","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135611754","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

Using deep learning to enhance business intelligence in organizational management 利用深度学习增强组织管理中的商业智能

Data Science in Finance and Economics Pub Date : 2023-01-01 DOI: 10.3934/dsfe.2023020

Sina Gholami, Erfan Zarafshan, Sheikh Sheikh, Shib Sankar Sana

{"title":"Using deep learning to enhance business intelligence in organizational management","authors":"Sina Gholami, Erfan Zarafshan, Sheikh Sheikh, Shib Sankar Sana","doi":"10.3934/dsfe.2023020","DOIUrl":"https://doi.org/10.3934/dsfe.2023020","url":null,"abstract":"<abstract> Business intelligence (BI) is crucial in organizational management, providing insights that enable informed decision-making. Traditional BI approaches, however, are limited in handling the vast amounts of data generated by organizations today. Deep learning, a subfield of machine learning, has shown great potential in improving BI through automated analysis of complex and large data sets. In this study, we explore the effectiveness of deep learning in enhancing BI for organizational management. We evaluate the accuracy and F-score of our proposed deep learning model against traditional BI methods in a real-world scenario. Our dataset contains a large volume of unstructured text data from customer feedback forms, which presents significant challenges for traditional BI approaches. Our deep learning model is trained using a convolution neural network (CNN) architecture to classify customer feedback into positive and negative sentiment categories. The model achieved an accuracy of 88% and an F-score of 0.86, outperforming traditional BI methods such as rule-based systems and sentiment analysis algorithms. Furthermore, our model's ability to handle unstructured data highlights its potential for processing diverse data types beyond structured data, commonly used in traditional BI. </abstract>","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052950","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

Enhancing classification performance in imbalanced datasets: A comparative analysis of machine learning models 增强不平衡数据集的分类性能:机器学习模型的比较分析

Data Science in Finance and Economics Pub Date : 2023-01-01 DOI: 10.3934/dsfe.2023021

Lindani Dube, Tanja Verster

{"title":"Enhancing classification performance in imbalanced datasets: A comparative analysis of machine learning models","authors":"Lindani Dube, Tanja Verster","doi":"10.3934/dsfe.2023021","DOIUrl":"https://doi.org/10.3934/dsfe.2023021","url":null,"abstract":"<abstract>In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.</abstract>","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307754","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

Intelligence and global bias in the stock market 股票市场的情报和全球偏见

Data Science in Finance and Economics Pub Date : 2022-10-28 DOI: 10.3934/dsfe.2023011

Kazuo Sano

引用次数: 0

Financial forecasting using stochastic models: reference from multi-commodity exchange of India 基于随机模型的财务预测:来自印度多商品交易所的参考

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021011

Paarth Thadani

{"title":"Financial forecasting using stochastic models: reference from multi-commodity exchange of India","authors":"Paarth Thadani","doi":"10.3934/dsfe.2021011","DOIUrl":"https://doi.org/10.3934/dsfe.2021011","url":null,"abstract":"\u0000 Non-linear forecasting models, including artificial neural networks, are popularly adopted in financial forecasting. These models require high computational infrastructure and resources for practical training and deployment, unlike linear forecasting models. However, this level of infrastructure and resources is not accessible to most market participants. This paper lays down a systematic approach to build a simplistic and effective forecasting model, which empowers investors to make informed decisions using a minimal computational infrastructure. Natural gas futures traded in the Multi-Commodity Exchange of India were identified as an appropriate asset for the study considering the massive, expected movement in energy consumption pattern in India. We have used data analytics and statistical techniques to identify optimal training strategies and features, which resulted in an accurate linear forecasting model. Data analytics also helped to accurately establish the context of the study with the identification of a positive shift in the sentiments of market participants of the asset, which was duly verified using the substantial change in the valuation of the asset. While formulating the forecasting model, several avenues, including identifying weekly and yearly patterns, introducing seasonality and exogenous variables were explored. The paper concludes that such attempts to reduce external dependency and segregate noise data result into better model performance with minimal computational resource and infrastructure.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"7 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":"123158408","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

The usage of logistic regression and artificial neural networks for evaluation and predicting property-liability insurers' solvency in Egypt 使用逻辑回归和人工神经网络评估和预测埃及财产责任保险公司的偿付能力

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2021012

Eid Elghaly Hassan, Diping Zhang

{"title":"The usage of logistic regression and artificial neural networks for evaluation and predicting property-liability insurers' solvency in Egypt","authors":"Eid Elghaly Hassan, Diping Zhang","doi":"10.3934/dsfe.2021012","DOIUrl":"https://doi.org/10.3934/dsfe.2021012","url":null,"abstract":"\u0000 Unlike prior solvency prediction studies conducted in Egypt, this study aims to set up a real picture of companies' financial performance in the Egyptian insurance market. Therefore, 11 financial ratios commonly used by NAIC, AM BEST Company, and S & P Global Ratings were calculated for all property-liability insurance companies in Egypt from 2010 to 2020. They have been used to measure those companies' financial performance efficiency levels by comparing these ratios with the international standard limits. The financial analysis results for those companies revealed that property-liability insurers in Egypt do not have the same level of financial performance efficiency where those companies are classified into three groups: excellent, good, and poor. Furthermore, this paper investigates using the stepwise logistic regression model to determine the most factors among these selected financial ratios that influence those companies' financial performance. The results suggest that only three ratios were statistically significant predictors: \"Risk retention rate\", \u0000\"Insurance account receivable to total assets\", and \"Net profit after tax to total assets\". Finally, this paper presents the multi-layers artificial neural network with a backpropagation algorithm as a new solvency prediction model with perfect classifying accuracy of 100%. The trained ANN could predict the next fiscal year with a prediction accuracy of 91.67%, and this percent is a good and favorable result comparing to other solvency prediction models used in Egypt.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"350 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":"116688120","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

Research on stock price prediction from a data fusion perspective 基于数据融合视角的股价预测研究

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2023014

Ai-hua Li, Qinyan Wei, Yong Shi, Zhidong Liu

{"title":"Research on stock price prediction from a data fusion perspective","authors":"Ai-hua Li, Qinyan Wei, Yong Shi, Zhidong Liu","doi":"10.3934/dsfe.2023014","DOIUrl":"https://doi.org/10.3934/dsfe.2023014","url":null,"abstract":"\u0000 Due to external factors such as political influences, specific events and sentiment information, stock prices exhibit randomness, high volatility and non-linear characteristics, making accurate predictions of future stock prices based solely on historical stock price data difficult. Consequently, data fusion methods have been increasingly applied to stock price prediction to extract comprehensive stock-related information by integrating multi-source heterogeneous stock data and fusing multiple decision results. Although data fusion plays a crucial role in stock price prediction, its application in this field lacks comprehensive and systematic summaries. Therefore, this paper explores the theoretical models used in each level of data fusion (data-level, feature-level and decision-level fusion) to review the development of stock price prediction from a data fusion perspective and provide an overall view. The research indicates that data fusion methods have been widely and effectively used in the field of stock price prediction. Additionally, future directions are proposed. For better performance of data fusion in the field of stock price prediction, future work can broaden the scope of stock-related data types used and explore new algorithms such as natural language processing (NLP) and generative adversarial networks (GAN) for text information processing.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"45 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":"116762136","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: 1

Multi-level stacking of LSTM recurrent models for predicting stock-market indices 预测股市指数的LSTM递归模型的多级叠加

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022007

Fatima Tfaily, Mohamad M. Fouad

引用次数: 1

Impact of blockchain-enabled analytics as a tool to revolutionize the banking industry 支持区块链的分析工具对银行业革命的影响

Data Science in Finance and Economics Pub Date : 1900-01-01 DOI: 10.3934/dsfe.2022014

M. Sazu, S. Jahan

{"title":"Impact of blockchain-enabled analytics as a tool to revolutionize the banking industry","authors":"M. Sazu, S. Jahan","doi":"10.3934/dsfe.2022014","DOIUrl":"https://doi.org/10.3934/dsfe.2022014","url":null,"abstract":"\u0000 Blockchain technology is disrupting the financial services industry and leading to extended big data applications in the banking sectors. Using blockchain and big data technology, banking industries can greatly improve decision-making, efficiency, and transparency. Nevertheless, there is a gap in research on the use of blockchain and big data technologies in banking systems from an academic viewpoint. To address the gap, we present a thorough overview of the impact of blockchain and big data technologies on banking systems. Although some banks have started blockchain development in small groups or isolation, this study was designed as a comprehensive exploration into a few facets of banking with blockchain technology to tackle the difficulties currently impeding the adoption of such technologies into banking systems throughout the world. This study shows that implementing big data and blockchain technology can significantly impact the security, speed and cost of transactions for banks. Further research could be conducted over a long-time span to capture the longitudinal impact of blockchain and big data technologies on banking in terms of the operating costs, profitability and scalability.\u0000","PeriodicalId":290300,"journal":{"name":"Data Science in Finance and Economics","volume":"9 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":"125547769","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: 21