Intelligent Systems in Accounting, Finance and Management最新文献

Generating Synthetic Journal-Entry Data Using Variational Autoencoder

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-03-26 DOI: 10.1002/isaf.70005

Ryoki Motai, Sota Mashiko, Yuji Kawamata, Ryota Shin, Yukihiko Okada

引用次数: 0

Causal Network Representations in Factor Investing

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-03-25 DOI: 10.1002/isaf.70001

Clint Howard, Harald Lohre, Sebastiaan Mudde

引用次数: 0

Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling 通过情绪分析改进 ETF 预测：采用受控种子采样的 DeepAR 和 FinBERT 方法

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-03-25 DOI: 10.1002/isaf.70004

Waleed Soliman, Zhiyuan Chen, Colin Johnson, Sabrina Wong

引用次数: 0

Open-Source Data-Driven Prediction of Environmental, Social, and Governance (ESG) Ratings Using Deep Learning Techniques

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-03-23 DOI: 10.1002/isaf.70003

Hye Lim Lee, Jin Ho Hwang, Do Yeol Ryu, Jong Woo Kim

{"title":"Open-Source Data-Driven Prediction of Environmental, Social, and Governance (ESG) Ratings Using Deep Learning Techniques","authors":"Hye Lim Lee, Jin Ho Hwang, Do Yeol Ryu, Jong Woo Kim","doi":"10.1002/isaf.70003","DOIUrl":"https://doi.org/10.1002/isaf.70003","url":null,"abstract":"<p>The evaluation of ESG ratings by ESG rating agencies is time-consuming and requires the participation of numerous human specialists. In this paper, we propose a method for creating proxies of ESG scores by collecting corporate ESG news and publicly available ESG-related data using data crawling techniques and deep learning-based classification technology while minimizing human involvement. To validate the effectiveness of the proposed approach, we suggest three hypotheses. Two of them are related to the connection between open-source information and ESG ratings, while one concerns the link between proxy ESG rating and firm performance. To validate the effectiveness of the proposed approach, we conduct an empirical analysis based on 976 unique companies listed by the Korean Corporate Governance Agency (KCGS) from 2016 to 2019. Initially, we gather ESG indicators from open sources including disclosures and firms' news articles from a news portal site. We utilize Bidirectional Encoder Representations from Transformers (BERT) to classify news articles into environment, social, and governance categories and determine their sentiments. We confirm that ESG news sentiment and variables extracted from open-source data are related to ESG ratings. Furthermore, we find a significantly positive relationship between E, S, and G ratings predicted based on open-source data and Tobin's Q.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Deep Learning Models Enhance the Accuracy of Agricultural Price Forecasting? Insights From India

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-03-23 DOI: 10.1002/isaf.70002

Ranjit Kumar Paul, Md Yeasin, C. Tamilselvi, A. K. Paul, Purushottam Sharma, Pratap S. Birthal

{"title":"Can Deep Learning Models Enhance the Accuracy of Agricultural Price Forecasting? Insights From India","authors":"Ranjit Kumar Paul, Md Yeasin, C. Tamilselvi, A. K. Paul, Purushottam Sharma, Pratap S. Birthal","doi":"10.1002/isaf.70002","DOIUrl":"https://doi.org/10.1002/isaf.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>Forecasting agricultural commodity prices has been a long-standing challenge for researchers and policymakers. The diverse behaviors exhibited by price of different commodities, ranging from the high volatility, nonlinearity, and complexity of vegetables to the lower volatility and linear patterns of cereals. This different pattern necessitates the use of data-driven models to more precisely capture this complex behavior. This study aims to examine the efficiency of deep learning models in handling various types of price datasets. Three deep learning models, namely, gated recurrent unit (GRU), long short-term memory (LSTM), and recurrent neural network (RNN), are employed and compared against benchmark models including random walk with drift, autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and support vector regression (SVR). The monthly wholesale price data during January 2010 to December 2022 for 19 agricultural commodities across 143 markets in India have been utilized to illustrate the performance of models. Empirical comparison has been carried out by using different accuracy measures. The predictive accuracy is the highest for less-volatile crops such as cereals and pulses, while it is comparatively lower for crops with high-volatility like vegetables. The significant difference in prediction accuracy of different models has also been investigated with the help of Diebold Mariano test and its multivariate version. The study concluded that deep learning techniques outperformed machine learning and stochastic models across a wide range of commodities.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690068","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

Evaluation of the Financial Distress of Hospitals Through Machine Learning: An Application of AI in Healthcare Industry

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-01-16 DOI: 10.1002/isaf.70000

Nurettin Oner, Ferhat D. Zengul, Ismail Agirbas

引用次数: 0

The Technological Innovation of the Metaverse in Financial Sector: Current State, Opportunities, and Open Challenges 金融领域的 Metaverse 技术创新：现状、机遇与挑战

Intelligent Systems in Accounting, Finance and Management Pub Date : 2024-09-03 DOI: 10.1002/isaf.1566

Arianna D'Ulizia, Domenica Federico, Antonella Notte

{"title":"The Technological Innovation of the Metaverse in Financial Sector: Current State, Opportunities, and Open Challenges","authors":"Arianna D'Ulizia, Domenica Federico, Antonella Notte","doi":"10.1002/isaf.1566","DOIUrl":"https://doi.org/10.1002/isaf.1566","url":null,"abstract":"<p>Metaverse is an emerging digital space that uses innovative technologies to allow users to facilitate building relationships virtually and to create new interaction opportunities. Even, the financial sector has been disrupted by the metaverse involving digital assets, cryptocurrencies, blockchain technology, and decentralized finance. The objective of this paper is to focus on novel intelligent systems technologies with the potential for application in the financial area to have a better knowledge of the current research topics, challenges, and future directions. A systematic literature review was conducted analyzing papers on technological innovation of the metaverse in financial sector. Following the PRISMA methodology, we have selected 29 primary studies from five scientific databases to be included in the review. The results show that 11 types of innovative metaverse technologies are applied in the financial sector, developing financial innovations, among which the most discussed is cryptocurrency. Among the opportunities that the use of the metaverse brings to the financial sector, the reduction of transaction costs is the most discussed. Finally, five open challenges in the use of metaverse technologies in the financial sector have been identified, relating to the use of data, the application of technologies, social integration, financial innovation, and regulatory compliance. Based on this study, recommendations on future research directions are provided to the scientific community.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Liquidity forecasting at corporate and subsidiary levels using machine learning 利用机器学习在公司和子公司层面进行流动性预测

Intelligent Systems in Accounting, Finance and Management Pub Date : 2024-08-09 DOI: 10.1002/isaf.1565

Vinay Singh, Bhasker Choubey, Stephan Sauer

引用次数: 0

Identification of fraudulent financial statements through a multi-label classification approach 通过多标签分类法识别欺诈性财务报表

Intelligent Systems in Accounting, Finance and Management Pub Date : 2024-06-18 DOI: 10.1002/isaf.1564

Maria Tragouda, Michalis Doumpos, Constantin Zopounidis

{"title":"Identification of fraudulent financial statements through a multi-label classification approach","authors":"Maria Tragouda, Michalis Doumpos, Constantin Zopounidis","doi":"10.1002/isaf.1564","DOIUrl":"https://doi.org/10.1002/isaf.1564","url":null,"abstract":"<p>Although the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi-label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi-label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting carbon and oil price returns using hybrid models based on machine and deep learning 利用基于机器学习和深度学习的混合模型预测碳和石油价格回报

Intelligent Systems in Accounting, Finance and Management Pub Date : 2024-06-05 DOI: 10.1002/isaf.1563

Jesús Molina-Muñoz, Andrés Mora-Valencia, Javier Perote

{"title":"Predicting carbon and oil price returns using hybrid models based on machine and deep learning","authors":"Jesús Molina-Muñoz, Andrés Mora-Valencia, Javier Perote","doi":"10.1002/isaf.1563","DOIUrl":"https://doi.org/10.1002/isaf.1563","url":null,"abstract":"<div>\u0000 \u0000 <p>Predicting carbon and oil prices is recently gaining relevance in the climate change literature. This is due to the fact that conventional energy market analysis and the design of mechanisms for climate change mitigation constitute key variables for artificial carbon markets. Yet, modelling non-linear effects in time series remains a major challenge for carbon and oil price forecasting. Hence, hybrid models seem to be appealing alternatives for this purpose. This study evaluates the performance of 12 hybrid models, which weigh results from random forest, support vector machine, autoregressive integrated moving average and the non-linear autoregressive neural network models. The weights are determined by (i) assuming equal weights, <span>(</span>ii) using a neural network to optimise individual weights and (iii) employing deep learning techniques. The findings of our work confirm the salient characteristics of modelling the non-linear effects of time series and the potential of hybrid models based on neural networks and deep learning in predicting carbon and oil price returns. Furthermore, the best results are obtained from hybrid models that combine machine learning and traditional econometric techniques as inputs, which capture the linear and non-linear effects of time series.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264581","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