Intelligent Systems in Accounting, Finance and Management最新文献

{"title":"Multi-Objective Bayesian Optimization of Deep Reinforcement Learning for Environmental, Social, and Governance (ESG) Financial Portfolio Management","authors":"Eduardo C. Garrido-Merchán,&nbsp;Sol Mora-Figueroa,&nbsp;María Coronado-Vaca","doi":"10.1002/isaf.70008","DOIUrl":"https://doi.org/10.1002/isaf.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>Financial portfolio management focuses on the maximization of several objectives in a trading period related not only to the risk and performance of the portfolio but also to other objectives such as the environment, social, and governance (ESG) score of the portfolio. Regrettably, classic methods such as the Markowitz model do not take into account ESG scores but only the risk and performance of the portfolio. Moreover, the assumptions made by this model about the financial returns make it unfeasible to be applicable to markets with high volatility such as the technological sector. This paper investigates the application of deep reinforcement learning (DRL) for ESG financial portfolio management. DRL agents circumvent the issue of classic models in the sense that they do not make assumptions like the financial returns being normally distributed and are able to deal with any information like the ESG score if they are configured to gain a reward that makes an objective better. However, the performance of DRL agents has high variability, and it is very sensible to the value of their hyperparameters. Bayesian optimization is a class of methods that are suited to the optimization of black-box functions, that is, functions whose analytical expression is unknown and are noisy and expensive to evaluate. The hyperparameter tuning problem of DRL algorithms perfectly suits this scenario. As training an agent just for one objective is a very expensive period, requiring millions of timesteps, instead of optimizing an objective being a mixture of a risk-performance metric and an ESG metric, we choose to separate the objective and solve the multi-objective scenario to obtain an optimal Pareto set of portfolios representing the best trade-off between the Sharpe ratio and the ESG mean score of the portfolio and leaving to the investor the choice of the final portfolio. We conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The experiments are carried out in the Dow Jones Industrial Average (DJIA) and the NASDAQ markets in terms of the Sharpe ratio achieved by the agent and the mean ESG score of the portfolio. We compare the performance of the obtained Pareto sets in hypervolume terms illustrating how portfolios are the best trade-off between the Sharpe ratio and mean ESG score. Also, we show the usefulness of our proposed methodology by comparing the obtained hypervolume with one achieved by a random search methodology on the DRL hyperparameter space.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315027","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 Wisdom of Electronic Employee Crowds—Employee Reviews as a Data Source in Finance, Accounting, Economics, and Management Research: A Systematic Literature Review","authors":"Nils Gimpl","doi":"10.1002/isaf.70007","DOIUrl":"https://doi.org/10.1002/isaf.70007","url":null,"abstract":"<p>This study explores the wealth of information inherent in online employee reviews as an emerging resource in academic research. The focus is on the fields of finance, accounting, economics, and management, with an emphasis on how employee reviews contribute to our understanding of these areas. A systematic literature review (SLR) of 70 high-quality articles highlights the insights gleaned from employee reviews. Their data points, such as employee satisfaction, employee outlook, evaluation of culture, management, and colleagues, and text comments are mainly used in (1) explaining and predicting firm performance, (2) predicting and understanding performance and satisfaction of specific job groups, and (3) CSR- and ESG-related research. This SLR is important because the three main topics mentioned in which employee reviews are mainly used are spread across the fields of finance, accounting, economics, and management. This SLR therefore provides researchers with an important and necessary overview of the research already addressed across these fields. Furthermore, the SLR provides an overview of employer rating platforms utilized for academic research and methods used to harness employee reviews for research purposes. Here, a significant finding of this SLR is the predominant use of Glassdoor as a data source and the focus on US markets. The SLR concludes by proposing five potential avenues for future research, paving the way for a deeper understanding of the interplay between employee reviews (information) and organizational dynamics.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190856","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}

{"title":"Quality Management of Billing-Relevant Data in Logistics and Supply Chains: A Case Study","authors":"Luisa Naumann,&nbsp;Michael Hoeck","doi":"10.1002/isaf.70006","DOIUrl":"10.1002/isaf.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>As the trend toward the digitization of complex business processes continues, the relevance of data quality for corporate success has increased. Especially, in multistep processes where data are created, modified, and transferred between different systems and departments, ensuring high data quality through continuous improvement is a competitive advantage. The interdependencies within multistep processes make troubleshooting more difficult and complex, as is typically the case in supply chains and logistics. At present, research on improving the data quality in complex process chains is relatively limited compared to the vast body of literature in operations research. Therefore, this exploratory study begins with a literature review on the measurement and monitoring of data quality in logistics and supply chains. Based on the findings from literature and the identified total data quality management model, a case study was conducted. As the first measuring approach, a survey was distributed to 148 employees in the central logistics department of a multinational automobile manufacturer to analyze the quality of billing-relevant data in vehicle logistics. Although both subjective and objective approaches for measuring data quality have been described in the literature, automated techniques for continuous assessment of data quality have only increased in popularity in recent years. There is still potential for further research in the fields of process-oriented measurement and monitoring that consider the interdependencies between systems and departments involved in multistage logistics processes. In the logistics and supply chain literature, the most common dimensions of data quality that can be measured automatically were accuracy, completeness, consistency, and timeliness. Consistency and accuracy were also found critical in the reference case, which could potentially be the result of unsatisfactory system interfaces, data quality checks, and system landscape. The statements related to the data quality checks, the system landscape, and the understandability dimension were rated quite differently by the different departments. The survey helped identify weaknesses that should be further investigated and improved in the future to ensure continuous process operation and profitability.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786871","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":"Generating Synthetic Journal-Entry Data Using Variational Autoencoder","authors":"Ryoki Motai,&nbsp;Sota Mashiko,&nbsp;Yuji Kawamata,&nbsp;Ryota Shin,&nbsp;Yukihiko Okada","doi":"10.1002/isaf.70005","DOIUrl":"10.1002/isaf.70005","url":null,"abstract":"<p>In recent years, research studies have been conducted on analyzing journal-entry data using advanced visualization techniques and machine learning models. However, because of their highly confidential nature, these data are not disclosed externally, which can limit research and business opportunities to analyze the rich organizational information they contain. To address these problems, this study utilized a variational autoencoder to generate synthetic journal-entry data with statistical properties similar to those of actual data. The synthetic journal-entry data we created adhered to the fundamental structure of double-entry bookkeeping and were quantitatively evaluated for quality.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707211","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}

{"title":"Causal Network Representations in Factor Investing","authors":"Clint Howard,&nbsp;Harald Lohre,&nbsp;Sebastiaan Mudde","doi":"10.1002/isaf.70001","DOIUrl":"10.1002/isaf.70001","url":null,"abstract":"<p>This paper explores the application of causal discovery algorithms to factor investing, addressing recent criticisms of correlation-based models. We create novel causal network representations of the S&amp;P 500 universe and apply them to three investment scenarios. Our findings suggest that causal approaches can complement traditional methods in areas such as stock peer group identification, factor construction, and market timing. While causal networks offer new insights and sometimes outperform correlation-based methods in terms of risk-adjusted returns, they do not consistently surpass traditional approaches. The causal method though shows promise in identifying unique market relationships and potential hedging opportunities. However, its practical implementation presents challenges due to computational complexity and interpretation difficulties. Our study demonstrates the potential value of causal discovery in factor investing, while also identifying areas for further research and refinement.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689805","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}

{"title":"Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling","authors":"Waleed Soliman,&nbsp;Zhiyuan Chen,&nbsp;Colin Johnson,&nbsp;Sabrina Wong","doi":"10.1002/isaf.70004","DOIUrl":"10.1002/isaf.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>Changes in macroeconomic policies and market news have considerable influence over financial markets and subsequently impact their predictability. This study investigates whether incorporating sentiment analysis can enhance the accuracy of ETF price predictions. Specifically, we aim to predict ETF price movements using sentiment scores derived from news article summaries. Utilizing FinBERT for sentiment analysis, we quantify the sentiment of these summaries and integrate these scores into our predictive models. We employ DeepAR as a probabilistic model and compare its performance with LSTM in predicting ETF prices. The results demonstrate that DeepAR generally outperforms LSTM and that integrating sentiment scores significantly improves prediction accuracy. Given the promising outcomes, we also introduce a fixed “Seed” approach to ensure greater reliability and stability in our probabilistic predictions, addressing the need for robust sampling techniques in practical applications.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689783","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":"Open-Source Data-Driven Prediction of Environmental, Social, and Governance (ESG) Ratings Using Deep Learning Techniques","authors":"Hye Lim Lee,&nbsp;Jin Ho Hwang,&nbsp;Do Yeol Ryu,&nbsp;Jong Woo Kim","doi":"10.1002/isaf.70003","DOIUrl":"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}

{"title":"Can Deep Learning Models Enhance the Accuracy of Agricultural Price Forecasting? Insights From India","authors":"Ranjit Kumar Paul,&nbsp;Md Yeasin,&nbsp;C. Tamilselvi,&nbsp;A. K. Paul,&nbsp;Purushottam Sharma,&nbsp;Pratap S. Birthal","doi":"10.1002/isaf.70002","DOIUrl":"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}

{"title":"Evaluation of the Financial Distress of Hospitals Through Machine Learning: An Application of AI in Healthcare Industry","authors":"Nurettin Oner,&nbsp;Ferhat D. Zengul,&nbsp;Ismail Agirbas","doi":"10.1002/isaf.70000","DOIUrl":"https://doi.org/10.1002/isaf.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the intricate nature of hospital structures, the examination of factors contributing to financial distress necessitates more advanced methodologies than conventional approaches. Recent advancements in artificial intelligence, specifically machine learning algorithms, offer alternative means of analyzing patterns in these factors to assess hospital financial distress. This study employs various machine learning algorithms to forecast financial distress, as measured by the Altman Z score, for hospitals in Turkey. Prediction models were constructed using decision trees, random forests, K-nearest neighbors, artificial neural networks, support vector machines, and lasso regression algorithms. The findings indicate that the most effective classifiers for predicting hospital financial distress were lasso regression and random forest. Additionally, financial factors, competition, and socioeconomic development level emerged as significant determinants in forecasting hospital financial distress.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252710","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 Technological Innovation of the Metaverse in Financial Sector: Current State, Opportunities, and Open Challenges","authors":"Arianna D'Ulizia,&nbsp;Domenica Federico,&nbsp;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}