Intelligent Systems in Accounting, Finance and Management最新文献

{"title":"Prediction of Volatility Using Monetary Rate and GARCH-LSTM Hybrid Model","authors":"Jyoti Ranjan,&nbsp;C. Anirvinna","doi":"10.1002/isaf.70013","DOIUrl":"https://doi.org/10.1002/isaf.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>Predicting volatility is very important for the financial markets as it helps to determine risk and decision-making. Predicting volatilities for such stock indices, which include the Nifty 50, is important for traders, investors, and policymakers. In this study, advanced hybrid models are used to predict the volatility of the Nifty 50 index over intervals of 1, 7, 14, and 21 days. The GJR-GARCH-LSTM and the GARCH-LSTM are two hybrid models that forecast the volatility of the Nifty 50. The effect of including the cash reserve ratio (CRR) in the analysis is also looked at. As the forecast horizon grows, the results show decreased prediction accuracy. The mean squared error (MSE) increased by 0.78% from the 1-day to the 7-day forecast, decreased by 2.63% between the 1-day and 7-day projections, rose by about 55% from the 7-day to the 14-day forecast, and grew by 56% between the 14-day and 21-day projections. The GJR-GARCH-LSTM model had better results compared to the simple GARCH-LSTM hybrid model. The novelty of this study is in building and validating hybrid models, specifically the GJR-GARCH-LSTM, to predict Nifty 50 index volatility and using the CRR as a macroeconomic explanatory variable. Different from current literature, which tends to use hybrid models in a generic sense, this paper adapts the model to the Indian financial environment and shows the additional predictive power of monetary policy determinants such as CRR.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758617","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":"Developing an Accounting Virtual Assistant Through Supervised Fine-Tuning (SFT) of a Small Language Model (SLM)","authors":"Mario Zupan","doi":"10.1002/isaf.70011","DOIUrl":"https://doi.org/10.1002/isaf.70011","url":null,"abstract":"<p>The development of an in-house accounting bot—an artificial intelligence (AI) assistant capable of generating internally structured bookkeeping double-entry posting schemes—is explored in this paper. The processes of curating a suitable dataset, selecting, and fine-tuning a seven-billion-parameter language model, categorized as a small language model (SLM) (SLMs typically refer to models with fewer than 10 billion parameters, whereas medium-sized models often have 14B parameters, and large-scale models exceed 70B), are described. A human-evaluated benchmark is also presented to assess model performance. To achieve efficient supervised fine-tuning (SFT), low-rank adaptation (LoRA) was employed, significantly reducing memory requirements by using a small set of trainable parameters while maintaining model expressiveness. The process of backpropagation was further optimized using Unsloth, a high-performance training framework designed for efficient video memory usage and flash attention mechanisms, which accelerates adaptation and reduces memory overhead. The model whose layers were updated is called QwenCoder2.5. It was selected with the presumption that it would be able to learn how to generate and examine bookkeeping patterns generated by accounting information system (AIS) over a 17-year history. This proof of concept aims to support researchers and practitioners exploring the integration of generative AI in accounting by providing insights into both the benefits and challenges of AI-driven automation in bookkeeping tasks. The study demonstrates how an SLM can be fine-tuned on a proprietary dataset of journal posting schemes to assist accountants, auditors, and financial analysts while also facilitating synthetic data generation. Challenges related to AI, data preprocessing, fine-tuning optimization, and evaluation methodology are introduced and examined.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716652","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":"Social Media, Traditional News and Stock Returns: A Causal Mediation Analysis","authors":"Kingstone Nyakurukwa,&nbsp;Yudhvir Seetharam","doi":"10.1002/isaf.70012","DOIUrl":"https://doi.org/10.1002/isaf.70012","url":null,"abstract":"<p>Increasing computing power and access to the internet have amplified the role of social media and online news media on financial market outcomes. However, these two sources of information are intertwined in such a way that information flows between them. As a result, sentiment expressed in one source can affect stock market outcomes through the other source. This study examines this interplay between news media sentiment, social media sentiment and stock returns within the Dow Jones constituent companies from 2016 to 2023. Leveraging an extensive dataset, we adopt an approach that combines causal mediation models with robust statistical techniques to establish the mediation effects of one sentiment proxy on the relationship between the other proxy and stock returns. We also use a range of other methods like path analysis, panel vector autoregression and causal forests for robustness. The study finds that news sentiment is more influential in directly affecting stock returns than <i>Twitter</i> sentiment while the latter is more influential indirectly when mediated by news sentiment.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647512","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":"Editorial: Analysis of Sentiment Estimates and Cognitive Fallacies in Large Language Models","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.70010","DOIUrl":"https://doi.org/10.1002/isaf.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper describes some experimentation with the evolving ability of large language models to generate sentiment estimates. We find that current models seem to equal or even exceed the ability of human annotators in a case study of single sentiment sentences. In addition, using the large language models, we were able to identify a small number of sentences in the data set, where it appears that the annotator made errors in assessing the sentiment. Unfortunately, analysis of the LLM results also illustrates apparent cognitive biases in the LLM behavior. Those effects appear to include an “ostrich effect” and a “no one is good enough” effect cognitive bias in LLM sentiment estimates.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624682","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":"Comparing the Prediction Performance of Random Forest, Lasso, and Logit in the Context of IPO Withdrawal","authors":"Annika Reiff","doi":"10.1002/isaf.70009","DOIUrl":"https://doi.org/10.1002/isaf.70009","url":null,"abstract":"<p>This paper examines the prediction of IPO withdrawal using machine learning methods (lasso and random forest) and conventional regression (logit). The dataset comprises 2444 US first-time IPOs from 1997 to 2014. Results show that random forest outperforms both logit and lasso in in-sample and cross-sectional out-of-sample predictions when the training and test sets are drawn from the same time period. However, when models are trained on past data and tested on future observations, all models fail to accurately predict IPO withdrawal. This failure is attributed to concept drift—a change in the relationship between predictors and IPO withdrawal over time. I show that concept drift occurs at multiple points in time, affects various predictors, and persists even when accounting for economic shocks, institutional changes, or different prediction horizons. These findings suggest that the generalizability of previous results on IPO withdrawal is limited, as the relationship between various predictors and IPO withdrawal seems to vary across time periods.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573470","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":"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}