Intelligent Systems in Accounting, Finance and Management最新文献_第5页

Anti-money laundering and financial fraud detection: A systematic literature review 反洗钱与金融欺诈侦查:系统的文献综述

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-05-19 DOI: 10.1002/isaf.1509

Lucas Schmidt Goecks, André Luis Korzenowski, Platão Gonçalves Terra Neto, Davenilcio Luiz de Souza, Taciana Mareth

引用次数: 2

Wikipedia pageviews as investors’ attention indicator for Nasdaq 维基百科页面浏览量是投资者关注纳斯达克的指标

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-04-17 DOI: 10.1002/isaf.1508

Raúl Gómez-Martínez, Carmen Orden-Cruz, Juan Gabriel Martínez-Navalón

{"title":"Wikipedia pageviews as investors’ attention indicator for Nasdaq","authors":"Raúl Gómez-Martínez, Carmen Orden-Cruz, Juan Gabriel Martínez-Navalón","doi":"10.1002/isaf.1508","DOIUrl":"https://doi.org/10.1002/isaf.1508","url":null,"abstract":"<p>The attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 1","pages":"41-49"},"PeriodicalIF":0.0,"publicationDate":"2022-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109170445","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: 1

Time-varying neural network for stock return prediction 时变神经网络用于股票收益预测

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-03-27 DOI: 10.1002/isaf.1507

Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi, Richard Y. D. Xu

引用次数: 4

A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act 对美国证券交易委员会与《萨班斯-奥克斯利法案》有关的会计和审计执法发布的文本分析

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-03-23 DOI: 10.1002/isaf.1506

Sergio Davalos, Ehsan H. Feroz

{"title":"A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act","authors":"Sergio Davalos, Ehsan H. Feroz","doi":"10.1002/isaf.1506","DOIUrl":"https://doi.org/10.1002/isaf.1506","url":null,"abstract":"<div>\u0000 \u0000 <p>We focus on textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases (AAERs). Our research question is: Did the Sarbanes–Oxley Act (SOX) 2002 affect the qualitative linguistic content of the AAERs in the post-SOX period? To answer this question, we test the null hypotheses that there will be no differences in the qualitative verbiage and sentiment of AAERs in the time periods that we study related to the enactment of SOX: pre-SOX and post-SOX. To resolve the research question, we applied several text mining methods and classification machine-learning methods. We first used two basic text-mining methods, generating a bag of words and topic modeling, for descriptive analysis of the AAER content before the enactment of SOX and after the enforcement of SOX. We then conducted sentiment analysis using four sentiment dictionaries on the content of the two time periods: before SOX and after SOX. Finally, we developed three different classification models based on well-known supervised learning algorithms and determined that SOX-related AAERs could be distinguished from non-SOX-related AAERs. Based on the results, we conclude that there were significant linguistic differences in the AAER content of the post-SOX period compared with the pre-SOX period. In other words, post-SOX-related AAERs are qualitatively different in terms of linguistic contents and sentiment values compared with the non-SOX-related AAERs.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 1","pages":"19-40"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109173501","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

Corporate governance performance ratings with machine learning 利用机器学习进行公司治理绩效评级

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-03-18 DOI: 10.1002/isaf.1505

Jan Svanberg, Tohid Ardeshiri, Isak Samsten, Peter Öhman, Presha E. Neidermeyer, Tarek Rana, Natalia Semenova, Mats Danielson

{"title":"Corporate governance performance ratings with machine learning","authors":"Jan Svanberg, Tohid Ardeshiri, Isak Samsten, Peter Öhman, Presha E. Neidermeyer, Tarek Rana, Natalia Semenova, Mats Danielson","doi":"10.1002/isaf.1505","DOIUrl":"https://doi.org/10.1002/isaf.1505","url":null,"abstract":"<p>We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 1","pages":"50-68"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109171216","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: 4

A Review of Big Data Research in Accounting 会计大数据研究述评

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-02-27 DOI: 10.1002/isaf.1504

Francis Aboagye-Otchere, Cletus Agyenim-Boateng, Abdulai Enusah, Theodora Ekua Aryee

{"title":"A Review of Big Data Research in Accounting","authors":"Francis Aboagye-Otchere, Cletus Agyenim-Boateng, Abdulai Enusah, Theodora Ekua Aryee","doi":"10.1002/isaf.1504","DOIUrl":"10.1002/isaf.1504","url":null,"abstract":"<div>\u0000 \u0000 <p>The impending fourth industrial revolution has enhanced the role of big data analytics in today’s business practice. Consequently, many now consider big data as the most strategic resource in business to the extent that organizations that fail to utilize it may become competitively disadvantaged. Following these developments, questions have been raised about the future of the accounting discipline, especially in terms of how it can continue to add value to organizations. While some scholars have attempted to address this question, it remains an abstract concept requiring further investigation. Therefore, this study conducts a systematic literature review to determine the status of accounting research on big data analytics and provides avenues for further studies. By conducting co-occurrence network analysis on 52 peer-reviewed articles published from 2010 to 2020, three broad themes emerged, entailing big data implications for accounting <i>practice</i>, <i>education</i>, and <i>research design</i>. A further examination of the themes revealed few empirical studies on the phenomenon, as conceptual research dominates the field. Although external audit implications of big data are widely discussed, other accounting domains (e.g., managerial accounting and taxation) are underexplored. Therefore, future studies may focus on the implications of big data on variables such as performance measurement, information governance, tax behavior, curriculum design, and pedagogy.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 4","pages":"268-283"},"PeriodicalIF":0.0,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117130211","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: 5

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective 人工智能采用的建模驱动因素和障碍:来自战略管理视角的见解

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-01-25 DOI: 10.1002/isaf.1503

Sudatta Kar, Arpan Kumar Kar, Manmohan Prasad Gupta

{"title":"Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective","authors":"Sudatta Kar, Arpan Kumar Kar, Manmohan Prasad Gupta","doi":"10.1002/isaf.1503","DOIUrl":"10.1002/isaf.1503","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two-step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises-multiplication appliqúe a un classment <i>or</i> cross-impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision-making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 4","pages":"217-238"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129545640","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: 16

Multi-party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross 匿名股票大宗交易的多方计算机制:绿松石plato uncross的安全实现

Intelligent Systems in Accounting, Finance and Management Pub Date : 2021-11-01 DOI: 10.1002/isaf.1502

John Cartlidge, Nigel P. Smart, Younes Talibi Alaoui

引用次数: 13

Explaining stock markets' performance during the COVID‐19 crisis: Could Google searches be a significant behavioral indicator? 解释COVID - 19危机期间股市的表现:谷歌搜索能成为一个重要的行为指标吗?

Intelligent Systems in Accounting, Finance and Management Pub Date : 2021-08-16 DOI: 10.1002/isaf.1499

Evangelos Vasileiou

{"title":"Explaining stock markets' performance during the COVID‐19 crisis: Could Google searches be a significant behavioral indicator?","authors":"Evangelos Vasileiou","doi":"10.1002/isaf.1499","DOIUrl":"https://doi.org/10.1002/isaf.1499","url":null,"abstract":"Summary The purpose of this study is to examine the impact of the pandemic on the performance of stock markets, focusing on the behavioral influence of the fear due to COVID‐19. Using a data set of 10 developed countries during the period December 31, 2019, to September 30, 2020, we examine the impact of COVID‐19 on the performance of the stock markets. We incorporate the impact of the COVID‐19 pandemic using the following variables: (a) the number of new COVID‐19 cases, which was widely used as the main explanatory variable for market performance in early financial studies, and (b) a Google Search index, which collects the number of Google searches related to COVID‐19 and incorporates the health risk and the fear of COVID‐19 (the higher the number of searches for Covid terms, the higher the index value, and the higher the fear index). We employ our input into an EGARCH(1,1,1) model, and the findings show that the Google Search index enables us to draw statistically significant information regarding the impact of the COVID‐19 fear on the performance of the stock markets. On the other hand, the variable of the number of new COVID‐19 cases does not have any statistically significant influence on the performance of the stock markets. Google searches could be a useful tool for supporters of behavioral finance, scholars, and practitioners.","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"299 1","pages":"173 - 181"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73582103","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: 8

Who gains and who loses on stock markets? Risk preferences and timing matter 股票市场上谁是赢家，谁是输家?风险偏好和时机很重要

Intelligent Systems in Accounting, Finance and Management Pub Date : 2021-06-03 DOI: 10.1002/isaf.1493

Iryna Veryzhenko

引用次数: 1