Intelligent Systems in Accounting, Finance and Management最新文献

{"title":"Corporate governance performance ratings with machine learning","authors":"Jan Svanberg,&nbsp;Tohid Ardeshiri,&nbsp;Isak Samsten,&nbsp;Peter Öhman,&nbsp;Presha E. Neidermeyer,&nbsp;Tarek Rana,&nbsp;Natalia Semenova,&nbsp;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}

{"title":"A Review of Big Data Research in Accounting","authors":"Francis Aboagye-Otchere,&nbsp;Cletus Agyenim-Boateng,&nbsp;Abdulai Enusah,&nbsp;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}

{"title":"Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective","authors":"Sudatta Kar,&nbsp;Arpan Kumar Kar,&nbsp;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}

{"title":"Multi-party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross","authors":"John Cartlidge,&nbsp;Nigel P. Smart,&nbsp;Younes Talibi Alaoui","doi":"10.1002/isaf.1502","DOIUrl":"10.1002/isaf.1502","url":null,"abstract":"<p>Dark pools are financial trading venues where orders are entered and matched in secret so that no order information is leaked. By preventing information leakage, dark pools offer the opportunity for large volume block traders to avoid the costly effects of market impact. However, dark pool operators have been known to abuse their privileged access to order information. To address this issue, we introduce a provably secure multi-party computation mechanism that prevents an operator from accessing and misusing order information. Specifically, we implement a secure emulation of Turquoise Plato Uncross, Europe's largest dark pool trading mechanism, and demonstrate that it can handle real world trading throughput, with guaranteed information integrity.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 4","pages":"239-267"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/86/d9/ISAF-28-239.PMC9615482.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40463694","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":"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}

{"title":"Who gains and who loses on stock markets? Risk preferences and timing matter","authors":"Iryna Veryzhenko","doi":"10.1002/isaf.1493","DOIUrl":"10.1002/isaf.1493","url":null,"abstract":"<p>This paper uses an agent-based multi-asset model to examine the effect of risk preferences and optimal rebalancing frequency on performance measures while tracking profit and risk-adjusted return. We focus on the evolution of portfolios managed by heterogeneous mean-variance optimizers with a quadratic utility function under different market conditions. We show that patient and risk-averse agents are able to outperform aggressive risk-takers in the long-run. Our findings also suggest that the trading frequency determined by the optimal tolerance for the deviation from portfolio targets should be derived from a tradeoff between rebalancing benefits and rebalancing costs. In a relatively calm market, the absolute range of 6% to 8% and the complete-way back rebalancing technique outperforms others. During particular turbulent periods, however, none of the existing rebalancing techniques improves tax-adjusted profits and risk-adjusted returns simultaneously.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"143-155"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130901187","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":"Conventional and neural network target-matching methods dynamics: The information technology mergers and acquisitions market in the USA","authors":"Ioannis Anagnostopoulos,&nbsp;Anas Rizeq","doi":"10.1002/isaf.1492","DOIUrl":"10.1002/isaf.1492","url":null,"abstract":"<p>In an era of a continuous quest for business growth and sustainability it has been shown that synergies and growth-driven mergers and acquisitions (M&amp;As) are an integral part of institutional strategy. In order to endure in the face of fierce competition M&amp;As have become a very important channel of obtaining technology, increasing competitiveness and market share (Carbone &amp; Stone, <span>2005</span>; Christensen et al., <span>2011</span>). During the post-2000 era, this is also a point where more than half of the said available growth and synergies in M&amp;As are strongly related to information technology (IT) and its disruptive synergistic potential, as the first decade of the 2000s has shown (Sarrazin &amp; West, <span>2011</span>). Such business growth materializes at the intersection of internalizing, integrating, and applying the latest data management technology with M&amp;As where there are vast opportunities to develop (a) new technologies, (b) new target screening and valuation methodologies, (c) new products, (d) new services, and (e) new business models (Hacklin et al., <span>2013</span>; Lee &amp; Lee, <span>2017</span>). However, while technology and its disruptive capabilities have received considerable attention from the business community in general, studies regarding the examination of technology convergence, integration dynamics, and success of M&amp;As from a market screening and valuation perspective are relatively scarce (Lee &amp; Cho, <span>2015</span>; Song et al., <span>2017</span>). Furthermore, little attention has been devoted to investigating the evolutionary path of technology-assisted, target screening methods and understanding their potential for effective target acquisition in the future (Aaldering et al., <span>2019</span>). We contribute to this by examining the application of neural network (NN) methodology in successful target screening in the US M&amp;As IT sector.</p><p>In addition, while there are recognized idiosyncratic motivations for pursuing M&amp;A-centered strategies for growth, there are also considerable system-wide issues that introduce waves of global M&amp;A deals. Examples include reactions to globalization dynamics, changes in competition, tax reforms (such as the recent US tax reform indicating tax benefits for investors), deregulation, economic reforms and liberalization, block or regional economic integration (i.e., the Gulf Cooperation Council and the EU). Hence, effective target-firm identification is an important research topic to business leaders and academics from both management and economic perspectives.</p><p>Technology firms in particular often exhibit unconventional growth patterns, and this also makes firm valuation problematic as it can drive their stocks being hugely misvalued (i.e., overvalued) therefore increasing M&amp;A activity (Rhodes-Kropf &amp; Viswanathan, <span>2004</span>). Betton et al. (<span>2008</span>) claimed that predicting targets with any degr","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"97-118"},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1492","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123533135","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":"Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework","authors":"Ajay Byanjankar,&nbsp;József Mezei,&nbsp;Markku Heikkilä","doi":"10.1002/isaf.1490","DOIUrl":"10.1002/isaf.1490","url":null,"abstract":"<p>In recent years, peer-to-peer (P2P) lending has been gaining popularity amongst borrowers and individual investors. This can mainly be attributed to the easy and quick access to loans and the higher possible returns. However, the risk involved in these investments is considerable, and for most investors, being nonprofessionals, this increases the complexity and the importance of investment decisions. In this study, we focus on generating optimal investment decisions to lenders for selecting loans. We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk. In the process, we use internal rate of return as the measure of return. As the starting point of the model, we use machine-learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data. Afterwards, we calculate the distance between loans using (i) default probabilities and, as a novel step, (ii) expected value. In the calculations, we utilize kernel functions to obtain similarity weights of loans as the input of the optimization models. Two optimization models are tested and compared on data from the popular P2P platform Lending Club. The results show that using the expected-value framework yields higher return.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"119-129"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050250","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":"Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach","authors":"Sauraj Verma","doi":"10.1002/isaf.1489","DOIUrl":"10.1002/isaf.1489","url":null,"abstract":"<p>Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"130-142"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122598401","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":"Journal entry anomaly detection model","authors":"Mario Zupan,&nbsp;Verica Budimir,&nbsp;Svjetlana Letinic","doi":"10.1002/isaf.1485","DOIUrl":"10.1002/isaf.1485","url":null,"abstract":"<div>\u0000 \u0000 <p>Although numerous scientific papers have been written on deep learning, very few have been written on the exploitation of such technology in the field of accounting or bookkeeping. Our scientific study is oriented exactly toward this specific field. As accountants, we know the problems faced in modern accounting. Although accountants may have a plethora of information regarding technology support, looking for errors or fraud is a demanding and time-consuming task that depends on manual skills and professional knowledge. Our efforts are oriented toward resolving the problem of error-detection automation that is currently possible through new technologies, and we are trying to develop a web application that will alleviate the problems of journal entry anomaly detection. Our developed application accepts data from one specific enterprise resource planning system while also representing a general software framework for other enterprise resource planning developers. Our web application is a prototype that uses two of the most popular deep-learning architectures; namely, a variational autoencoder and long short-term memory. The application was tested on two different journals: data set D, learned on accounting journals from 2007 to 2018 and then tested during the year 2019, and data set H, learned on journals from 2014 to 2016 and then tested during the year 2017. Both accounting journals were generated by micro entrepreneurs.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 4","pages":"197-209"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200393","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}