{"title":"GOOGLE'S Duplex: Pretending to be human*","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.1443","DOIUrl":"10.1002/isaf.1443","url":null,"abstract":"<div>\u0000 \u0000 <p>Google's Duplex is a computer-based system with natural language capabilities that provides a human sounding conversation as it performs a set of tasks, such as making restaurant reservations. This paper analyses Google's Duplex and some of the initial reaction to the system and its capabilities. The paper does a text analysis and finds that the system-generated text creates standardized ratings that suggest the text is analytical, authentic and possesses a generally positive tone. As would be expected for the applications for which it is being used, the text is heavily focused on the present. In addition, this analysis indicates that the text provides evidence of social processes, cognitive processes, tentativeness and affiliation. Further, this paper examines some of the characteristics of speech that Duplex uses to sound human. Those capabilities appear to allow the system pass the Turing test for some well-structured tasks. However, this paper investigates some of the ethics of pretending to be human and suggests that such impersonation is against evolving computer codes of ethics.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 1","pages":"46-53"},"PeriodicalIF":0.0,"publicationDate":"2019-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116182","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":"Profitability of alternative methods of combining the signals from technical trading systems","authors":"Jasdeep S. Banga, B. Wade Brorsen","doi":"10.1002/isaf.1442","DOIUrl":"10.1002/isaf.1442","url":null,"abstract":"<div>\u0000 \u0000 <p>Past efforts determining the profitability of technical analysis reached varied conclusions. We test the profitability of a composite prediction that uses buy and sell signals from technical indicators as inputs. Both machine learning methods, like neural networks, and statistical methods, like logistic regression, are used to get predictions. Inputs are signals from trend-following and mean-reversal technical indicators in addition to the variance of prices. Four representative commodities from agricultural, livestock, financial, and foreign exchange futures markets are selected to determine profitability. Special care is taken to avoid data snooping error. Both neural networks and statistical methods did not show consistent profitability.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 1","pages":"32-45"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127281348","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":"Assessing qualitative similarities between financial reporting frameworks using visualization and rules: COREP vs. pillar 3","authors":"Wenmei Yang, Adriano S. Koshiyama","doi":"10.1002/isaf.1441","DOIUrl":"10.1002/isaf.1441","url":null,"abstract":"<div>\u0000 \u0000 <p>Financial institutions are struggling with larger volume, more specific and greater frequency of regulatory reporting after the global financial crisis in 2008, especially those that need to report to multiple jurisdictions. To help to improve reporting efficiency, this paper aims to assess the existence of similarities between templates related to credit and counter party credit risk of COREP and Pillar 3 regulatory reporting frameworks by applying Correspondence Analysis and Association Rules Mining. Our results suggest a high degree of overlap between these reporting frameworks, more prominently the three business functions as Front office, Finance and Risk. These patterns can be used as guidance for financial institutions to reshape their reporting architecture.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 1","pages":"16-31"},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124010142","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}
Alejandro Parot, Kevin Michell, Werner D. Kristjanpoller
{"title":"Using Artificial Neural Networks to forecast Exchange Rate, including VAR-VECM residual analysis and prediction linear combination","authors":"Alejandro Parot, Kevin Michell, Werner D. Kristjanpoller","doi":"10.1002/isaf.1440","DOIUrl":"10.1002/isaf.1440","url":null,"abstract":"<div>\u0000 \u0000 <p>The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision-makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short-term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post-processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long-term non-linear components of a time series, which VECM and VAR models are unable to do. Post-processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post-processing increases forecasting accuracy.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 1","pages":"3-15"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114650306","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":"DNA Mining and genealogical information systems: Not just for finding family ethnicity","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.1439","DOIUrl":"10.1002/isaf.1439","url":null,"abstract":"<div>\u0000 \u0000 <p>The primary expected use of DNA and genealogy sites has been their ability to help users find their family, find their ethnicity and to help them connect with distant relatives. In so doing such sites help users to “learn more about themselves.” Such systems have also been proposed to have the broader goals of helping connect mankind and show people how their similarities are greater than their differences. However, the use of DNA and genealogy information recently turned away from just finding family connections, ethnicity and origins. Recently it was announced that the “Golden State Killer” had been caught using information generated from using DNA and consumer genealogical websites.</p>\u0000 <p>This paper investigates some of the questions and unanticipated consequences raised by this alternative use of these technologies and their impact on individuals, organizations and society. As part of that analysis we analyze some of the immediate consequences on the firm from which the DNA information was gathered, the new emerging approach used by law enforcement, some privacy concerns and provide a network game formulation as a means to model user behavior. Finally, we examine some potential emerging research issues.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 4","pages":"190-196"},"PeriodicalIF":0.0,"publicationDate":"2018-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1439","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134225584","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 Information Enterprise Transactions: Business Intelligence and Wash and Spoof Transactions in Blockchain and Social Commerce","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.1438","DOIUrl":"10.1002/isaf.1438","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates what are referred to as ‘open information transactions’. Such transactions are in contrast to traditional transactions, where typically two parties to a transaction are the only ones with information about the transaction. For example, in a sale, the seller and the purchaser typically are the only ones with information about the transaction. However, some emerging technologies, such as blockchain accounting, supply chain social media, and hashtag commerce are making information about the transactions potentially openly available to others. This paper investigates some of the implications and strategies that include the use of that open information. For example, open information in accounting and supply chain transactions provides the potential for both business intelligence analysis of the information and possibly misleading and illusory transactions, analogous to those that have garnered the recent attention of the Justice Department in cryptocurrencies. Finally, this paper suggests that blockchain transaction processing will provide reliable information in those settings where there is a “single truth” feed of information flow for the phenomena of interest, no ability to do off-blockchain transactions (or a large penalty cost) and limitation to a single identity for each enterprise on the blockchain.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 3","pages":"148-158"},"PeriodicalIF":0.0,"publicationDate":"2018-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127605571","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}
Feng Yi, Guan Feng, Hongtao Wang, Zhi Li, Limin Sun
{"title":"MIAC: A mobility intention auto-completion model for location prediction","authors":"Feng Yi, Guan Feng, Hongtao Wang, Zhi Li, Limin Sun","doi":"10.1002/isaf.1432","DOIUrl":"https://doi.org/10.1002/isaf.1432","url":null,"abstract":"<div>\u0000 \u0000 <p>Location prediction is essential to many commercial applications and enables appealing experience for business and governments. Many research work show that human mobility is highly predictable. However, existing work on location prediction reported limited improvements in using generalized spatio-temporal features and unsatisfactory prediction accuracy for complex human mobility. To address these challenges, in this paper we propose a <i>Mobility Intention and Auto-Completion</i> (MIAC) model. We extract those mobility patterns that generalize common spatio-temporal features of all users, and use the mobility intentions as the hidden states from mobility dataset. A new predicting algorithm based on auto-completion is then proposed. The experimental results on real-world datasets demonstrate that the proposed MIAC model can properly capture the regularity of a user's mobility by simultaneously considering the spatial and temporal features. The comparison results also indicate that MIAC model significantly outperforms state-of-the-art location prediction methods, and also can predicts long range locations.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 4","pages":"161-173"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137675346","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":"Predicting credit card delinquencies: An application of deep neural networks","authors":"Ting Sun, Miklos A. Vasarhelyi","doi":"10.1002/isaf.1437","DOIUrl":"10.1002/isaf.1437","url":null,"abstract":"<div>\u0000 \u0000 <p>The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest <i>F</i> scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 4","pages":"174-189"},"PeriodicalIF":0.0,"publicationDate":"2018-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126435026","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":"How information and communication technology affects decision-making on innovation diffusion: An agent-based modelling approach","authors":"Carlos M. Fernández-Márquez, Francisco J. Vázquez","doi":"10.1002/isaf.1430","DOIUrl":"10.1002/isaf.1430","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a computational agent-based model of innovation diffusion that allows us to analyse the influence of information and communication technology (ICT) development on decision-making. Model dynamics are based on local emulation between pairs of individuals that generate an evolving social network on which an innovation is virally spread (by word of mouth). Results suggest that ICT development affects the data usefulness for decision-making by changing the topology of the social network (the means whereby the innovation is propagated). Paradoxically, a higher level of ICT development (providing a larger volume of data) narrows the differences between better and worse launch strategies, thus reducing data-driven decision-making usefulness, which then shows diminishing returns on the ICT level.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 3","pages":"124-133"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122021923","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":"Hybrid performance evaluation of sustainable service and manufacturing supply chain management: An integrated approach of fuzzy dematel and fuzzy inference system","authors":"Ehsan Pourjavad, Arash Shahin","doi":"10.1002/isaf.1431","DOIUrl":"10.1002/isaf.1431","url":null,"abstract":"<div>\u0000 \u0000 <p>The aim of this paper is to propose a comprehensive framework for simultaneously measuring the performance of sustainable service and manufacturing supply chain management. Application of the proposed approach also results in reduced uncertainty of the performance measurement process caused by qualitative criteria evaluation. The proposed approach consists of two main steps. First, the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method has been used to determine important criteria by avoiding low influences; and then a Mamdani fuzzy inference system model has been adopted and applied for performance evaluation of sustainable supply chain management (SSCM). This model is employed in order to cope with the vagueness that exists in the SSCM performance investigation due to the vagueness intrinsic in the evaluation of criteria. In the proposed model, human reasoning has been modelled with fuzzy inference rules and has been set in the system, which is an advantage compared with those models in which fuzzy set theory and multicriteria decision-making models are integrated. The proposed approach has been implemented in the pipe and fitting industry in order to highlight its application in real life. Sensitivity analysis has been carried out to determine the influence of service and manufacturing criteria on SSCM performance. The findings reveal that sustainable manufacturing criteria compared with sustainable service criteria have more effect on the performance of SSCM.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"25 3","pages":"134-147"},"PeriodicalIF":0.0,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1431","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127376542","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}