Intell. Syst. Account. Finance Manag.最新文献

{"title":"ISAFM Paper of the Year for 2011","authors":"D. O’Leary","doi":"10.1002/isaf.1468","DOIUrl":"https://doi.org/10.1002/isaf.1468","url":null,"abstract":"","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121394676","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 to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions","authors":"M. Esen, Emrah Bilgiç, Ulkem Basdas","doi":"10.1002/ISAF.1446","DOIUrl":"https://doi.org/10.1002/ISAF.1446","url":null,"abstract":"Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116185426","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":"Erratum and addendum - GOOGLE'S duplex: Pretending to be human","authors":"D. O’Leary","doi":"10.1002/ISAF.1454","DOIUrl":"https://doi.org/10.1002/ISAF.1454","url":null,"abstract":"","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114861506","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":"ISAFM paper of the year for 2018","authors":"D. O’Leary","doi":"10.1002/ISAF.1451","DOIUrl":"https://doi.org/10.1002/ISAF.1451","url":null,"abstract":"","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233785","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 SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece","authors":"V. Giannopoulos, E. Aggelopoulos","doi":"10.1002/ISAF.1456","DOIUrl":"https://doi.org/10.1002/ISAF.1456","url":null,"abstract":"","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960749","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":"Strategy on a Page: An ArchiMate-based tool for visualizing and designing strategy","authors":"Adina Aldea, M. Iacob, J. Hillegersberg, D. Quartel, H. Franken","doi":"10.1002/isaf.1423","DOIUrl":"https://doi.org/10.1002/isaf.1423","url":null,"abstract":"Nowadays, organizations need to be able to adjust more rapidly to the circumstances of their environment, at a strategic, tactical, and operational level. However, most software tools are designed to support the tactical and operational levels, while at a strategic level there are not many options available. In this paper, we propose a software tool which supports modelling of strategic information, covering several well†known strategy techniques, and also facilitates the design of highly customizable management dashboards. To validate our proposed software tools, we perform two case studies, with two inherently different organizations, namely a public university and an investment fund.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128770817","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":"Defining personalized concepts for XBRL using iPAD-drawn fuzzy sets","authors":"M. Reformat, R. Yager, Nhuan D. To","doi":"10.1002/isaf.1426","DOIUrl":"https://doi.org/10.1002/isaf.1426","url":null,"abstract":"An efficient and effective analysis of business data requires a better understanding of what the data represents, and to what degree. A human†like way of accomplishing that without being too detailed yet learning more about data content is to summarize and map the data into concepts familiar to a person performing analysis. Processes of summarization help identify the most essential facts that are embedded in the data. All this is of significant importance for analysis of large amounts of business data required to make good and sound financial decisions. There are two aspects enabling more comprehensive yet easier processing of data: a standardized representation format of financial data; and a human†friendly way of defining concepts and using them for building personalized models representing processing data. The first of the aspects has been addressed by the eXtensible Business Reporting Language (XBRL)—a standardized format of defining, representing and exchanging corporate and financial information. The second aspect is related to providing individuals with the ability to gain understanding of data content via determining a degree of truth of statements summarizing data based on their own perception of concepts they are looking for. In this paper, we introduce a tablet application—Tablet†based input of Fuzzy Sets (TiFS)—and demonstrate its usefulness for entering personalized definitions of concepts and terms that enable a quick analysis of financial data. Such analysis means utilization of soft queries and operations of aggregation that extract and summarize the data and present it in a form familiar to analysts. The application allows for defining concepts and terms with ‘finger†made’ drawings representing a person's perception of concepts. Further, these definitions are used to build summarization statements for exploring XBRL data. They are equipped with ‘drawn’ definitions of linguistic terms (e.g. LARGE, SMALL, FAST) and linguistic quantifiers (e.g. ALL, MOSTLY), and enable summarization of data content from the perspective of a user's interests. The ‘drawn’ linguistic terms and quantifiers represent membership functions of fuzzy sets. Utilization of fuzzy sets allows for performing operations of data summarization in a human†like way. The application of TiFS illustrates ease of inputting personalized definitions of concepts and their influence on the interpretation of data. This introduces aspects of personalization and adaptation of artificial intelligence systems to perceptions and views of individuals. The proposed application is used to perform a basic analysis of an XBRL document.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410716","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":"Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges","authors":"A. Preece","doi":"10.1002/ISAF.1422","DOIUrl":"https://doi.org/10.1002/ISAF.1422","url":null,"abstract":"Recent rapid progress in machine learning (ML), particularly so†called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML†based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647211","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":"Deep networks for predicting direction of change in foreign exchange rates","authors":"Svitlana Galeshchuk, Sumitra Mukherjee","doi":"10.1002/isaf.1404","DOIUrl":"https://doi.org/10.1002/isaf.1404","url":null,"abstract":"Summary \u0000Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out-of-sample prediction accuracy.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512896","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":"City formation with complex landscapes","authors":"J. Fain","doi":"10.1002/isaf.1418","DOIUrl":"https://doi.org/10.1002/isaf.1418","url":null,"abstract":"Using simulation methods I explore some of the properties of the new economic geography model using a complex landscape. I introduce landscape complexity by allowing the existence of limited pathways that can be traversed at a lower cost than most other paths. I also introduce a river that may be crossed at limited points and may be used to transport goods. I find that adding complexity substantially alters how many cities form and where they form. Compared with a simple landscape, complex landscapes produce a distribution of city sizes that more closely resemble the actual distribution of city sizes.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115342216","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}