Intell. Syst. Account. Finance Manag.最新文献_第3页

ISAFM Paper of the Year for 2016 ISAFM 2016年度论文

Intell. Syst. Account. Finance Manag. Pub Date : 2017-10-01 DOI: 10.1002/isaf.1416

D. O’Leary

引用次数: 0

MIAC: A Mobility Intention Auto-Completion Model for Location Prediction MIAC:一种用于位置预测的移动意图自动完成模型

Intell. Syst. Account. Finance Manag. Pub Date : 2017-08-19 DOI: 10.1007/978-3-319-63558-3_37

Feng Yi, Zhi Li, Hongtao Wang, Weimin Zheng, Limin Sun

引用次数: 3

An empirical investigation of analytical procedures using mixture distributions 使用混合分布的分析程序的实证研究

Intell. Syst. Account. Finance Manag. Pub Date : 2017-05-25 DOI: 10.1002/isaf.1405

J. Westland

{"title":"An empirical investigation of analytical procedures using mixture distributions","authors":"J. Westland","doi":"10.1002/isaf.1405","DOIUrl":"https://doi.org/10.1002/isaf.1405","url":null,"abstract":"Analytical procedures are evaluations of account and transaction flow information made by a study of plausible relationships between both accounting and nonâ€ accounting data. This study investigates the performance of Tweedie distributions (which have Gaussian distributions as members) in improving fit of zeroâ€ inflated, nonâ€ negative, kurtotic and multimodal analytical review data. The study found that account valuations are more informative than marginal data in analytical review, that mixture Poissonâ€“Gamma distributions offer better fit than Gaussian distributions, even under assumptions of central limit theorem convergence, and that mixture Poissonâ€“Gamma distributions provide better predictions of future account and transaction volumes and values. Model performance improvement with price versus returns data in this empirical study was substantial: from less than oneâ€ quarter of variance, to almost twoâ€ thirds. Tweedie generalized linear model risk assessments were found to be a magnitude smaller than traditional risk assessments, lending support to market inefficiency and increased risk from idiosyncratic factors. An example with several differing distributions shows that use of mixture distributions instead of point estimation can reduce sample size while retaining the power of the audit tests. The results of this study are increasingly important as accounting datasets are growing exponentially larger over time, requiring wellâ€ defined roles for models, algorithms, data and narrative which can only be achieved with statistical protocols and algorithmic languages.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126846632","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

Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research 自然语言处理在会计，审计和财务:文献的综合与未来的研究路线图

Intell. Syst. Account. Finance Manag. Pub Date : 2016-07-01 DOI: 10.1002/isaf.1386

Ingrid E. Fisher, M. Garnsey, Mark E. Hughes

{"title":"Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research","authors":"Ingrid E. Fisher, M. Garnsey, Mark E. Hughes","doi":"10.1002/isaf.1386","DOIUrl":"https://doi.org/10.1002/isaf.1386","url":null,"abstract":"Natural language processing NLP is a part of the artificial intelligence domain focused on communication between humans and computers. NLP attempts to address the inherent problem that while human communications are often ambiguous and imprecise, computers require unambiguous and precise messages to enable understanding. The accounting, auditing and finance domains frequently put forth textual documents intended to communicate a wide variety of messages, including, but not limited to, corporate financial performance, management's assessment of current and future firm performance, analysts' assessments of firm performance, domain standards and regulations as well as evidence of compliance with relevant standards and regulations. NLP applications have been used to mine these documents to obtain insights, make inferences and to create additional methodologies and artefacts to advance knowledge in accounting, auditing and finance. This paper synthesizes the extant literature in NLP in accounting, auditing and finance to establish the state of current knowledge and to identify paths for future research. Copyright © 2016 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196178","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: 132

A Psychological Approach to Microfinance Credit Scoring via a Classification and Regression Tree 基于分类与回归树的小额信贷信用评分的心理学方法

Intell. Syst. Account. Finance Manag. Pub Date : 2014-10-01 DOI: 10.1002/isaf.1355

Ibtissem Baklouti

{"title":"A Psychological Approach to Microfinance Credit Scoring via a Classification and Regression Tree","authors":"Ibtissem Baklouti","doi":"10.1002/isaf.1355","DOIUrl":"https://doi.org/10.1002/isaf.1355","url":null,"abstract":"Microfinance institutions' MFIs' peculiar lending methodology is characterized by an unchallenged decision-making predominance from the part of loan officers. Indeed, the latter are in charge of providing a great deal of diagnostic information regarding the entrepreneur's psychological traits likely to help them run a business. This paper constitutes an initial attempt towards exploring the role of borrowers' psychological traits in predicting future default occurrences. It builds on a nonparametric credit scoring model, based on a decision tree, including borrowers' quantitative behavioural traits as input for the final scoring model. On applying data collected from a Tunisian microfinance bank, the major depicted result lies in the fact that borrowers' psychological traits constitute a major information source in predicting their creditworthiness. Actually, the variables deployed have helped reduce the proportion of bad loans classified as good loans by 3.125%, which leads to a decrease in MFIs' losses by 4.8%. In addition, the results indicate that the scoring model based on a classification and regression tree CART outperforms the classic techniques. Actually, implementing this CART model might well help MFIs reduce misclassification costs by 6.8% and 13.5% in comparison with the discriminant analysis and logistic regression models respectively. Our conceived model, we consider, would be of great practical implication for microfinance and may provide a means for securing competitive advantage over other MFIs that fail to implement such a methodology. Copyright © 2014 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391421","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: 15

Predicting Next Trading Day Closing Price of Qatar Exchange Index using Technical indicators and Artificial Neural Networks 利用技术指标和人工神经网络预测卡塔尔外汇指数下一个交易日收盘价

Intell. Syst. Account. Finance Manag. Pub Date : 2014-10-01 DOI: 10.1002/isaf.1358

A. Fadlalla, Farzaneh Amani

{"title":"Predicting Next Trading Day Closing Price of Qatar Exchange Index using Technical indicators and Artificial Neural Networks","authors":"A. Fadlalla, Farzaneh Amani","doi":"10.1002/isaf.1358","DOIUrl":"https://doi.org/10.1002/isaf.1358","url":null,"abstract":"Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks ANNs have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange QE Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well-established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility. Copyright © 2014 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127626881","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: 26

A stochastic Setting to Bank Financial Performance for Refining Efficiency estimates 银行财务绩效的随机设定以改进效率估算

Intell. Syst. Account. Finance Manag. Pub Date : 2014-10-01 DOI: 10.1002/isaf.1357

W. Wong, Qiang Deng, M. Tseng, L. Lee, C. Hooy

引用次数: 4

Integration of Random Sample Selection, Support Vector Machines and Ensembles for Financial Risk Forecasting with an Empirical Analysis on the Necessity of Feature Selection 基于随机样本选择、支持向量机和集成的金融风险预测——特征选择必要性的实证分析

Intell. Syst. Account. Finance Manag. Pub Date : 2012-10-01 DOI: 10.1002/isaf.1331

Jie Sun

{"title":"Integration of Random Sample Selection, Support Vector Machines and Ensembles for Financial Risk Forecasting with an Empirical Analysis on the Necessity of Feature Selection","authors":"Jie Sun","doi":"10.1002/isaf.1331","DOIUrl":"https://doi.org/10.1002/isaf.1331","url":null,"abstract":"Financial risk forecasting (FRF) is an effective tool to help people forecast whether or not a company will fail in future. Among all techniques of FRF, the support vector machine (SVM) is the most newly developed, and one of the most accurate and effective techniques. This study is devoted to investigating an ensemble model of FRF by integrating bagging with an SVM to generate a data-driven SVM ensemble. Bagging is used to produce diverse training datasets on which multiple SVM classifiers are trained to make FRF for a target company. Simple voting is employed to produce a final decision from the SVM model committee. The empirical study has two objectives. One is to verify whether the data-driven SVM ensemble can produce a more dominating performance than the most frequently used techniques in the area of FRF, i.e. multivariate discriminant analysis, logistics regression and a single SVM. The other is to verify whether feature selection is necessary to help the SVM make more precise FRF, although the SVM can handle high-dimensional data. The results indicate that the data-driven SVM ensemble significantly improves the predictive ability of SVM-based FRF. Meanwhile, feature selection can effectively help the SVM achieve better predictive performance, which means that use of feature selection is necessary in SVM-based FRF. Copyright © 2012 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127234715","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

Psychological Contracts and Job Satisfaction: Clustering Analysis using Evidential C-Means and Comparison with Other Techniques 心理契约与工作满意度:基于证据c均值的聚类分析及其与其他技术的比较

Intell. Syst. Account. Finance Manag. Pub Date : 2012-10-01 DOI: 10.1002/isaf.1334

M. Beynon, M. Heffernan, A. McDermott

{"title":"Psychological Contracts and Job Satisfaction: Clustering Analysis using Evidential C-Means and Comparison with Other Techniques","authors":"M. Beynon, M. Heffernan, A. McDermott","doi":"10.1002/isaf.1334","DOIUrl":"https://doi.org/10.1002/isaf.1334","url":null,"abstract":"The psychological contract refers to an individual employee's belief in mutual obligations between them and their employer. Psychological contracts are a key management concern, as they can impact employees' attitudes and behaviors in ways that influence organizational efficiency and effectiveness. In this paper, we analyse the relationship between the psychological contract and facets of job satisfaction among non-profit sector employees, using the nascent non-hierarchical evidential c-means (ECM) clustering technique. To date, this technique has been theoretically discussed but not widely applied. Based on the Dempster–Shafer theory of evidence, ECM is novel in facilitating the assignment of objects, not only to single clusters, but to sets of clusters, and no clusters (outliers). \u0000 \u0000The paper compares the theoretical underpinnings and findings from ECM with those of three other well-known clustering techniques, namely (1) the hierarchical Ward's method, (2) the non-hierarchical crisp k-means and (3) the non-hierarchical fuzzy c-means approaches. We present and interpret the cluster solutions from each clustering technique. We establish three clusters differentiated by the content of the employees' psychological contracts. These clusters are validated by considering their relationship with facets of job satisfaction, to ensure the clusters are theoretically meaningful. Comparisons of the findings from each technique: (1) provide insights into the relationship between the psychological contract and job satisfaction; (2) reveal what ECM encompasses, relative to other clustering techniques; (3) inform the selection of an appropriate clustering technique for a specific research problem; and (4) demonstrate potential future directions in the development of cluster analysis. Copyright © 2012 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"642 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117097368","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

Pareto-archived evolutionary wavelet network for financial constrained portfolio optimization pareto存档演化小波网络在财务约束投资组合优化中的应用

Intell. Syst. Account. Finance Manag. Pub Date : 2010-04-01 DOI: 10.1002/isaf.313

N. C. Suganya, G. Pai

{"title":"Pareto-archived evolutionary wavelet network for financial constrained portfolio optimization","authors":"N. C. Suganya, G. Pai","doi":"10.1002/isaf.313","DOIUrl":"https://doi.org/10.1002/isaf.313","url":null,"abstract":"The multi-objective portfolio optimization problem is too complex to find direct solutions by traditional methods when constraints reflecting investor's preferences and-or market frictions are included in the mathematical model and hence heuristic approaches are sought for their solution. \u0000 \u0000In this paper we propose the solution of a multi-criterion (bi-objective) portfolio optimization problem of minimizing risk and maximizing expected return of the portfolio which includes basic, bounding, cardinality, class and short sales constraints using a Pareto-archived evolutionary wavelet network (PEWN) solution strategy. Initially, the empirical covariance matrix is denoised by employing a wavelet shrinkage denoising technique. Second, the cardinality constraint is eliminated by the application of k-means cluster analysis. Finally, a PEWN heuristic strategy with weight standardization procedures is employed to obtain Pareto-optimal solutions satisfying all the constraints. The closeness and diversity of Pareto-optimal solutions obtained using PEWN is evaluated using different measures and the results are compared with existing only solution strategies (evolution-based wavelet Hopfield neural network and evolution-based Hopfield neural network) to prove its dominance. Eventually, data envelopment analysis is also used to test the efficiency of the non-dominated solutions obtained using PEWN. \u0000 \u0000Experimental results are demonstrated on the Bombay Stock Exchange, India (BSE200 index: period July 2001–July 2006), and the Tokyo Stock Exchange, Japan (Nikkei225 index: period March 2002–March 2007), data sets. Copyright © 2010 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125785453","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: 7