Intelligent Systems in Accounting, Finance and Management最新文献_第4页

Massive data language models and conversational artificial intelligence: Emerging issues 海量数据语言模型和会话式人工智能:新兴问题

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-09-16 DOI: 10.1002/isaf.1522

Daniel E. O’Leary

引用次数: 9

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium-sized enterprises 分类、特征选择和重采样方法对中小企业破产预测的影响

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-09-15 DOI: 10.1002/isaf.1521

Lenka Papíková, Mário Papík

{"title":"Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium-sized enterprises","authors":"Lenka Papíková, Mário Papík","doi":"10.1002/isaf.1521","DOIUrl":"10.1002/isaf.1521","url":null,"abstract":"<div>\u0000 \u0000 <p>Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results. Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 4","pages":"254-281"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126923972","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: 2

Application and performance of data mining techniques in stock market: A review 数据挖掘技术在股票市场中的应用与性能综述

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-08-31 DOI: 10.1002/isaf.1518

Jasleen Kaur, Khushdeep Dharni

引用次数: 4

Multilayer-neighbor local binary pattern for facial expression recognition 基于多层邻域局部二值模式的面部表情识别

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-08-10 DOI: 10.1002/isaf.1520

Wei-Yen Hsu, Hsien-Jen Hsu, Yen-Yao Wang, Tawei Wang

{"title":"Multilayer-neighbor local binary pattern for facial expression recognition","authors":"Wei-Yen Hsu, Hsien-Jen Hsu, Yen-Yao Wang, Tawei Wang","doi":"10.1002/isaf.1520","DOIUrl":"10.1002/isaf.1520","url":null,"abstract":"<div>\u0000 \u0000 <p>Facial expression recognition (FER) has drawn the interest of practitioners and researchers due to its potential in opening new business opportunities. One critical aspect of any successful FER system is a feature extraction method that can efficiently find sufficient facial features and characterize facial expressions. This paper proposes an appearance-based feature extraction method by introducing a local feature descriptor, a multilayer-neighbor local binary pattern (LBP), for recognizing facial expressions. This new LBP operator is an extension of the original one-layer-neighbor LBP to two-layer-neighbor and three-layer-neighbor LBPs. We extract features by comparing new center points with neighborhood points. In addition, based on facial landmark locations, we extract active facial blocks during emotional stimulations. These prominent facial blocks utilize facial symmetry to improve the accuracy and speed of expression recognition. After using principal component analysis to reduce the dimensionality of features, we use a support vector machine to assign expressions to seven categories. We evaluate the proposed method by comparing it with other commonly used methods, and the proposed method is more accurate. Implications for business researchers are discussed.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 3","pages":"156-168"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115427266","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: 0

Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat 通过神经网络预测咖啡、玉米、棉花、燕麦、大豆、大豆油、糖和小麦的商品价格

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-08-03 DOI: 10.1002/isaf.1519

Xiaojie Xu, Yun Zhang

引用次数: 25

Enhanced financial fraud detection using cost-sensitive cascade forest with missing value imputation 基于缺失价值估算的成本敏感级联森林增强财务欺诈检测

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-07-28 DOI: 10.1002/isaf.1517

Lukui Huang, Alan Abrahams, Peter Ractham

{"title":"Enhanced financial fraud detection using cost-sensitive cascade forest with missing value imputation","authors":"Lukui Huang, Alan Abrahams, Peter Ractham","doi":"10.1002/isaf.1517","DOIUrl":"10.1002/isaf.1517","url":null,"abstract":"<div>\u0000 \u0000 <p>Financial statement fraud is a global problem for investors, audit firms, regulators, and other stakeholders. Fraud detection can be regarded as a binary classification problem with a false negative being more expensive than a false positive. Although existing studies have made great efforts to detect fraud using various data-mining techniques, the difference in misclassification costs is seldom considered. In this study, we propose a cost-sensitive cascade forest (CSCF) for fraud detection, which places heavy penalty on false negative prediction and self-adjusts the depth of a cascade forest according to the classifier’s recall (i.e. the classifier’s sensitivity). As missing values are ubiquitous in fraud research, we also explore the effect of selected missing data treatments on prediction performance, including complete case analysis, three selected classic statistical mechanisms (zero, mean, and modified mean imputation), and two machine learning (K-nearest neighbor [KNN] and random forest [RF]) approaches. The experimental results show that the proposed CSCF significantly improves the fraud prediction in comparison with one of the latest fraud detection models using the RUSBoost algorithm. Comparing different missing value treatments, even though RUSBoost and CSCF perform well when using complete case analysis, we find that the best performance is achieved when CSCF is used with missing data imputed as zero. Such treatment further improves the performance, and results in an area under curve (AUC) score of 0.82 compared to the highest AUC (0.71) from the baseline model. Supplementary analysis further reveals that the low AUC of complete case analysis for the two examined models persists under different training sizes. Thus, our findings shed light on the potential benefits of missing value imputation for the model’s performance for fraud detection.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 3","pages":"133-155"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122885228","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: 0

Towards an early warning system for sovereign defaults leveraging on machine learning methodologies 利用机器学习方法建立主权违约预警系统

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-06-12 DOI: 10.1002/isaf.1516

Anastasios Petropoulos, Vasilis Siakoulis, Evangelos Stavroulakis

引用次数: 2

Measuring relative volatility in high-frequency data under the directional change approach 在方向变化方法下测量高频数据的相对波动性

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-06-02 DOI: 10.1002/isaf.1510

Shengnan Li, Edward P. K. Tsang, John O'Hara

引用次数: 1

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach 预测商品市场收益波动:一种基于GARCH-LSTM的混合集成学习方法

Intelligent Systems in Accounting, Finance and Management Pub Date : 2022-05-31 DOI: 10.1002/isaf.1515

Kshitij Kakade, Aswini Kumar Mishra, Kshitish Ghate, Shivang Gupta

{"title":"Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach","authors":"Kshitij Kakade, Aswini Kumar Mishra, Kshitish Ghate, Shivang Gupta","doi":"10.1002/isaf.1515","DOIUrl":"https://doi.org/10.1002/isaf.1515","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 2","pages":"103-117"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137749873","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: 0

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