Intelligent Systems in Accounting, Finance and Management最新文献_第7页

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths 谷歌-维基百科-推特模型作为冠状病毒死亡人数的领先指标

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-09-28 DOI: 10.1002/isaf.1482

Daniel E. O'Leary, Veda C. Storey

{"title":"A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths","authors":"Daniel E. O'Leary, Veda C. Storey","doi":"10.1002/isaf.1482","DOIUrl":"10.1002/isaf.1482","url":null,"abstract":"<p>Forecasting the number of cases and the number of deaths in a pandemic provides critical information to governments and health officials, as seen in the management of the coronavirus outbreak. But things change. Thus, there is a constant search for real-time and leading indicator variables that can provide insights into disease propagation models. Researchers have found that information about social media and search engine use can provide insights into the diffusion of flu and other diseases. Consistent with this finding, we found that a model with the number of Google searches, Twitter tweets, and Wikipedia page views provides a leading indicator model of the number of people in the USA who will become infected and die from the coronavirus. Although we focus on the current coronavirus pandemic, other recent viruses have threatened pandemics (e.g. severe acute respiratory syndrome). Since future and existing diseases are likely to follow a similar search for information, our insights may prove fruitful in dealing with the coronavirus and other such diseases, particularly in the early phases of the disease.</p><p><b>Subject terms</b>: coronavirus, COVID-19, unintentional crowd, Google searches, Wikipedia page views, Twitter tweets, models of disease diffusion.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 3","pages":"151-158"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79124116","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}

引用次数: 27

The digital future of internal staffing: A vision for transformational electronic human resource management 内部人员配置的数字化未来:转型电子人力资源管理的愿景

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-07-18 DOI: 10.1002/isaf.1481

Philip Rogiers, Stijn Viaene, Jan Leysen

引用次数: 6

A neural-network-based decision-making model in the peer-to-peer lending market 基于神经网络的p2p借贷市场决策模型

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-07-14 DOI: 10.1002/isaf.1480

Golnoosh Babaei, Shahrooz Bamdad

{"title":"A neural-network-based decision-making model in the peer-to-peer lending market","authors":"Golnoosh Babaei, Shahrooz Bamdad","doi":"10.1002/isaf.1480","DOIUrl":"10.1002/isaf.1480","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes an investment recommendation model for peer-to-peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision-making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root-mean-square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision-making algorithm with the output of a traditional model. The experimental results on a real-world data set show that our model leads to a better investment concerning both risk and return.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 3","pages":"142-150"},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126040106","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: 12

Tick size and market quality: Simulations based on agent-based artificial stock markets 滴答大小和市场质量:基于基于代理的人工股票市场的模拟

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-21 DOI: 10.1002/isaf.1474

Xinhui Yang, Jie Zhang, Qing Ye

引用次数: 4

RegTech—the application of modern information technology in regulatory affairs: areas of interest in research and practice 监管技术-现代信息技术在监管事务中的应用:研究和实践的兴趣领域

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-18 DOI: 10.1002/isaf.1479

Michael Becker, Kevin Merz, Rüdiger Buchkremer

引用次数: 13

Predicting credit card fraud with Sarbanes-Oxley assessments and Fama-French risk factors 用萨班斯-奥克斯利评估和法玛-弗伦奇风险因素预测信用卡欺诈

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-12 DOI: 10.1002/isaf.1472

James Christopher Westland

{"title":"Predicting credit card fraud with Sarbanes-Oxley assessments and Fama-French risk factors","authors":"James Christopher Westland","doi":"10.1002/isaf.1472","DOIUrl":"10.1002/isaf.1472","url":null,"abstract":"<div>\u0000 \u0000 <p>This research developed and tested machine learning models to predict significant credit card fraud in corporate systems using Sarbanes-Oxley (SOX) reports, news reports of breaches and Fama-French risk factors (FF). Exploratory analysis found that SOX information predicted several types of security breaches, with the strongest performance in predicting credit card fraud. A systematic tuning of hyperparamters for a suite of machine learning models, starting with a random forest, an extremely-randomized forest, a random grid of gradient boosting machines (GBMs), a random grid of deep neural nets, a fixed grid of general linear models where assembled into two trained stacked ensemble models optimized for F1 performance; an ensemble that contained all the models, and an ensemble containing just the best performing model from each algorithm class. Tuned GBMs performed best under all conditions. Without FF, models yielded an AUC of 99.3% and closeness of the training and validation matrices confirm that the model is robust. The most important predictors were firm specific, as would be expected, since control weaknesses vary at the firm level. Audit firm fees were the most important non-firm-specific predictors. Adding FF to the model rendered perfect prediction (100%) in the trained confusion matrix and AUC of 99.8%. The most important predictors of credit card fraud were the FF coefficient for the High book-to-market ratio Minus Low factor. The second most influential variable was the year of reporting, and third most important was the Fama-French 3-factor model <i>R</i><sup>2</sup> – together these described most of the variance in credit card fraud occurrence. In all cases the four major SOX specific opinions rendered by auditors and the signed SOX report had little predictive influence.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 2","pages":"95-107"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133283930","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

Trend-cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets 基于模糊变换的趋势周期估计及其在市场牛熊阶段识别中的应用

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-11 DOI: 10.1002/isaf.1473

Linh Nguyen, Vilém Novák, Soheyla Mirshahi

引用次数: 1

Using clustering ensemble to identify banking business models 使用聚类集成识别银行业务模型

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-04-28 DOI: 10.1002/isaf.1471

Bernardo P. Marques, Carlos F. Alves

{"title":"Using clustering ensemble to identify banking business models","authors":"Bernardo P. Marques, Carlos F. Alves","doi":"10.1002/isaf.1471","DOIUrl":"10.1002/isaf.1471","url":null,"abstract":"<div>\u0000 \u0000 <p>The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non-core banks), as well as banks with a stable business model over time (persistent banks) and others (non-persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 2","pages":"66-94"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116746180","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

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra-day data 一个集成了内在模式函数、人工神经网络和遗传算法的预测系统，用于预测标准普尔500指数日内数据

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-03-18 DOI: 10.1002/isaf.1470

Salim Lahmiri

{"title":"A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra-day data","authors":"Salim Lahmiri","doi":"10.1002/isaf.1470","DOIUrl":"10.1002/isaf.1470","url":null,"abstract":"<div>\u0000 \u0000 <p>There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal-processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra-day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra-day price data. For comparison purposes, the performance of the EMD-GA-ANN presented is compared with that of a GA-ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA-general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root-mean-squared errors show evidence of the superiority of EMD-GA-ANN over WT-GA-ANN and GA-GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time-consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 2","pages":"55-65"},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130791862","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

The role of attribute selection in Deep ANNs learning framework for high-frequency financial trading 属性选择在深度人工神经网络高频金融交易学习框架中的作用

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-03-12 DOI: 10.1002/isaf.1466

Monira Essa Aloud

{"title":"The role of attribute selection in Deep ANNs learning framework for high-frequency financial trading","authors":"Monira Essa Aloud","doi":"10.1002/isaf.1466","DOIUrl":"10.1002/isaf.1466","url":null,"abstract":"<div>\u0000 \u0000 <p>In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time-series forecasting. This paper analyses the role of attribute selection on the development of a simple deep-learning ANN (D-ANN) multi-agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D-ANN multi-agent framework over different time spans of high-frequency (HF) intraday asset time-series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short-term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision-support systems and autonomous trading strategies for the financial market.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 2","pages":"43-54"},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127453318","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: 3