Intelligent Systems in Accounting, Finance and Management最新文献_第6页

Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework 基于期望值框架的点对点贷款组合数据驱动优化

Intelligent Systems in Accounting, Finance and Management Pub Date : 2021-03-17 DOI: 10.1002/isaf.1490

Ajay Byanjankar, József Mezei, Markku Heikkilä

{"title":"Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework","authors":"Ajay Byanjankar, József Mezei, Markku Heikkilä","doi":"10.1002/isaf.1490","DOIUrl":"10.1002/isaf.1490","url":null,"abstract":"<p>In recent years, peer-to-peer (P2P) lending has been gaining popularity amongst borrowers and individual investors. This can mainly be attributed to the easy and quick access to loans and the higher possible returns. However, the risk involved in these investments is considerable, and for most investors, being nonprofessionals, this increases the complexity and the importance of investment decisions. In this study, we focus on generating optimal investment decisions to lenders for selecting loans. We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk. In the process, we use internal rate of return as the measure of return. As the starting point of the model, we use machine-learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data. Afterwards, we calculate the distance between loans using (i) default probabilities and, as a novel step, (ii) expected value. In the calculations, we utilize kernel functions to obtain similarity weights of loans as the input of the optimization models. Two optimization models are tested and compared on data from the popular P2P platform Lending Club. The results show that using the expected-value framework yields higher return.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"119-129"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050250","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: 4

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach GARCH-RNN混合方法预测原油期货波动率

Intelligent Systems in Accounting, Finance and Management Pub Date : 2021-03-11 DOI: 10.1002/isaf.1489

Sauraj Verma

{"title":"Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach","authors":"Sauraj Verma","doi":"10.1002/isaf.1489","DOIUrl":"10.1002/isaf.1489","url":null,"abstract":"<p>Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"28 2","pages":"130-142"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122598401","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

Journal entry anomaly detection model 日记账异常检测模型

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-12-22 DOI: 10.1002/isaf.1485

Mario Zupan, Verica Budimir, Svjetlana Letinic

{"title":"Journal entry anomaly detection model","authors":"Mario Zupan, Verica Budimir, Svjetlana Letinic","doi":"10.1002/isaf.1485","DOIUrl":"10.1002/isaf.1485","url":null,"abstract":"<div>\u0000 \u0000 <p>Although numerous scientific papers have been written on deep learning, very few have been written on the exploitation of such technology in the field of accounting or bookkeeping. Our scientific study is oriented exactly toward this specific field. As accountants, we know the problems faced in modern accounting. Although accountants may have a plethora of information regarding technology support, looking for errors or fraud is a demanding and time-consuming task that depends on manual skills and professional knowledge. Our efforts are oriented toward resolving the problem of error-detection automation that is currently possible through new technologies, and we are trying to develop a web application that will alleviate the problems of journal entry anomaly detection. Our developed application accepts data from one specific enterprise resource planning system while also representing a general software framework for other enterprise resource planning developers. Our web application is a prototype that uses two of the most popular deep-learning architectures; namely, a variational autoencoder and long short-term memory. The application was tested on two different journals: data set D, learned on accounting journals from 2007 to 2018 and then tested during the year 2019, and data set H, learned on journals from 2014 to 2016 and then tested during the year 2017. Both accounting journals were generated by micro entrepreneurs.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 4","pages":"197-209"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200393","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: 5

Modelling unbalanced catastrophic health expenditure data by using machine-learning methods 利用机器学习方法对不平衡的灾难性医疗支出数据进行建模

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-12-01 DOI: 10.1002/isaf.1483

Songul Cinaroglu

引用次数: 4

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