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

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

Call for papers about Google duplex and related developments 征集有关Google duplex及其相关发展的论文

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-02-14 DOI: 10.1002/isaf.1453

引用次数: 0

Call for papers - special issue on “AI and big data in accounting and finance” 征文-“会计与金融中的人工智能与大数据”特刊

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-02-14 DOI: 10.1002/isaf.1452

引用次数: 0

Blockchain for tracking serial numbers in money exchanges 用于追踪货币交易序列号的区块链

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-02-14 DOI: 10.1002/isaf.1462

Kareem Mohamed, Amr Aziz, Belal Mohamed, Khaled Abdel-Hakeem, Mostafa Mostafa, Ayman Atia

引用次数: 3

Exploring corporate governance research in accounting journals through latent semantic and topic analyses 通过潜在语义和主题分析探索会计期刊中的公司治理研究

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-02-14 DOI: 10.1002/isaf.1461

Ferhat D. Zengul, James D. Byrd Jr, Nurettin Oner, Mark Edmonds, Arline Savage

{"title":"Exploring corporate governance research in accounting journals through latent semantic and topic analyses","authors":"Ferhat D. Zengul, James D. Byrd Jr, Nurettin Oner, Mark Edmonds, Arline Savage","doi":"10.1002/isaf.1461","DOIUrl":"10.1002/isaf.1461","url":null,"abstract":"<div>\u0000 \u0000 <p>The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accounting's important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journal-quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDC's top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 4","pages":"175-192"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126409837","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

Using long short-term memory neural networks to analyze SEC 13D filings: A recipe for human and machine interaction 使用长短期记忆神经网络分析SEC 13D文件:人机交互的配方

Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-01-09 DOI: 10.1002/isaf.1464

Murat Aydogdu, Hakan Saraoglu, David Louton

{"title":"Using long short-term memory neural networks to analyze SEC 13D filings: A recipe for human and machine interaction","authors":"Murat Aydogdu, Hakan Saraoglu, David Louton","doi":"10.1002/isaf.1464","DOIUrl":"10.1002/isaf.1464","url":null,"abstract":"<div>\u0000 \u0000 <p>We implement an efficient methodology for extracting themes from Securities Exchange Commission 13D filings using aspects of human-assisted active learning and long short-term memory (LSTM) neural networks. Sentences from the ‘Purpose of Transaction’ section of each filing are extracted and a randomly chosen subset is labelled based on six filing themes that the existing literature on shareholder activism has shown to have an impact on stock returns. We find that an LSTM neural network that accepts sentences as input performs significantly better, with precision of 77%, than an alternately specified neural network that uses the common bag of words approach. This indicates that both sentence structure and vocabulary are important in classifying SEC 13D filings. Our study has important implications, as it addresses the recent cautions raised in the literature that analysis of finance and accounting-related text sources should move beyond bag-of-words approaches to alternatives that incorporate the analysis of word sense and meaning reflecting context.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 4","pages":"153-163"},"PeriodicalIF":0.0,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134364152","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

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms 基于深度学习算法的股票价格预测及其与机器学习算法的比较

Intelligent Systems in Accounting, Finance and Management Pub Date : 2019-12-03 DOI: 10.1002/isaf.1459

Mahla Nikou, Gholamreza Mansourfar, Jamshid Bagherzadeh

引用次数: 105