Intelligent Systems in Accounting, Finance and Management最新文献_第8页

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

Some issues in blockchain for accounting and the supply chain, with an application of distributed databases to virtual organizations† 区块链在会计和供应链中的一些问题，以及分布式数据库在虚拟组织中的应用

Intelligent Systems in Accounting, Finance and Management Pub Date : 2019-11-15 DOI: 10.1002/isaf.1457

Daniel E. O'Leary

引用次数: 27

Co-evolved genetic programs for stock market trading 共同进化的股票市场交易基因程序

Intelligent Systems in Accounting, Finance and Management Pub Date : 2019-11-15 DOI: 10.1002/isaf.1458

Jason F. Nicholls, Andries P. Engelbrecht

{"title":"Co-evolved genetic programs for stock market trading","authors":"Jason F. Nicholls, Andries P. Engelbrecht","doi":"10.1002/isaf.1458","DOIUrl":"10.1002/isaf.1458","url":null,"abstract":"<div>\u0000 \u0000 <p>The profitability of trading rules evolved by three different optimised genetic programs, namely a single population genetic program (GP), a co-operative co-evolved GP, and a competitive co-evolved GP is compared. Profitability is determined by trading thirteen listed shares on the Johannesburg Stock Exchange (JSE) over a period of April 2003 to June 2008. An empirical study presented here shows that GPs can generate profitable trading rules across a variety of industries and market conditions. The results show that the co-operative co-evolved GP generates trading rules perform significantly worse than a single population GP and a competitively co-evolved GP. The results also show that a competitive co-evolved GP and the single population GP produce similar trading rules. The profits returned by the evolved trading rules are compared to the profit returned by the buy-and-hold trading strategy. The evolved trading rules significantly outperform the buy-and-hold strategy when the market trends downwards. No significant difference is identified among the buy-and-hold strategy, the competitive co-evolved GP, and single population GP when the market trends upwards.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"26 3","pages":"117-136"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115068287","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: 1

Exploring the predictability of range-based volatility estimators using recurrent neural networks 利用递归神经网络探索基于区间的波动估计器的可预测性

Intelligent Systems in Accounting, Finance and Management Pub Date : 2019-08-09 DOI: 10.1002/isaf.1455

Gábor Petneházi, József Gáll

引用次数: 6