BigData-FAB 2016 : Special Issue on “Big Data and Machine Learning in Finance, Accounting and Business” in Electronic Commerce Research (Springer)
Call For Papers
Electronic Commerce Research (Springer,SSCI index, 2014 IF=1.773)
Editor-in-Chief, J. Christopher Westland
Special Issue on “Big Data and Machine Learning in Finance, Accounting and Business”
Advances in information and communication technologies are rapidly expanding the boundaries of accounting, financial and business analysis, operations and competitive strategy. Digital data volumes have been growing exponentially in the past century, and their trajectory shows no sign of flagging in the near future. The growth in volume, scope and modality of data far exceeds the capabilities of hardware, software and telecommunications technologies to keep up with. According to storage giant EMC, global data supply is now around 10 zettabytes (trillion gigabytes) with only around 0.5% of this ever properly analyzed. The rise of information economics is not limited to just one country, but is a global phenomenon, and one that increasingly integrates business across cultures and political boundaries.
Interaction with data today is more complicated due to the large scale of volume, real-time streaming in velocity, various and different data formats, and veracity in data uncertainty, but potentially more rewarding than ever. Finance, accounting and business in general must embrace this complexity to effectively fulfill their responsibilities. This will demand new computational tools, a more comprehensive embrace of statistical methods, and expanded reporting. As with other industries, the only effective solution is the one that embraces information technology. Most if not all financial data is now digitally recorded, and dispersed among servers, clouds and networks of computers over which the audited firm has no control. Potential values and useful patterns are deeply hidden in the interior of the big data of finance and business. Deep learning is an emerging branch of machine learning based on a set of complex analytic algorithms on big data, which can help to discover the valuable patterns from financial data and to build efficient models in accounting, finance, and business.
This special issue seeks high-quality submissions for research in the emerging ‘big data’ information technologies. We aim to promote research on organizing and managing finance big data in efficient ways, building novel big data driven business models, metrics for traditional finance issues with big data analytic tools, heterogeneous finance data integration and application, security ensuring and privacy protecting in finance big data, business recommendation, prediction and promotion by using big data, and so on.
Original and research articles are solicited in all aspects including theoretical studies, practical applications, and experimental prototypes. Potential topics include, but are not limited to:
Novel Approaches for Financial Data Organization, Storage, and Integration
Data Analytics and Prediction in Finance Business
Machine Learning Algorithms for Financial Business Models
Finance Public Feeling Cognition from Social Community
Credit Rating, Market Forecasting, and Risk Managing in view of Data Analytics
Visualization for Financial Data
Influence of Public Opinions on the Finance Business
New Business Model Discovery based on Financial and Business Big Data
Complex Relations Network Analysis for Listed Companies
High-Frequency Trading and Algorithmic Execution
Security, Privacy, and Regulation in Finance Data
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers may only be submitted if the paper has been extended at least 40%. Please submit your manuscript through the online system: https://www.editorialmanager.com/elec/default.aspx. All submitted papers will be peer-reviewed and selected based on both their quality and their relevance to the theme of this special issue.
Submission: Nov 30, 2016
Notification: Jan 31, 2017
Revised submission: Mar 31, 2017
Final notification: Apr 30, 2017
Publication: 2nd or 3rd quarter of 2017
Beijing Normal University
Institute for Financial Studies,