Financial Big Data Technology and Its Applications

Guest Editor
Prof. Kin Keung Lai
Department of Industrial and Manufacturing Systems Engineering, Hong Kong University, Pokfulam, Hong Kong

Manuscript Topics
Big data often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. Research areas of financial engineering include forecasting, risk management and portfolio optimization. Big data technology is a new perspective in financial engineering research. Big data provides different valuable data sources for financial engineering researchers. Application of big data technology in financial engineering is a hot topic now. The challenges of big data technology can be summarized in three aspects. Firstly, the database or data flow is in petabytes. Secondly, the data isn’t easily put into the traditional rows and columns of conventional databases. Thirdly, the data is perturbed by different sources of noises.

Quantitative Finance and Economics (QFE) aims at providing high-quality research information about advances on big data technology and its applications in finance. Due to the increasing popularity of this topic, world-wide interest in the work performed by researchers in this area is constantly expanding.

Paper submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is November 30th, 2017.

Instructions for authors
Please submit your manuscript to online submission system

Wenhao Chen, Kinkeung Lai, Yi Cai
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Hongxuan Huang, Zhengjun Zhang
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Francesco Audrino, Lorenzo Camponovo, Constantin Roth
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