FinTech revolution: the impact of management information systems upon relative firm value and risk

The FinTech or ‘financial technology’ revolution has been gaining increasing interest as technologies are fundamentally changing the business of financial services. Consequently, financial technology is playing an increasingly important role in providing relative performance growth to firms. It is also well known that such relative performance can be observed through pairs trading investment. Therefore pairs trading have implications for understanding financial technology performance, yet the relationships between relative firm value and financial technology are not well understood. In this paper we investigate the impact of financial technology upon relative firm value in the banking sector. Firstly, using pairs trade data we show that financial technologies reveal differences in relative operational performance of firms, providing insight on the value of financial technologies. Secondly, we find that contribution of relative firm value growth from financial technologies is dependent on the specific business characteristics of the technology, such as the business application and activity type. Finally, we show that financial technologies impact the operational risk of firms and so firms need to take into account both the value and risk benefits in implementing new technological innovations. This paper will be of interest to academics and industry professionals

Identifying utility functions using random forests

Utility functions are general purpose functions, which are useful in many parts of a system. To facilitate reuse, they are usually implemented in specific libraries. However, developers frequently miss opportunities to implement general-purpose functions in utility libraries, which decreases the chances of reuse. In this paper, we describe our ongoing investigation on using Random Forest classifiers to automatically identify utility functions. Using a list of static source code metrics we train a classifier to identify such functions, both in Java (using 84 projects from the Qualitas Corpus) and in JavaScript (using 22 popular projects from GitHub). We achieve the following median results for Java: 0.90 (AUC), 0.83 (precision), 0.88 (recall), and 0.84 (F-measure). For JavaScript, the median results are 0.80 (AUC), 0.75 (precision), 0.89 (recall), and 0.76 (F-measure)