Using semantic fingerprinting in finance

Researchers in finance and adjacent fields have increasingly been working with textual data, a common challenge being analysing the content of a text. Traditionally, this task has been approached through labour- and computation-intensive work with lists of words. In this article we compare word list analysis with an easy-to-implement and computationally efficient alternative called semantic fingerprinting. Using the prediction of stock return correlations as an illustration, we show semantic fingerprinting to produce superior results. We argue that semantic fingerprinting significantly reduces the barrier to entry for research involving textual content analysis, and we provide guidance on implementing this technique.     

Drivers of optimal prices in two-sided markets: the state of the art

In two-sided markets, a platform has two groups of customers and enables their interactions. Demand on one market side depends on demand on the other; therefore, when platforms set prices, they must consider this interdependency and set prices on both sides simultaneously. By surveying the vast theoretical marketing and economics literature, this article provides a clear and systematic overview of profit-maximizing pricing in two-sided markets. It identifies and structures the various drivers of those optimal prices. Using this framework, it summarizes the findings from prior research and offers an assessment of the state of the art with regard to drivers of optimal prices and their impacts on a platform’s price setting. This improves understanding of both the principles of two-sided markets and their optimal pricing. Finally, this state of the art paper suggests some directions for further research.