Business Opportunities in Crowdsourced Stock Market Analysis

This paper presents a study regarding how crowdsourcing and wisdom of crowds effect can be used to perform stock market analysis. This thesis is divided into three main parts. The thesis begins by conducting a systematic literature review based on existing research on crowdsourcing, the wisdom of crowds effect and performance of social investing. In the second part, the thesis also presents a list of existing online crowdsourcing-based investment research platforms. To understand the value of these services, the paper summarizes the accuracy of these investing platforms using the existing academic literature about these services. The paper also provides an empirical contribution. In the third part, the findings of the literature review are used to form the methodology for empirical testing part of this paper. The empirical part acts as a ground work study to benchmark how the learnings from existing research can be applied to build automated trading models using Stonder user opinion data. A geometrical series based method for aggregating user opinions is presented. The thesis also describes a set of algorithms that use the aggregated data as an input and evaluates their performance against benchmark indices using a virtual trading approach. The results of this paper indicate crowdsourcing and the wisdom of crowds provide valuable insight for investment research. As the new MiFID II regulation is about to soon take place, crowdsourced investment research is likely to provide lucrative new business opportunities for data-driven companies in financial industry