Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Finding the hidden influencers

This thesis is a first step in the search for the characteristics of funders, and the underlying motivation that drives them to participate in crowdfunding. The purpose of the study is to identify demographics and psychographics that influence a funder’s willingness to financially support a crowdfunding project (WFS). Crowdfunding, crowdsourcing and donation literature are combined to create a conceptual model in which age, gender, altruism and income, together with several control variables, are expected to have an influence on a funder’s WFS. Primary data collection was conducted using a survey, and a dataset of 175 potential crowdfunders was created. The data is analysed using a multiple regression and provided several interesting results. First of all, age and gender have a significant effect on WFS, males and young adults until the age of 30 have a higher intention to give money to crowdfunding projects. Second, altruism is significantly positively related to WFS, meaning that the funders do not just care about the potential rewards they could receive, but also about the benefits that they create for the entrepreneur and the people affected by the crowdfunding project. Third, the moderation effect of income was found to be insignificant in this model. It shows that income does not affect the strength of the relationship between the age, gender and altruism, and WFS. This study provides important theoretical contributions by, to the best of my knowledge, being the first study to quantitatively investigate the characteristics of funders and using the funder as the unit of analysis. Moreover, the study provides important insights for entrepreneurs who wish to target the crowd better in order to attract and retain more funders, thereby increasing the chance of success of their project.