Computational analytics for venture finance

This thesis investigates the application of computational analytics to the domain of venture finance — the deployment of capital to high-risk ventures in pursuit of maximising financial return. Traditional venture finance is laborious and highly inefficient. Whilst high street banks approve (or reject) personal loans in a matter of minutes It takes an early-stage venture capital (VC) firm months to put a term sheet in front of a fledgling new venture. Whilst these are fundamentally different forms of finance (longer return period, larger investments, different risk profiles) a more data-informed and analytical approach to venture finance is foreseeable. We have surveyed existing software tools in relation to the venture capital investment process and stage of investment. We find that analytical tools are nascent and use of analytics in industry is limited. To date only a small handful of venture capital firms have publicly declared their use of computational analytical methods in their decision making and investment selection process. This research has been undertaken with several industry partners including venture capital firms, seed accelerators, universities and other related organisations. Within our research we have developed a prototype software tool NVANA: New Venture Analytics — for assessing new ventures and screening prospective deal flow. We have focused on computational analytics in the context of three sub-components of the NVANA system. Firstly, improving the classification of private companies using supervised and multilabel classification techniques to develop a novel form of industry classification. Secondly, we have investigated the potential to benchmark private company performance based upon a company’s “digital footprint”. Finally, the novel application of collaborative filtering and content-based recommendation techniques to the domain of venture finance: • Multi-label Industry Classification — we utilise supervised learning techniques (Naive Bayes, c4.5, Random Forests, Support Vector Machines (SVM)) to address the shortcomings of existing schemes (out-of-date, misrepresentation, misinterpretation) and automating the process of classifying private companies.