Pricing collateralized loan obligation tranches using machine learning : Machine learning applied to financial data

Sammanfattning: Machine learning and neural networks have recently become very popular in a large category of domains, partly thanks to their ability to solve complex problems by finding patterns in data, but also due to an increase in computing power and data availability. Successful applications of machine learning include for example image classification, natural language processing, and product recommendation. Despite the potential upside of machine learning applied to financial data there exists relatively few articles published while the ones that do exist exhibit that there exists a potential for the tools that it provides. This thesis utilizes neural networks to price collateralized loan obligations which is a type of bond that is backed by a large pool of corporate loans, rather than being issued by a single company or government like a regular bond. The large pool of corporate loans and structure of a collateralized loan obligation makes it a good candidate for this type of research as it involves regressing a large number of variables into a final single real-valued price of the bond where the relations are not necessarily linear. The thesis establishes a relatively simple model and builds upon this using a state-of-the-art ensemble method while also exploring a volatility scaled loss function. The findings of this thesis are that artificial neural networks can price collateralized loan obligations using only their structural and loan pool data with an accuracy close to that of a human. Ensemble methods outperform non-ensemble methods and boost performance by up to 28% when only considering mean squared error while scaling the loss function with the inverse of market volatility does not boost performance. The best performing model can price a collateralized loan obligation tranche rated AAA with an average absolute error of 0.88 and an equity tranche with an average mean absolute error of 4.67.