Benjamin Scharpf

Thesis title:

Assessing the Performance of an Inter-market Trading Strategy in the Low and High Frequency Domain Based on Historic Data Using a Machine Learning Approach


An interesting application and expansion of deep learning concepts will be pioneered in Benjamin’s research by applying them to high frequency order book data, namely using machine learning tools to derive a high frequency trading mechanism. NVWAP (Notional Volume Weighted Average Price) curves are governed by four statistics: steepening/flattening and contraction/expansion. Contraction/expansion of NVWAP curves is quantified by computing the change in total volume on both, the bid and ask-side of the limit order book. This concept should be taken and further developed by machine learning tools to derive whether an automated trading system could operate profitably in practice. For this, the data should be separated in various chunks serving as input for the deep neural net. Its findings should then be applied on trading real time markets to evaluate whether a profitable operation is achievable. To appreciate inter-market relations, two or more correlated assets can be investigated simultaneously to derive trading decisions.The science of machine learning is quite new compared to the concepts of finance and investments. Given recent developments in terms of the amount of data recorded and accessible, and modern computing technology, it is worthwhile to search for synergy between the two subjects and scan the data for patterns that have not been obvious for market participants before. Recent major advances in combining advanced computational tools and finance further motivate to do so. One of the most recent tools in machine learning are deep neural nets (DNN). A deep neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers. For example, one can build deep neural networks for modelling mortgage delinquency and prepayment risk using a dataset of over 120 million prime and subprime mortgages and simulate mortgage portfolios for risk analysis purposes. It was even found that some of the classical theory in finance such as the efficient market hypothesis might be challenged by deep learning. Yet another motivation is to apply deep learning to discover trading strategies not yet executed in the markets. A neural net could be trained on entering and exiting futures markets and optimize its decisions based on past experience.

First supervisor:

Panos Parpas


4 – Economics, Finance & the World Economy