Abstract: Markov Portfolio Theory has observed that investors put together a collection of stocks to build optimally diversified portfolios. The diversification is also emphasised in modern portfolio theory in neo-classical finance for rational investors. But despite the fact that such models provide effective normative explanations of how investors should behave, it is unknown whether investors ultimately do behave in this way.
This talk presents a Behavioural Finance research project focusing on the analysis of trader behaviour and whether it can be accurately emulated and predicted by analysing historical trader decision making using machine learning techniques. One aim is to try to imitate the behaviour (decision making) of traders based on their historical portfolio and market data, in order to predict their future decision making. The methods being used include inverse reinforcement learning and generative adversarial imitation learning, with a view to suggesting improvements and adjustments to adapt these models to be used with financial market data.
A second aim is to study visual trading patterns using candlestick pictures of stock data which are used by traders in their decision making. It is possible to apply machine learning techniques to extract useful patterns from the pictures and standard image classification methods have so far been applied, such as Convolutional Neural Networks (CNN) for multi financial time series image classification. However, since CNN does not have time series features, Convolutional LSTM which is a combination of CNN and LSTM is being used, which allows for the capturing of time series data.
Bio: Yuting Fu is a PhD student in Data Science at the Oxford-NIE financial Big Data Lab, Mathematical Institute at the University of Oxford. Her research interest lies in machine/deep learning in finance, including trading behaviors and performance evaluation.
Prior to entering Oxford University, Yuting won the first prize in Sichuan Province in the 2014 China National Olympiad in Mathematics and second prize of Beijing in the 2017 China Undergraduate Mathematical Contest in Modeling. She graduated from Tsinghua University in 2019 with a first class honours Bachelor's degree in Pure and Applied Mathematics at Tsinghua University.