| Content | In this event, Dr Hao Ni will give a 45 minutes presentation on the second part of the supervised learning algorithms, which include the following aspects:1. Convolutional neural network (CNN);2. Recurrent neural network (RNN);3. Learning to trade via direct reinforcement learning (RRL) using recurrent neural network (Moody, 2001 12(4)).In this talk, she will explain the architectures of CNN and RNN, and the advantages of CNN and RNN in image data and sequential data respectively. Additionally she will focus on one financial application of RNN for learning an optimal trading strategy based on (Moody, 2001 12(4)).The lecture is followed by a Q&A session and some stimulating group discussions focused on the challenges of the quantitative trading. We encourage participants to share their experience, thoughts and ideas on quantitative trading.Reference1. Moody, J. and Saffell, M., 2001. Learning to trade via direct reinforcement. IEEE transactions on neural Networks, 12(4), pp.875-889.Link password:MachineLearningQuantFinance1 |