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Price Returns Prediction using Recurrent Neural Networks with Long Short Term Memory (LSTM) Units

Traditionally RNNs have been used for text classification and sentiment analysis; in this paper, we extend the idea to price returns prediction. The primary objective of the models will be to accurately predict 5-minute price returns based on multiple price based features. In our network, we use stacked LSTMs instead of MLP architecture. LSTM units are preferred as its architecture helps in solving the vanishing gradient problem and enables in modeling the long-term dependency which is persistent in financial price series data.

Sujit Khanna, Quantitative Researcher - Algorithmic Trading at Edelweiss Financial Services

Sujit works at Edelweiss securities as a Quantitative Researcher in the Algorithmic Trading Desk where he uses a host of quantitative techniques to create Alpha strategies. Sujit’s work majorly focuses on analyzing and applying ML algorithms for price prediction and environment modeling, which includes creating feature selection and dimensionality reduction models like RFE, Lasso, t-SNE, and PCA. Over the last couple of years, he has focused his attention on using Deep Learning Algorithms for Price predictions at Mid and High-Frequency intervals. Such algorithms included variants of LSTM RNNs, Deep generative Models, Adversarial, and Evolutionary algorithms. Prior to Edelweiss securities, Sujit was working with Morgan Stanley in the Quantitative and Derivatives Strategies team. Sujit has bachelor’s degree in Electronics and Telecommunication engineering from Mumbai University and an MBA from NMIMS.