Speaker "Miquel Noguer Alonso" Details Back



Deep Learning in Finance


In this paper we investigate the pro tability of a quantitative trading strategy based on Deep Learning methods. Speci cally we focus on a variant of the Recurrent Neural Network (RNN), the Long Short Term Memory Network (LSTM) and show its predictive power on stock price data. We use LSTM networks for selecting stocks using historical price. The reason why RNNs are good for regression or classi cation of time series or data where time ordering matters is that RNNs capture the variation through time, thanks to its internal state dynamics. We made two studies, the rst focuses on predicting stock returns using one stock at a time. The hit-ratio in this experiment lies in the range 0.47 and 0.60 for the worst respectively best performing stock on unseen \live" data. The second experiment looks at the whole universe of stocks simultaneously. In this experiment our model achieves a hit-ratio between 0.50 and 0.71 on unseen \live" data. From this experiment two portfolios were constructed, a long portfolio and a long-short portfolio with a Sharpe ratio of 8 respectively 10 for each of the portfolios. Our stock universe in both studies is composed of 50 stocks from the S&P 500.


Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and Technology at IEF. He is the co-Founder of the artificial intelligence finance institute. He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).