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Investing using #DeepLearning, #MacroTrading and #Chatbots

Posted on June 2nd, 2017


Qplum, 185 Hudson Street , Jersey City, suite 1620

Mansi Singhal and Gaurav Chakravorty @Qplum  gave two presentations on how Qplum uses machine learning within a systematic macro investment strategy. Mansi talked about how a macro economic world view is used to focus the ML team on target markets. She walked the audience through an economic analysis of the factors driving the U.S. residential housing market and how an understanding of the drivers (interest rates, GDP, demographics,…) and anticipation of future economic trends (e.g. higher interest rates) would lead them to focus on (or not consider) that market for further analysis by the ML group.

Gaurav ( talked about how they use an AutoEncoder to better understand the factors driving a statistical arbitrage strategy. Here, instead of using a method like principal components analysis, they use a deep learning algorithm to determine the factors driving the prices of a group of stocks. The model uses a relatively shallow neural net. To understand the underlying factors, they look at which factors are the largest driver of current market moves and determine the historical time periods when this factor has been active. One distinction between their factor models and classic CAPM models is that non-linearities are introduced by the activation functions within each layer of the neural net.

Next, Aziz Lookman talked about his analysis showing that an analysis of county-by-county unemployment rates affects the default rates (and therefore the investment returns) on loans within Lending Club.

Lastly, Hardik Patel @Qplum talked about the opportunities and challenges of creating a financial chatbot. The opportunity is that the investment goals and concerns are unique for each customer, so each will have different questions and need different types of information and advice.

The wide variety of questions and answers challenges the developer so their approach has been to develop and LSTM model of the questions which will point the bot to a template that will generate the answer. Their initial input will use word vectors and bag of words methods to map questions to categories.

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