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Quantitative Investing Blog - Lucena Research

How To Forecast Securities Using Neural Networks

At Lucena we look to turn knowledge into actionable insights. CEO Erez Katz discusses using machine learning to forecast securities in a webinar recording. 
Erez Katz

 Erez Katz, Lucena Research CEO and Co-founder

Using Deep Neural Nets to Forecast Stock Prices


There's been a lot of buzz surrounding using machine learning to forecast securities. In the below video, I highlight a unique approach to identifying investment opportunities through the use of deep neural networks.

More specifically, how convolutional neural networks (CNN) provide a compelling approach to classifying time series data in order to project stocks’ impending price action.

What You Can Expect: 

A quick explanation of what deep neural networks are and how convolutional neural networks (CNNs) classify images.

How CNNs are used to recognize hand writing with uncanny accuracy (above 99.7%).

Discussion on how the success of computer vision using CNNs for image classification, speech recognition, and object detection can also be applied to tradable securities.

How our trial and error led to a compelling solution. Specifically, what seemed promising on paper, but didn’t work for us.

Lastly, what specific actions we took to “help” the neural networks learn and how we attempted to overcome data not always conforming to IID (Independently and Identically Distributed data) and the non-stationary nature of the financial markets.

Whether you're an investment professional looking to understand machine learning or a Quant with experience in quantitative finance this discussion has something for you.

Our goal is to give you a glimpse into the considerations that a quant team takes into account while attempting to provide enough actionable reference for the portfolio managers to succeed. Enjoy! 

 

 

 Additional Resources:

 Full list of Q&A received during the webinar

 Video: How to Apply Deep Reinforcement Learning to Trading

 Video: The Journey of Validating Alternative Data Signals 

 

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