Key Challenges Remain for the Application of Deep Learning and Artificial Intelligence in Finance

NEW YORK - Oct. 5, 2017 - PRLog -- While many industries susceptible to automation already been widely adopting Deep Learning, its application has only been growing recently in financial trading. While it is true that asset managers of all sizes are recruiting every Deep Learning professional they can contact, it is also true that traditional alphas are getting very hard to make money on; in fact, Deep Learning is not making a substantive difference, yet.

Investment firms today are looking for information in new data sources like shipping receipts, customer feedback on Twitter, speeches by federal reserve members (even attempting to categorize the tone or word selection in these speeches), and literally anything under the sky. Most of this data is unstructured and there is very little precedence to it. It would be close to impossible to make simple predictive models. Deep Learning has been used to take a lot of unlabeled data and successfully learn the structure. That will help to improve the accuracy of their models.

For Edgar Perez (http://www.mredgarperez.com), author of The AI Breakthrough (www.theaibreakthrough.com), Knightmare on Wall Street (www.knightmareonwallstreet.com) and The Speed Traders (www.thespeedtraders.com), the advancement of deep learning will impact financial trading in the near-term future. Some firms are using Convolutional Neural Networks (CNNs) to analyze trading charts to see if they can identify patterns in stock trading charts; other firms are using Deep Reinforcement Learning (Deep Q Learning) to analyze the optimal times to trade. Ultimately, "the application of deep learning in trading will be second nature," says Perez; he is a well-known international futurist, artificial intelligence and deep learning speaker who currently offers the world's first Deep Learning (http://www.terrapinntraining.com/training/3-Day-Mastercla...) workshop for Fortune 500 firms and private equity groups.

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Artificial intelligence has been referred as the general ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term has indeed been frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, generalize, discover meaning, or learn from past experiences. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge.

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