Machine Learning-based Stock Trading Strategies



Special thanks: Klout / Lithium for hosting and providing video recording!

Lightning talk: Verbs of data science (Abe Gong, Jawbone)

Abstract: Almost everything you’ve read and heard about data science focused on nouns: algorithms, software, infrastructure, etc. that data workers use, or products that they make. Noun-based texts teach tools and technical skills, but do not teach about time. Therefore, they have little to say about scheduling, deadlines, efficiency, and return on investment—fundamental concepts that separate abstract thinkers from productive doers. This lightning talk will kick off the evening with thoughts about the verbs of data science: how to arrange your workflow to get more data work done, on deadline.

Main talk: Machine Learning-based Stock Trading Strategies (Dr. Tucker Balch, Lucena Research)

Abstract: Dr. Tucker Balch will review Lucena Research’s approach to building Machine Learning-based trading strategies. The presentation will include several example strategies developed at Lucena. Tucker will also talk about some of the challenges for ML in this domain and lessons learned.

Bio:

Tucker Balch, Ph.D. is an associate professor of Interactive Computing at Georgia Tech, and CTO of Lucena Research. His course at Coursera, Computational Finance, Part I has been taken by more than 100,000 students worldwide. At Georgia Tech he teaches courses in Artificial Intelligence and Finance. Balch has published over 120 research publications related to Robotics and Machine Learning. His work has been covered by CNN and by New York Times. His graduated students work at Goldman Sachs, Morgan Stanley, Citadel, AQR, and Yahoo! Finance.

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Machine Learning-based Stock Trading Strategies

Special thanks: Klout / Lithium for hosting and providing video recording!





Lightning talk: Verbs of data science (Abe Gong, Jawbone)



Abstract: Almost everything you've read and heard about data science focused on nouns: algorithms, software, infrastructure, etc. that data workers use, or products that they make. Noun-based texts teach tools and technical skills, but do not teach about time. Therefore, they have little to say about scheduling, deadlines, efficiency, and return on investment—fundamental concepts that separate abstract thinkers from productive doers. This lightning talk will kick off the evening with thoughts about the verbs of data science: how to arrange your workflow to get more data work done, on deadline.





Main talk: Machine Learning-based Stock Trading Strategies (Dr. Tucker Balch, Lucena Research)



Abstract: Dr. Tucker Balch will review Lucena Research’s approach to building Machine Learning-based trading strategies. The presentation will include several example strategies developed at Lucena. Tucker will also talk about some of the challenges for ML in this domain and lessons learned.



Bio:



Tucker Balch, Ph.D. is an associate professor of Interactive Computing at Georgia Tech, and CTO of Lucena Research. His course at Coursera, Computational Finance, Part I has been taken by more than 100,000 students worldwide. At Georgia Tech he teaches courses in Artificial Intelligence and Finance. Balch has published over 120 research publications related to Robotics and Machine Learning. His work has been covered by CNN and by New York Times. His graduated students work at Goldman Sachs, Morgan Stanley, Citadel, AQR, and Yahoo! Finance.