Reinforcement Learning and Hidden Markov Model Based Smart Trading Strategies

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Reinforcement Learning and Hidden Markov Model Based Smart Trading Strategies

Samit Ahlawat discusses AI priorities in trading strategies and his career in quant finance and machine learning.

Friday 29 April 2022

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Podcast Description

QuantSpeak host, Dan Tudball, is joined by Samit Ahlawat, Senior Vice President in Quantitative Research, Capital Modeling at J.P. Morgan Chase, to discuss what researchers should prioritize when using artificial intelligence and machine learning in building automated trading strategies, his career path in quantitative finance and machine learning, and where his research will take him next.

Topics & Timestamps

[00:00 - 01:12] Guest Introduction

[01:12 - 01:34] Opening Question: AI/ML Priorities in Trading Strategy Design

[01:34 - 06:47] Key Guidelines for AI/ML in Trading: Information Leakage, Survivorship Bias, Data Snooping, Model Parsimony, Methodology Choice

[06:47 - 09:11] Production Process: Clean Pipeline, Assumptions, Validation, Recalibration

[09:11 - 10:03] Overview: Hidden Markov Models & Reinforcement Learning for Smarter Trading

[10:03 - 15:19] Comparing AI/ML Methods with Traditional Trading Strategies

[15:19 - 16:34] Career Path: Quantitative Modeling, Finance, and Interdisciplinary Work

[16:34 - 18:33] Career Start: Entering Finance Post-Great Recession

[18:33 - 22:17] Adoption of Machine Learning in Finance: Skepticism, Evolution, and Advantages

[22:17 - 24:41] Future Research: Fine-Tuning, Policy Learning, Generative Models, Data Scarcity

[24:41 - 25:59] Generative Models for Scenario Analysis in Finance

[25:59 - 26:50] Closing.

Disclaimer

Podcasts are for informational purposes only and provided “as is” without any representation or warranty from Fitch Learning of any kind. Comments or statements expressed by speakers may not be those of the Fitch Learning. Fitch Learning is not providing advice or recommendations. Fitch Learning, its directors, officers, or employees do not accept any liability for any loss arising from the use of information.