Samson Qian Director MIT Sloan Quant Finance Club
Samson is a Master of Finance student at the MIT Sloan School of Management studying financial engineering and holds a B.S. degree from the University of California, San Diego in data science. At MIT, he is the president of Sloan’s Quantitative Finance Club and is affiliated with various A.I. and DeFi organizations. He has done extensive research in state-of-the-art machine learning methods applied to statistical analysis, blockchain, quantitative finance, etc.
His experience mainly lies in working on quant research and data science teams and financial services firms working to research and develop quantitative methods to support systematic investment strategies, market analysis, and risk management.
Samson’s MIT thesis “Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging” explores how GANs can be used as an alternative non-parametric approach to simulate and generate market data. As opposed to traditional Monte-Carlo methods that rely on assumptions about underlying distributions. This systematic framework becomes applied to deep hedging algorithms. Moreover, to train agents to find the optimal options pricing and hedging policy. Lastly, this deep reinforcement learning-based approach makes the dynamic hedging strategies more robust and precise compared to traditional greek hedging.
LinkedIn: https://www.linkedin.com/in/samsonq/
GitHub: https://github.com/samsonq
Ai & Quant Conference 2022 Hosted By MIT Sloan Quantitative Finance Club
Samson Qian Director MIT Sloan Quant Finance Club
