31 citations · 32 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
Samuel Atkins, Ali Fathi, Sammy Assefa
A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. U…
A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging
Ali Fathi, Bernhard Hientzsch
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare…
Adversarial Attacks on Deep Algorithmic Trading Policies
Yaser Faghan, Nancirose Piazza, Vahid Behzadan +1
Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown…
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
Masoud Hashemi, Ali Fathi
This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. Our proposed…
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds
Greg Kirczenow, Masoud Hashemi, Ali Fathi +1
This paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. We consider an example of a hypothetical illi…
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)
Greg Kirczenow, Ali Fathi, Matt Davison
This paper studies the application of machine learning in extracting the market implied features from historical risk neutral corporate bond yields. We consider the example of a hy…