Reinforcement learning
Choosing trading strategies using importance sampling
The sampling technique is more efficient than A-B testing at comparing decision rules
Podcast: Halperin on reinforcement learning and option pricing
Fidelity quants working on machine learning techniques to optimise investment strategies
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Buy-side quants of the year: Matthew Dixon and Igor Halperin
Risk Awards 2022: New machine learning tool tackles an age-old, old age problem
Podcast: Man Group’s Zohren on forecasting prices with DeepLOB
Deep learning model can project prices around 100 ticks into the future
An ‘optimal’ way to calculate future P&L distributions?
Quants use neural networks to upgrade classic options pricing model
Podcast: NYU’s Kolm on transaction costs and machine learning
TCA methodologies that ignore partial fills “might be off by 20% to 30%”
The case for reinforcement learning in quant finance
The technology behind Google’s AlphaGo has been strangely overlooked by quants
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
Buy-side quant of the year: Gordon Ritter
Risk Awards 2019: Quant uses new tech to tackle old problem of optimal execution
Machine learning could solve optimal execution problem
Reinforcement learning can be used to optimally execute order flows
Machine learning for trading
Gordon Ritter applies reinforcement learning to dynamic trading strategies with market impact