FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
Serena Della Corte, Laurens Van Mieghem, Antonis Papapantoleon, and Jonas Papazoglou-Hennig
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
Machine learning regression models assess the impact of US tariffs on the Australian stock market index around the April 2025 implementation date.
citing papers explorer
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Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
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Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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USA Tariffs Effect: Machine Learning Insights into the Stock Market
Machine learning regression models assess the impact of US tariffs on the Australian stock market index around the April 2025 implementation date.