MLP-Initialized Reinforcement Learning for Exit Timing in SPX Short-Straddle Portfolios
This paper studies exit timing for SPX short-straddle portfolios using a two-stage supervised and reinforcement-learning framework. Historical episodes first generate ex-post optimal hold/exit labels for training a multilayer perceptron (MLP) using only contemporaneously available state variables at deployment. The tra...