Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
This addresses computational bottlenecks for financial analysts and traders needing faster decision-making in dynamic portfolio allocation and real-time option pricing, though it appears incremental as it applies RL to a known preconditioning problem.
The paper tackles slow convergence in iterative solvers for portfolio optimization and option pricing by using reinforcement learning to dynamically adjust block-preconditioner sizes, resulting in significantly accelerated convergence and reduced computational cost in evaluations on real-world portfolio matrices.
We present a reinforcement learning (RL)-driven framework for optimizing block-preconditioner sizes in iterative solvers used in portfolio optimization and option pricing. The covariance matrix in portfolio optimization or the discretization of differential operators in option pricing models lead to large linear systems of the form $\mathbf{A}\textbf{x}=\textbf{b}$. Direct inversion of high-dimensional portfolio or fine-grid option pricing incurs a significant computational cost. Therefore, iterative methods are usually used for portfolios in real-world situations. Ill-conditioned systems, however, suffer from slow convergence. Traditional preconditioning techniques often require problem-specific parameter tuning. To overcome this limitation, we rely on RL to dynamically adjust the block-preconditioner sizes and accelerate iterative solver convergence. Evaluations on a suite of real-world portfolio optimization matrices demonstrate that our RL framework can be used to adjust preconditioning and significantly accelerate convergence and reduce computational cost. The proposed accelerated solver supports faster decision-making in dynamic portfolio allocation and real-time option pricing.