Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment
Neural network approach for portfolio optimization with leverage constraints, outperforming benchmarks during high inflation periods.
What it examines
The paper aims to develop a dynamic multi-period allocation strategy to outperform a passive benchmark under high inflation, considering leverage constraints. It formulates an optimal control problem using a jump-diffusion model and proposes a leverage-feasible neural network (LFNN) to solve it.
What it concludes
The research offers a robust framework for portfolio optimization under high inflation and leverage constraints, with potential applications in managing large sovereign wealth funds. Future research could explore different market conditions and further refine the LFNN model.
Evidence objects
The research offers a robust framework for portfolio optimization under high inflation and leverage constraints, with potential applications in managing large sovereign wealth funds. Future research could explore different market conditions and further refine the LFNN model.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. To this end, we formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. Assuming the asset prices follow the jump-diffusion model during high… ▽ More Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. To this end, we formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. Assuming the asset prices follow the jump-diffusion model during high inflation periods, we first establish a closed-form solution for the optimal strategy that outperforms a passive strategy under the cumulative quadratic tracking difference (CD) objective, assuming continuous trading and no bankruptcy. To obtain strategies under the bounded leverage constraint among other realistic constraints, we then propose a novel leverage-feasible neural network (LFNN) to represent control, which converts the original constrained optimization problem into an unconstrained optimization problem that is computationally feasible with standard optimization methods. We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. We further apply the LFNN approach to a four-asset investment scenario with bootstrap resampled asset returns from the filtered high inflation regime data. The LFNN strategy is shown to consistently outperform the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the end of the investment horizon. △ Less
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