Optimal trading under non-negativity constraints using approximate dynamic programming
提出一种扩展的动态规划方法，在证券大宗交易中引入非负约束和时滞状态，以最小化执行成本，并通过数值模拟证明其优于经典模型。
In this paper, we develop an extended dynamic programming (DP) approach to solve the problem of minimising execution cost in block trading of securities. To make the problem more practical, we add non-negativity constraints to the model and propose a novel approach to solve the resulting DP problem to near-optimal results. We also include time lags in the problem state to account for the autoregressive behaviour of most financial securities as a way of increasing problem sensitivity to variability of prices and information. The computation times achieved for the proposed algorithm are fast and allow for the possibility of live implementation. We demonstrate the benefits offered by the new approach through numerical analysis and simulation runs in comparison to the classic model without the non-negativity constraints.