| Abstract: |
| This talk studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in Wang and Zhou (2020), the discrete-time model makes more general assumptions about the asset`s return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model. |
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