2024年度人工知能学会全国大会(第38回), May 31, 2024
2024年度人工知能学会全国大会(第38回) (JSAI2024)
DeepHedge, using deep learning and price time series simulation for better hedging, is noted for handling real-world market issues like trading fees, not just ideal markets. It's known that training gets tough with standard feedforward neural networks in Deep Hedging, but some settings have efficient structures like the No-Transaction Band Network. Deep Hedging can be seen through reinforcement learning too. Learning hedging strategies with actor-critic reinforcement learning is done, but this can make training harder. This study introduces an algorithm to model value functions well, making neural network learning easier across many problems. It shows that this method outputs better hedging strategies faster than typical networks.
@inproceedings{Matoya2024-jsai38,
title={{価値関数学習に基づいた効率的DeepHedging機構}},
author={的矢 知樹 and 王 允卓 and 平野 正徳 and 今城 健太郎},
booktitle={2024年度人工知能学会全国大会(第38回)},
year={2024}
}