用迁移学习提升新公司股票波动率预测精度
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning
- 从历史数据丰富的资产中提取相似样本，辅助新上市企业波动率建模
- 新发股首日即能显著提升预测效果，优于纯目标数据训练模型
- 适合金融风控、量化交易人员参考，尤其关注新上市公司
金融资产波动率预测对各类金融应用至关重要。本文针对历史数据有限的新发公司或分拆公司等资产，提出一种多源迁移学习方法。通过选取与目标资产最相似的历史数据实例，结合目标数据，构建线性与非线性已实现波动率模型，并对比仅使用目标数据训练、以及使用全部源与目标数据训练的模型表现。结果表明，该迁移学习方法优于其他对比模型，且在新资产上市首日即可带来显著预测性能提升。
原文摘要 · Abstract (English)
Forecasting the volatility of financial assets is essential for various financial applications. This paper addresses the challenging task of forecasting the volatility of financial assets with limited historical data, such as new issues or spin-offs, by proposing a multi-source transfer learning approach. Specifically, we exploit complementary source data of assets with a substantial historical data record by selecting source time series instances that are most similar to the limited target data of the new issue/spin-off. Based on these instances and the target data, we estimate linear and non-linear realized volatility models and compare their forecasting performance to forecasts of models trained exclusively on the target data, and models trained on the entire source and target data. The results show that our transfer learning approach outperforms the alternative models and that the integration of complementary data is also beneficial immediately after the initial trading day of the new issue/spin-off.
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