用时间序列预测指导风险感知的基金分配，减少决策失误。
Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting
- 构建端到端框架，对齐预测与分配目标
- 自适应校准预测不确定性，提升分配鲁棒性
- 不依赖具体预测模型，适合金融场景落地
基金分配在金融领域日益重要。实际中，需在特定未来周期内将资金配置于特定资产。传统仅依赖预测或预测后优化的方法存在目标错配问题，且先进时间序列模型引入额外预测不确定性。为此，提出无前提假设的、风险感知的时间序列预测-分配框架（RTS-PnO），包含三特性：(i) 目标对齐的端到端训练，(ii) 自适应预测不确定性校准，(iii) 对预测模型无依赖。在离线实验中使用涵盖货币、股票、加密货币三类的8个数据集评估，持续优于多个基线方法。在线实验在腾讯金融科技跨境支付业务上进行，相比产品线方案，后悔值降低8.4%。离线实验代码已开源：https://github.com/fuyuanlyu/RTS-PnO。
原文摘要 · Abstract (English)
Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. Naive solutions such as prediction-only or Predict-then-Optimize approaches suffer from goal mismatch. Additionally, the introduction of the SOTA time series forecasting model inevitably introduces additional uncertainty in the predicted result. To solve both problems mentioned above, we introduce a Risk-aware Time-Series Predict-and-Allocate (RTS-PnO) framework, which holds no prior assumption on the forecasting models. Such a framework contains three features: (i) end-to-end training with objective alignment measurement, (ii) adaptive forecasting uncertainty calibration, and (iii) agnostic towards forecasting models. The evaluation of RTS-PnO is conducted over both online and offline experiments. For offline experiments, eight datasets from three categories of financial applications are used: Currency, Stock, and Cryptos. RTS-PnO consistently outperforms other competitive baselines. The online experiment is conducted on the Cross-Border Payment business at FiT, Tencent, and an 8.4\% decrease in regret is witnessed when compared with the product-line approach. The code for the offline experiment is available at https://github.com/fuyuanlyu/RTS-PnO.
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