Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting

• URL: http://arxiv.org/abs/2505.24835v3
• Date: Wed, 16 Jul 2025 21:42:51 GMT
• Title: Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting
• Authors: Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying, Zhiyan Xu, Wen Zou, Haolun Wu, Xiuqiang He, Xing Tang,
• Abstract summary: We introduce a Risk-aware Time-Series Predict-and-Allocate (RTS-PnO) framework to solve the problem of fund allocation.<n>The framework contains three features: (i) end-to-end training with objective alignment measurement, (ii) adaptive forecasting uncertainty calibration, and (iii) agnostic towards forecasting models.<n>The evaluation of RTS-PnO is conducted over both online and offline experiments.
• Score: 10.540006708939647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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