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

TL;DR

This paper introduces a risk-aware, flexible fund allocation framework that improves decision-making by integrating time-series forecasting with uncertainty calibration, outperforming baselines in financial datasets and online experiments.

Contribution

It proposes the RTS-PnO framework, which aligns forecasting and allocation goals, calibrates uncertainty adaptively, and is model-agnostic, addressing key issues in fund allocation.

Findings

RTS-PnO outperforms baselines across multiple financial datasets.

Online experiments show an 8.4% reduction in regret.

Framework is flexible and does not rely on specific forecasting models.

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.