2 research outputs found
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
Quantitative investment is a fundamental financial task that highly relies on
accurate stock prediction and profitable investment decision making. Despite
recent advances in deep learning (DL) have shown stellar performance on
capturing trading opportunities in the stochastic stock market, we observe that
the performance of existing DL methods is sensitive to random seeds and network
initialization. To design more profitable DL methods, we analyze this
phenomenon and find two major limitations of existing works. First, there is a
noticeable gap between accurate financial predictions and profitable investment
strategies. Second, investment decisions are made based on only one individual
predictor without consideration of model uncertainty, which is inconsistent
with the workflow in real-world trading firms. To tackle these two limitations,
we first reformulate quantitative investment as a multi-task learning problem.
Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE)
framework for quantitative investment to mimic the efficient bottom-up trading
strategy design workflow of successful trading firms. In Stage one, multiple
independent trading experts are jointly optimized with an individual
uncertainty-aware loss function. In Stage two, we train neural routers
(corresponding to the role of a portfolio manager) to dynamically deploy these
experts on an as-needed basis. AlphaMix is also a universal framework that is
applicable to various backbone network architectures with consistent
performance gains. Through extensive experiments on long-term real-world data
spanning over five years on two of the most influential financial markets (US
and China), we demonstrate that AlphaMix significantly outperforms many
state-of-the-art baselines in terms of four financial criteria