Agree to Disagree: Personalized Temporal Embedding and Routing for Stock Forecast

Stock forecast is a crucial yet challenging task in modern quantitative trading. Given theoretical and investment merits, recently a variety of deep learning methods have been proposed for automatically simulating stock movements from historical time series. However, these methods typically follow the i.i.d. assumption that actually contradicts the complex trading environment. In reality, individual stocks often exhibit diverse volatility patterns, while macro market scenarios may also change over time, jointly resulting in distribution shifts and weak generalization. To combat these bottlenecks, in this paper we propose a new learning architecture called Personalized Temporal Embedding and Routing ( PTER ) to improve stock forecast by forming a relaxed weight-sharing paradigm. The key of PTER is introducing hypernetworks to guide tailoring target network parameters, such that stock time series are embedded adapting to multi-object multi-scenario data disparities. Specifically, in the encoding stage, PTER first captures hyper-knowledge characterizing the similarity and peculiarity of different stocks and market scenarios. The knowledge space is then projected onto the temporal parameter space, enabling the customization of protruded features from chaotic observation signals. In the inference stage, each sample is dispatched to orthogonal predictor heads to dynamically output expected returns based on market conditions. Through experiments on benchmark datasets spanning over five years on four of the world's largest exchange markets, we show that PTER improves the cumulative and risk-adjusted revenue performance by a significant margin.