Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling

Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning(ML) methods have become increasingly popular and achieved promising results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification(to predict the direction) or a regression(to predict the return) problem over the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address this issue, we propose a novel price movement forecasting framework, called Locality-Aware Attention and Iterative Refinement Labeling(LARA), which consists of two main components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to surrounding class-aware label information. Moreover, equipped with metric learning techniques, locality-aware attention enjoys task-specific distance metrics and distributes attention on potentially profitable samples in a more effective way. 2)Iterative refinement labeling further iteratively refines the labels of noisy samples and then combines the learned predictors to be robust to the unseen and noisy samples. In a number of experiments on three real-world financial markets: ETFs, stocks, and cryptocurrencies, LARA achieves superior performance compared with the traditional time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments also demonstrate that LARA indeed captures more reliable trading opportunities.