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Price movement forecasting aims at predicting the future trends of financialassets based on the current market conditions and other relevant information.Recently, machine learning (ML) methods have become increasingly popular andachieved promising results for price movement forecasting in both academia andindustry. Most existing ML solutions formulate the forecasting problem as aclassification (to predict the direction) or a regression (to predict thereturn) problem over the entire set of training data. However, due to theextremely low signal-to-noise ratio and stochastic nature of financial data,good trading opportunities are extremely scarce. As a result, without carefulselection of potentially profitable samples, such ML methods are prone tocapture the patterns of noises instead of real signals. To address this issue,we propose a novel price movement forecasting framework named LARA consistingof two main components: Locality-Aware Attention (LA-Attention) and IterativeRefinement Labeling (RA-Labeling). (1) LA-Attention automatically extracts thepotentially profitable samples by attending to label information. Moreover,equipped with metric learning techniques, LA-Attention enjoys task-specificdistance metrics and effectively distributes attention to potentiallyprofitable samples. (2) RA-Labeling further iteratively refines the noisylabels of potentially profitable samples, and combines the learned predictorsrobust to the unseen and noisy samples. In a set of experiments on threereal-world financial markets: stocks, cryptocurrencies, and ETFs, LARAsignificantly outperforms several machine learning based methods on the Qlibquantitative investment platform. Extensive ablation studies and experimentsalso demonstrate that LARA indeed captures more reliable trading opportunities.

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