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

TL;DR

This paper introduces LARA, a novel framework for price movement forecasting that combines locality-aware attention and iterative label refinement to improve prediction accuracy amidst noisy financial data.

Contribution

LARA's innovative combination of locality-aware attention and iterative label refinement enhances the detection of profitable trading signals in noisy financial markets.

Findings

LARA significantly outperforms existing ML methods on stocks, cryptocurrencies, and ETFs.

Extensive ablation studies confirm LARA's superior ability to identify reliable trading opportunities.

LARA effectively handles the low signal-to-noise ratio in financial data.

Abstract

Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) methods. Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention, enhanced by metric learning techniques, automatically extracts the potentially profitable samples through masked attention scheme and task-specific distance metrics. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies confirm LARA's superior ability in capturing more reliable trading opportunities.