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.

翻译：价格走势预测旨在基于当前市场信息预测金融资产趋势，通过机器学习方法已取得显著进展。然而，现有大多数机器学习方法受限于金融数据极低的信噪比与随机性，常因未仔细筛选潜在盈利样本而将噪声误判为真实交易信号。针对此问题，本文提出LARA——一种包含两个核心组件的新型价格走势预测框架：局部感知注意力与迭代优化标注。(1) 局部感知注意力通过度量学习技术增强，借助掩码注意力机制与任务特定距离度量自动提取潜在盈利样本。(2) 迭代优化标注进一步对潜在盈利样本的噪声标签进行迭代优化，并结合对未知噪声样本具有鲁棒性的学习预测器。在股票、加密货币和ETF三个真实金融市场数据集上的实验表明，LARA在Qlib量化投资平台上显著优于多种基于机器学习的方法。大量消融实验证实了LARA在捕捉更可靠交易机会方面的卓越能力。