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

2021/07/26 by Liang Zeng, Zeng, Liang, Lei Wang +9 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computational Engineering #Computer science #Econometrics #Economics #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Financial market #Forecasting Techniques and Applications #Locality #Machine Learning (cs.LG) #Machine learning #Metric (unit) #Set (abstract data type) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2107.11972

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2021/07/26 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

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.

Cited by

• From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
• FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

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