| Abstract: |
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 in 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 the above
issues, we propose a novel framework-LARA(Locality-Aware Attention and
Adaptive Refined Labeling), which contains the following three components:
1)Locality-aware attention automatically extracts the potentially profitable
samples by attending to their label information in order to construct a more
accurate classifier on these selected samples. 2)Adaptive refined labeling
further iteratively refines the labels, alleviating the noise of samples.
3)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. To validate our method, we conduct
comprehensive experiments on three real-world financial markets: ETFs, the
China's A-share stock market, and the cryptocurrency market. LARA achieves
superior performance compared with the time-series analysis methods and a set
of machine learning based competitors on the Qlib platform. Extensive ablation
studies and experiments demonstrate that LARA indeed captures more reliable
trading opportunities. |