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 over 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 this issue, we propose a novel price movement forecasting framework, called Locality-Aware Attention and Iterative Refinement Labeling(LARA), which consists of two main components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to surrounding class-aware label information. Moreover, 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. 2)Iterative refinement labeling further iteratively refines the labels of noisy samples and then combines the learned predictors to be robust to the unseen and noisy samples. In a number of experiments on three real-world financial markets: ETFs, stocks, and cryptocurrencies, LARA achieves superior performance compared with the traditional time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments also demonstrate that LARA indeed captures more reliable trading opportunities.
翻译:价格变动预测旨在根据当前市场条件和其他相关信息预测金融资产的未来趋势。最近,机器学习方法在学术界和行业的价格变动预测方面越来越受欢迎,并取得了有希望的结果。大多数现有的市场流动解决方案将预测问题表述为整个培训数据组的分类(预测方向)或回归(预测回报)问题。然而,由于信号到噪音比率极低,金融数据具有随机性,良好的交易机会极为稀少。因此,没有认真选择潜在盈利的样本,这种市场学习方法就容易捕捉到传统市场噪音的模式,而不是真实信号。为了解决这一问题,我们提议一个新的价格变动预测框架,称为“地方-意识关注”和“迭代改善”(LARA),它由两个主要部分组成:(1) 地方-认知性关注自动提取潜在盈利的样本样本,通过关注周围的类别了解标签信息,从而获得潜在的盈利性样本。此外,根据计量学习技术,地方认知的注意得到了具体任务的远程测量,将时间分配到潜在盈利性样品的升级。我们提出了一个新的价格变化预测框架,将一个更精确的模型的升级到更精确的模型上。