有效的股票状态分析和预测对于股票市场参与者能够提高收益和降低相关风险非常重要。然而,股票市场数据充满了噪声和随机性,使实现精确价格预测的任务艰巨。此外,价格预测的滞后现象使得相应的交易策略很难捕捉到转折点,导致较低的投资回报。为了解决这个问题,我们提出了基于回报自适应分段线性表示(RA-PLR)和批量注意多尺度卷积递归神经网络(Batch-MCRNN)的重要交易点(ITP)预测框架,以提高股票投资收益为出发点。首先,采用一种新的RA-PLR方法检测股票市场的历史ITP。然后,我们应用Batch-MCRNN模型来集成跨空间的数据信息,时间,和预测未来ITP的样本维度。最后,我们设计了一种结合了相对强弱指数(RSI)和双重检查(DC)方法的交易策略,以匹配ITP预测。我们对现实世界的数据集进行了关于预测准确性的几个最先进的基准模型的全面和系统的比较,风险,返回,其他指标。我们提出的方法在所有指标上都明显优于比较方法,对股票投资具有重要的参考价值。
Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.