摘要
股票市场是一个高噪音的混沌系统,其外部属性之间的相关性问题以及在长期预测时外部影响对股价波动的加剧,导致对股票市场进行准确预测是一项富有挑战性的工作。为解决上述问题,本文利用基于注意力机制的双向长短期记忆神经网络(BiLSTM)对香港地区恒生指数收盘价进行有效性的实证检验。其中,空间注意力机制用于捕捉输入指标之间的相关性并为其赋予区别权重,时间注意力机制用于描述数据的时间相关性以解决长期预测中的时间依赖问题并为时间步赋予区别权重,BiLSTM神经网络用于拟合数据并构建预测模型。本文还比较了四种基于注意力机制的神经网络方法和六种基线方法,实验结果表明,与当下流行的股票指数预测方法相比,基于双维度注意力机制的BiLSTM可以在短、中、长期预测中均实现更准确的股票指数收盘价预测。
In the environment of increasing volatility in financial markets and international capital flows,the accuracy and robustness of forecasts are key factors in financial decision-making.Predicting stock price indices has been an active area of research.Among them,many studies use data mining techniques,including artificial neural networks.However,most studies have shown that artificial neural networks have certain limitations in terms of learning patterns,because stock market data has huge noise and complex dimensions,correlation problems between its external properties,and external influences in long-term forecasts can lead to increased stock price volatility.Artificial neural networks have excellent learning capabilities,but they are often faced with inconsistent and unpredictable noisy data.In addition,sometimes the amount of data is too large,and the learning of patterns may not work well.In addition,in long-term forecasting,the redundancy of features and the complexity of the model cause the forecasting model to be unable to accurately extract the price and time change relationship.The presence of continuous data and large amounts of data poses serious problems for extracting valid information from raw data.Reduction and transformation of uncorrelated or redundant features can reduce uptime and produce universal results.To solve the above problems,this article uses a bidirectional long short-term memory neural network(BiLSTM)based on attention mechanism to empirically test the effectiveness of the closing price of the Hang Seng Index in Hong Kong.The data used in this article comes from the Ruisi Financial Database,and the data interval is selected from all trading data with daily trading volume data available until August 3,2020,and predicts the closing price of the stock index for 1 day(next day),7 days,30 days,60 days,and 120 days respectively.Among them,spatial attention mechanism is used to capture the correlation between input indicators and assign different weights to them,and temporal attention mechanis
作者
杨蓦
王静
YANG Mo;WANG Jing(College of Economics and Management,Northwest A&F University,Yangling 712100,China)
出处
《运筹与管理》
CSCD
北大核心
2023年第8期174-180,共7页
Operations Research and Management Science
基金
国家自然科学基金资助项目(71873101)。
