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Abstract：
Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets\' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks\' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters\' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets\' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.
摘要：
对股票市场的准确预测对于股票市场的投资者和其他利益相关者制定有利可图的投资策略非常重要。即使有轻微的边际，预测模型准确性的提高也可以转化为可观的货币回报。然而,股市预测被认为是一个复杂的噪声研究问题，股票数据的复杂性和波动性。近年来,深度学习模型已经成功地为顺序数据提供了可靠的预测。我们通过将窥视孔LSTM与时间注意力层（TAL）相结合，提出了一种基于深度学习的混合分类模型，以准确预测股票市场的方向。包括美国在内的四个世界指数的每日数据，英国，中国和印度,从2005年到2022年，进行了检查。我们通过初步数据分析进行了全面评估，股市预测问题的特征提取和超参数优化。后窥视孔LSTM引入了TAL，以选择有关时间的相关信息并增强所提出模型的性能。将该模型的预测性能与基准模型CNN的预测性能进行了比较，LSTM,SVM和RF使用精度评估指标，精度,召回，F1分数，AUC-ROC,PR-AUC和MCC。实验结果表明，对于大多数评估指标和所有数据集，我们提出的模型的性能优于基准模型。该模型对英国和中国股市的准确率分别为96%和88%，对美国和印度股市的准确率为85%。因此，英国和中国的股市比美国和印度的股市更可预测。我们工作的重要发现包括注意层使窥视孔LSTM能够更好地识别股票市场数据中的长期依赖性和时间模式。可以根据我们提出的预测模型制定有利可图的及时交易策略。
