Study of Stock Return Predictions Using Recurrent Neural Networks with LSTM
Study of Stock Return Predictions Using Recurrent Neural Networks with LSTM
复制标题
使用带有 LSTM 的循环神经网络进行股票收益预测的研究
DOI：
10.1007/978-3-030-20257-6_39
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
B. Mohan
中科院分区：
文献类型：
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作者：
Nagaraj Naik;B. Mohan
股票价格收益预测对于日间交易者来说是一项具有挑战性的任务，以获得更多的收益。以往的文献大多集中在机器学习算法来预测股票收益上。本文研究了具有长短期记忆(LSTM)的递归神经网络(RNN)对股票未来收益的预测。它有能力保持对历史股票收益的记忆，以预测未来的股票收益输出。带LSTM的RNN用于存储最新的股票信息，而不是旧的相关股票信息。我们已经考虑了RNN层中的反复辍学，以避免在模型中过度拟合。为了完成这项任务，我们根据股票收盘价计算了股票回报。这些股票收益被作为递归神经网络的输入。预测模型的目标函数是使模型中的误差最小。为了进行实验，从印度国家证券交易所(NSE)收集了数据。与前馈人工神经网络相比，具有LSTM模型的RNN具有更好的性能。
Stock price returns forecasting is challenging task for day traders to yield more returns. In the past, most of the literature was focused on machine learning algorithm to predict the stock returns. In this work, the recurrent neural network (RNN) with long short term memory (LSTM) is studied to forecast future stock returns. It has the ability to keep the memory of historical stock returns in order to forecast future stock return output. RNN with LSTM is used to store recent stock information than old related stock information. We have considered a recurrent dropout in RNN layers to avoid overfitting in the model. To accomplish the task we have calculated stock return based on stock closing prices. These stock returns are given as input to the recurrent neural network. The objective function of the prediction model is to minimize the error in the model. To conduct the experiment, data is collected from the National Stock Exchange, India (NSE). The proposed RNN with LSTM model outperforms compared to an feed forward artificial neural network.