论文标题
使用机器学习和基于LSTM的深度学习模型的股票价格预测
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
论文作者
论文摘要
长期以来，对股票价格的预测一直是研究的重要领域。尽管有效的市场假设的支持者认为不可能准确预测股票价格，但有正式的主张表明，准确的建模和适当变量的设计可能会导致模型使用哪些股价和股票价格变动方式可以非常准确地预测。在这项工作中，我们提出了一种用于股票价格预测的混合建模方法，以建立不同的机器学习和基于深度学习的模型。 For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then,通过使用长期和短期记忆（LSTM）网络构建四个基于深度学习的回归模型，从而增强了我们的预测框架的预测能力，并采用了新颖的步行验证方法。我们利用四个不同的模型在其体系结构和输入数据的结构上利用LSTM回归模型的功能来预测未来的Nifty 50开放式值。所有回归模型的各种指标都会给出广泛的结果。结果清楚地表明，基于LSTM的单变量模型使用一周的先验数据作为预测Nifty 50时间序列的开放值的输入是最准确的模型。
Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.
