Forecasting significant stock price changes using neural networks

Kamalov, Firuz
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Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multilayer perceptron, convolutional net, and long short-term memory net. As benchmark models, we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
This article is not available at CUD collection. The version of scholarly record of this article is published in Neural Computing and Applications (2020), available online at:
CNN, LSTM, Neural networks, RSI, Stock price forecasting, Convolutional neural networks, Decision trees, Financial markets, Fintech, Forecasting, Image recognition, Learning algorithms, Machine learning, Multilayer neural networks, Speech recognition, Benchmark models, High degree of accuracy, Neural network classifier, Neural network model, Public company, Relative strength index, Research topics, Stock price prediction, Electronic trading
Kamalov, F. (2020). Forecasting significant stock price changes using neural networks. Neural Computing and Applications.