  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 multi-layer 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.
