Prediction of the Change Points in Stock Markets Using DAE-LSTM

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Abstract

Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise accordingly. Based on this empirical result, this study seeks to predict the start date of industry uptrends using the Russell 2000 industry index. The proposed model in this study predicts future stock prices using a denoising autoencoder (DAE) long short-term memory (LSTM) model and predicts the existence and timing of future change points in stock prices through Pettitt’s test. The results of the empirical analysis confirmed that this proposed model can find the change points in stock prices within 7 days prior to the start date of actual uptrends in selected industries. This study contributes to predicting a change point through a combination of statistical and deep learning models, and the methodology developed in this study could be applied to various financial time series data for various purposes.

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2002)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.