Abstract
With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.
Highlights
With recent advances in computing technology, massive amounts of data and information are being constantly accumulated
This study proposes a hybrid model that integrates long short-term memory (LSTM) network with a genetic algorithm (GA) to search for a suitable model for prediction of the next-day index of the stock market
The prediction of the stock market can generate an actual financial loss or gain, so it is practically important to enhance the predictability of models
Summary
With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Various machine learning techniques, including artificial neural network (ANN) and support vector machine (SVM), that can reflect nonlinearity and complex characteristics of financial time series, have started being applied to stock market prediction. These approaches have provided prominent skills in predicting the chaotic environments of stock markets by capturing their nonlinear and unstructured nature [6,7]. The neural network models have a highly complex computational process, which can achieve a prominent solution for the target problem to be solved They are not able to provide specific explanations for their prediction results.
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