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Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction

Title
Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction
Authors
Chung, HyejungShin, Kyung-shik
Ewha Authors
신경식
SCOPUS Author ID
신경식scopus
Issue Date
2018
Journal Title
SUSTAINABILITY
ISSN
2071-1050JCR Link
Citation
SUSTAINABILITY vol. 10, no. 10
Keywords
long short-term memoryrecurrent neural networkgenetic algorithmdeep learningstock market prediction
Publisher
MDPI
Indexed
SCIE; SSCI; SCOPUS WOS scopus
Document Type
Article
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.
DOI
10.3390/su10103765
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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