Prediction of KOSPI 200 Index Movement Using Generative Adversarial Network Based on LSTM
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초록
Stock price prediction research has been actively underway since the past. Predicting the direction of the stock price movement is an attractive study that can be applied to actual investment. Even a little improvement in stock price performance can lead to large profits from actual investment. Nonetheless, predicting a random, volatile stock market poses many challenges. With the development of deep learning methodologies, there have been various attempts to apply them to predicting stock prices. This study aims to improve stock price direction prediction performance by using the Generative Adversarial Network (GAN) model, which is widely used in the image field, and the LSTM model, which is widely used for time series data prediction. The proposed LSTM-GAN model compared performance with that of benchmark models using the KOSPI 200 index, Korea’s representative stock index.
Abstract
Stock price prediction research has been actively underway since the past. Predicting the direction of the stock price movement is an attractive study that can be applied to actual investment. Even a little improvement in stock price performance can lead to large profits from actual investment. Nonetheless, predicting a random, volatile stock market poses many challenges. With the development of deep learning methodologies, there have been various attempts to apply them to predicting stock prices. This study aims to improve stock price direction prediction performance by using the Generative Adversarial Network (GAN) model, which is widely used in the image field, and the LSTM model, which is widely used for time series data prediction. The proposed LSTM-GAN model compared performance with that of benchmark models using the KOSPI 200 index, Korea’s representative stock index.
참고문헌 (11)
- Schwager JD, "Technical analysis" (1995), John Wiley & Sons
- Hochreiter S, "Long short-term memory", Neural Comput, 9, pp.1735~1780 (1997)
- Goodfellow I, "Generative adversarial nets", Adv Neur In, 3, pp.2672~2680 (2014)
- Nelson D, "Stock market’s price movement prediction with LSTM neural networks", Ieee Ijcnn, pp.1419~1426 (2017)
- Xu Y, "Stock movement prediction from tweets and historical prices", Comput Linguist, 1, pp.1970~1979 (2018)
- Soun YJ, "Accurate stock movement prediction with self-supervised learning from sparse noisy tweets", Ieee Big Data, pp.1691~1700 (2022)
- 이상규, "KOSPI200 Index Prediction Using Sequence-to-Sequence based on Denoising Filter and Attention Mechanism", Quantitative Bio-Science, 39(2), pp.127~135 (2020), 자연과학연구소[DOI]
- 양준혁, "Forecasting the KOSPI 200 Stock Index Based on LSTM Autoencoder", Quantitative Bio-Science, 39(2), pp.101~109 (2020), 자연과학연구소[DOI]
- 이동원, "KOSPI200 Prediction through Low-Pass Filtered Long Short-Term Memory Algorithm", Quantitative Bio-Science, 39(1), pp.25~31 (2020), 자연과학연구소[DOI]
- Zhou X, "Stock market prediction on high-frequency data using generative adversarial nets", Math Probl Eng, 2018 (2018)
- Zhang K, "Stock market prediction based on generative adversarial network", Procedia Comput Sci, 147, pp.400~406 (2019)