Abstract
Stock price trend prediction is a challenging issue in the financial field. To get improvements in predictive performance, both data and technique are essential. The purpose of this paper is to compare deep learning model (LSTM) with two ensemble models (RF and XGboost) using multiple data. Data is gathered from four stocks of financial sector in China A-share market, and the accuracy and F1-measure are used as performance measure. The data of the past three days is applied to classify the rise and fall trend of price on the next day. The models’ performance are tested under different market styles (bull or bear market) and different market activities. The results indicate that under the same conditions, LSTM is the top algorithm followed by RF and XGBoost. For all models applied in this study, prediction performance in bull markets is much better than in bear markets, and the result in active period is better than inactive period by average. It is also found that adding data sources is not always effective in improving forecasting performance, and valuable data sources and proper processing may be more essential than providing a large quantity of data source.
Y. Xia and Y. Wang---Contributed equally to this work.
This work is supported by: Engineering Research Center of State Financial Security, Ministry of Education, Central University of Finance and Economics, Beijing, 102206, China; Program for Innovation Research in Central University of Finance and Economics.
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Xia, Y., Wang, Y. (2021). Predicting Stock Price Movement with Multiple Data Sources and Machine Learning Models. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_7
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