Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

Shangkun Deng1, Xiaoru Huang1, Yingke Zhu1, Zhihao Su2, Zhe Fu3, Tatsuro Shimada4
1College of Economics and Management, China Three Gorges University, Yichang 443002, China
2School of Economics, Shandong University, Jinan 250100, China
3School of History, Beijing Normal University, Beijing 100875, China
4Graduate School of Science and Technology, Keio University, Yokohama 2238522, Japan

Tài liệu tham khảo

Baker, 2012, Global, local, and contagious investor sentiment, Journal of Financial Economics, 104, 272, 10.1016/j.jfineco.2011.11.002 Bollen, 2011, Twitter mood predicts the stock market, Journal of Computational Science SCI-NETH., 2, 1, 10.1016/j.jocs.2010.12.007 Chen, T., Guestrin, C., 2016. Xgboost: a scalable tree boosting system. In the 22nd ACM SIGKDD International Conference. 785-794. Chi, 2012, Investor sentiment in the Chinese stock market: An empirical analysis, Applied Economics Letters, 19, 345, 10.1080/13504851.2011.577003 Chung, 2012, When does investor sentiment predict stock returns?, Journal of Empirical Finance, 19, 217, 10.1016/j.jempfin.2012.01.002 Deng, 2021, A novel hybrid method for direction forecasting and trading of Apple Futures, Applied Soft Computing, 110, 10.1016/j.asoc.2021.107734 Deng, 2021, An intelligent system for insider trading identification in Chinese security market, Computational Economics, 57, 593, 10.1007/s10614-020-09970-8 Dergiades, 2012, Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy, Economic Letters, 116, 404, 10.1016/j.econlet.2012.04.018 Derrac, 2011, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, 1, 3, 10.1016/j.swevo.2011.02.002 Fan, 2018, Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China, Energy Conversion and Management, 164, 102, 10.1016/j.enconman.2018.02.087 Fang, 2021, A machine learning based asset pricing factor model comparison on anomaly portfolios, Economic Letters, 109919, 10.1016/j.econlet.2021.109919 Fisher, 2000, Investor sentiment and stock returns, Financial Analysts Journal, 56, 16, 10.2469/faj.v56.n2.2340 Frazzini, 2008, Dumb money: mutual fund flows and the cross-section of stock returns, Journal of Financial Economics, 88, 299, 10.1016/j.jfineco.2007.07.001 Gao, 2020, Institutional investor sentiment and aggregate stock returns, European Financial Management, 27, 899, 10.1111/eufm.12292 Han, 2017, Can investor sentiment be a momentum time-series predictor? Evidence from China, Journal of Empirical Finance, 42, 212, 10.1016/j.jempfin.2017.04.001 Kumar, 2006, Retail investor sentiment and return comovements, The Journal of Finance, 61, 2451, 10.1111/j.1540-6261.2006.01063.x Kwon, 2022, Predicting crowd funding success with visuals and speech in video ads and text ads, European Journal of Marketing, 56, 1610, 10.1108/EJM-01-2020-0029 Li, 2020, Stock index prediction based on wavelet transform and FCD-MLGRU, J. Forecasting, 39, 1229, 10.1002/for.2682 Li, 2019, Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm, Neural Computing and Applications, 32, 1971, 10.1007/s00521-019-04378-4 Lundberg, 2017, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems., 4765 Meng, 2020, Prediction of methane adsorption in shale: Classical models and machine learning based models, Fuel, 278, 10.1016/j.fuel.2020.118358 Ryu, 2017, Investor sentiment, trading behavior and stock returns, Applied Economics Letters, 24, 826, 10.1080/13504851.2016.1231890 Shapley, L.S., 2016. 17. A value for n-person games. In H. Kuhn & A. Tucker (Ed.), Contributions to the Theory of Games (AM-28), Volume II (pp. 307-318). Tanaka, 2016, Random forests-based early warning system for bank failures, Economics Letters, 148, 118, 10.1016/j.econlet.2016.09.024 Thomason, 1999, The practitioner methods and tool, Journal of Computational Finance, 7, 36