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