Prediction of Hang Seng Index Based on Machine Learning

The stock market is a complex nonlinear dynamic system. Hong Kong is an open market and Hang Seng Index (HSI) reflects the Hong Kong stock market and the global economy. HSI daily frequency data from January 3, 2005 to July 5, 2019 was used in this paper. Then Decision Tree model and Random Forest model were chosen to predict the movements of HSI based on the highest price, the lowest price, the opening price, the closing price and the volumes. The results showed that Random Forest is more accurate. To some extent, this paper provides insight into investing Hong Kong stock market.

[1]  Shan Lu,et al.  Aggregating multiple types of complex data in stock market prediction: A model-independent framework , 2018, Knowl. Based Syst..

[2]  Terje Gobakken,et al.  A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data , 2018, Remote Sensing of Environment.

[3]  Achilleas Zapranis,et al.  Stock performance modeling using neural networks: A comparative study with regression models , 1994, Neural Networks.

[4]  Mu-Yen Chen,et al.  A hybrid fuzzy time series model based on granular computing for stock price forecasting , 2015, Inf. Sci..

[5]  T.B. Trafalis,et al.  Kernel principal component analysis and support vector machines for stock price prediction , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[6]  Zhi Xiao,et al.  A multiple support vector machine approach to stock index forecasting with mixed frequency sampling , 2017, Knowl. Based Syst..

[7]  Esmaeil Hadavandi,et al.  Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction , 2012, Knowl. Based Syst..

[8]  Jui-Chung Hung.  A Fuzzy Asymmetric GARCH model applied to stock markets , 2009, Inf. Sci..

[9]  Huanhuan Chen,et al.  Evolving Least Squares Support Vector Machines for Stock Market Trend Mining , 2009, IEEE Trans. Evol. Comput..

[10]  You-Shyang Chen,et al.  Modeling fitting-function-based fuzzy time series patterns for evolving stock index forecasting , 2014, Applied Intelligence.

[11]  Qing Cao,et al.  The three-factor model and artificial neural networks: predicting stock price movement in China , 2011, Ann. Oper. Res..

[12]  Pritpal Singh,et al.  Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization , 2014, Int. J. Approx. Reason..