Gradient Boost with Convolution Neural Network for Stock Forecast

Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.

[1]  Gisele L. Pappa,et al.  From an artificial neural network to a stock market day-trading system: A case study on the BM&F BOVESPA , 2009, 2009 International Joint Conference on Neural Networks.

[2]  Erdinç Altay,et al.  Stock Market Forecasting: Artificial Neural Network and Linear Regression Comparison in An Emerging Market , 2006 .

[3]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[4]  Alexander Porshnev,et al.  Machine Learning in Prediction of Stock Market Indicators Based on Historical Data and Data from Twitter Sentiment Analysis , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[5]  Fei Chao,et al.  Feature Selection Inspired Classifier Ensemble Reduction , 2014, IEEE Transactions on Cybernetics.

[6]  Kyoung-jae Kim,et al.  Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting , 2004, Applied Intelligence.

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Chen Tian-lun,et al.  Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization , 2006 .

[9]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[10]  Huaiqing Wang,et al.  A Feature Fusion Based Forecasting Model for Financial Time Series , 2014, PloS one.

[11]  Ying Wah Teh,et al.  Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment , 2015, Expert Syst. Appl..

[12]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[13]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[14]  Ramon Lawrence,et al.  Using Neural Networks to Forecast Stock Market Prices , 2000 .

[15]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[16]  O. EmenikeKalu.  Forecasting Nigerian Stock Exchange Returns: Evidence from Autoregressive Integrated Moving Average (ARIMA) Model , 2010 .