Predicting stock market indexes with world news

With the rapid development of economy, people need to raise the accuracy of predicting prices considering late events. The main tackle in raising the accuracy is to fully use the information in daily reports. Unfortunately, most of the current solutions separate the time series statistics, the events reported in text data and prediction in data mining way from each other. As a result, although they predict the prices accurately in most of the days, they can not grasp the sudden change points of those time series. In this paper, we propose an ensemble framework to take advantage of the news text in predicting change points in stock market indexes as well as traditional prediction works, so that we can improve our prediction sufficiently. Our extensive experimental results shows that we reduce the loss of error predictions and enhance the good prediction results.

[1]  A. G. Mouhaffel,et al.  Using Multiple Regression Analysis Lineal to Predict Occupation Market Work in Occupational Hazard Prevention Services , 2017 .

[2]  Subhash Panwar,et al.  Forecasting Stock Market Movements Using Various Kernel Functions in Support Vector Machine , 2016 .

[3]  Zhiping Lin,et al.  Predicting time series with wavelet packet neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[4]  M. Vohland,et al.  Downscaling land surface temperatures at regional scales with random forest regression , 2016 .

[5]  Thomas Dimpfl,et al.  Can Internet Search Queries Help to Predict Stock Market Volatility? , 2016 .

[6]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[7]  Lakshminarayanan Subramanian,et al.  Predicting Socio-Economic Indicators using News Events , 2016, KDD.

[8]  Sihem Amer-Yahia,et al.  Health Monitoring on Social Media over Time , 2016, IEEE Transactions on Knowledge and Data Engineering.

[9]  Ming Zhu,et al.  Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[10]  Akhter Mohiuddin Rather,et al.  Stock market prediction and Portfolio selection models: a survey , 2017, Quarterly Journal of the Operational Research Society of India (OPSEARCH).

[11]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[12]  Evangelos Kanoulas,et al.  Dynamic Clustering of Streaming Short Documents , 2016, KDD.

[14]  Snehashis Roy,et al.  Random forest regression for magnetic resonance image synthesis , 2017, Medical Image Anal..

[15]  Qing Li,et al.  Exploiting Social Relations and Sentiment for Stock Prediction , 2014, EMNLP.

[16]  Jui-Feng Yeh,et al.  Topic detection and tracking for conversational content by using conceptual dynamic latent Dirichlet allocation , 2016, Neurocomputing.

[17]  Gyözö Gidófalvi.  Using News Articles to Predict Stock Price Movements , 2001 .

[18]  Yu Fang,et al.  Improving the genetic-algorithm-optimized wavelet neural network for stock market prediction , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[19]  In-Young Ko,et al.  Predictive Power of Public Emotions as Extracted from Daily News Articles on the Movements of Stock Market Indices , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[20]  Lipo Wang,et al.  Stock Forecasting with Feedforward Neural Networks and Gradual Data Sub-Sampling , 2010, Aust. J. Intell. Inf. Process. Syst..

[21]  Philip S. Yu,et al.  Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing , 2017, Proc. VLDB Endow..