ABSTRACT
The stock1 market is dynamic, noisy and hard to predict. In this paper, we explored four machine learning models using technical indicators as input features to predict the price trend 30 days later. The experimental dataset is Shanghai Stock Exchange(SSE) 50 index stocks. The result demonstrates that ANN performs better than the other three models and is promising to find some profitable patterns.
- Bisoi, R. and P.K. Dash. 2014. A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter. Applied Soft Computing, 19, 41--56.Google ScholarCross Ref
- Chang, T.S. 2011. A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Systems with Applications, 38(12), 14846--14851. Google ScholarDigital Library
- Chen, R.Y. and B. Pan. 2016. Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition. Mathematical Problems in Engineering, 2016.Google Scholar
- Cocianu, C.L. and H. Grigoryan. 2016. Machine Learning Techniques for Stock Market Prediction. A Case Study of Omv Petrom. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 63--82.Google Scholar
- Fama, E. 1970. Efficient market hypothesis: A Review of Theory and Empirical Work, 1970.Google Scholar
- Adebiyi, A.A., A.O. Adewumi, and C.K. Ayo. 2014. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics.Google ScholarCross Ref
- Akita, R., et al. 2016. Deep learning for stock prediction using numerical and textual information. in Ieee/acis International Conference on Computer and Information Science.Google Scholar
- Singh, R. and S. Srivastava. 2017. Stock prediction using deep learning. Multimedia Tools and Applications, 76(18), 18569--18584. Google ScholarDigital Library
- Sugumar, R., A. Rengarajan, and C. Jayakumar. 2014. A Technique to Stock Market Prediction Using Fuzzy Clustering and Artificial Neural Networks. Computing and Informatics, 33(5), 992--1024.Google Scholar
- Lahmiri, S. 2014. Entropy-Based Technical Analysis Indicators Selection for International Stock Markets Fluctuations Prediction Using Support Vector Machines. Fluctuation and Noise Letters, 13(2).Google Scholar
- Schumaker, R.P., et al. 2017. Prediction from regional angst - A study of NFL sentiment in Twitter using technical stock market charting. Decision Support Systems, 98, 80--88. Google ScholarDigital Library
- Ballings, M., et al. 2015. Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046--7056. Google ScholarDigital Library
- Shynkevich, Y., et al. 2017. Forecasting Price Movements using Technical Indicators: Investigating the Impact of Varying Input Window Length. Neurocomputing.Google Scholar
- Dong, G., K. Fataliyev, and L. Wang. 2014. One-step and multi-step ahead stock prediction using backpropagation neural networks. in Communications and Signal Processing.Google Scholar
- Somani, Poonam, Shreyas Talele, and Suraj Sawant. 2014. Stock market prediction using hidden Markov model. Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International. IEEE.Google ScholarCross Ref
Index Terms
- Predicting Chinese Stock Market Price Trend Using Machine Learning Approach
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