Predicting Chinese Stock Market Price Trend Using Machine Learning Approach-Reference-Cited by-同舟云学术

Predicting Chinese Stock Market Price Trend Using Machine Learning Approach

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Computer Science and Application Engineering - CSAE '18
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Author:

Zhang Chongyang¹,Ji Zhi¹,Zhang Jixiang¹,Wang Yanqing¹,Zhao Xingzhi²,Yang Yiping¹

Affiliation:

1. Institution of Automation, Chinese Academy of Sciences, Beijing, China

2. The Governor's Academy, Massachusetts, United States of America

Publisher

ACM Press

Reference15 articles.

1. 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.

2. 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.

3. 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.

4. 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.

5. Fama, E. 1970. Efficient market hypothesis: A Review of Theory and Empirical Work, 1970.

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