China’s Stock Market Trend Prediction Model based on Adversarial Learning

[1] Wen, M., Li, P., Zhang, L., & Chen, Y. (2019). Stock market trend prediction using high-order information of time series. Ieee Access, 7, 28299-28308. Search in Google Scholar

[2] Zhou, F., Zhou, H. M., Yang, Z., & Yang, L. (2019). EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Systems with Applications, 115, 136-151. Search in Google Scholar

[3] Fama, E. F. (1970). Efficient market hypothesis: A review of theory and empirical work. Journal of Finance, 25(2), 28-30. Search in Google Scholar

[4] Mehtha, S. R. K., Pareek, P., & Mehtha, V. . (2021). Fundamental analysis of the three companies (srf limited, navin fluorine, suzlon energy) listed on stock market (bse, nse) with help of ratio analysis. Advances in computational sciences and technology(2), 14. Search in Google Scholar

[5] Sharma, N., & Mohan, B. R. (2022). Stock market prediction using historical stock prices and dependence on other companies in automotive sector. In 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 425-431). IEEE. Search in Google Scholar

[6] Rahman, T., Akhter, R., Lawal, K., Mazumder, S. A., Afroz, T., & Rahman, A. . (2021). Forcasting and Pattern Analysis of Dhaka Stock Market using LSTM and Phrophet Algorithm. Search in Google Scholar

[7] Xi-Jian, H. U. . (2007). Analysis and modelling of financial time series. Journal of Xinjiang University(Natural Science Edition). Search in Google Scholar

[8] Sathyabama, S., Stemina, S. C., SumithraDevi, T., & Yasini, N. (2021, May). Intelligent Monitoring and Forecasting Using Machine Learning Techniques. In Journal of Physics: Conference Series (Vol. 1916, No. 1, p. 012175). IOP Publishing. Search in Google Scholar

[9] Singh, D., & Gupta, B. K. (2022). Closing Price Prediction of Nifty Stock Using LSTM with Dense Network. In Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021 (pp. 382-392). Singapore: Springer Singapore. Search in Google Scholar

[10] Kanwal, A., Lau, M. F., Ng, S. P., Sim, K. Y., & Chandrasekaran, S. (2022). BiCuDNNLSTM-1dCNN—A hybrid deep learning-based predictive model for stock price prediction. Expert Systems with Applications, 202, 117123. Search in Google Scholar

[11] An, Z., & Feng, Z. (2021). A stock price forecasting method using autoregressive integrated moving average model and gated recurrent unit network. In 2021 International conference on big data analysis and computer science (BDACS) (pp. 31-34). IEEE. Search in Google Scholar

[12] Nazário, R. T. F., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126. Search in Google Scholar

[13] Besimi, A., Dika, Z., Shehu, V., & Selimi, M. (2019). Applied text-mining algorithms for Stock Price prediction based on financial news articles. Managing Global Transitions, 17(4), 335-351. Search in Google Scholar

[14] Stuefer, M. . (2014). Social Media Sentiment Analysis for Stock Price Behavior Prediction. Search in Google Scholar

[15] Guo, X., Zhao, Q., Zheng, D., Ning, Y., & Gao, Y. (2020). A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price. Energy Reports, 6, 1046-1053. Search in Google Scholar