Afsharirad, E., Alavi, S.E., Sinaei, H. (2018). Developing an Intelligent Model to Predict Stock Trend Using the Technical Analysis. Financial Research Journal, 20 (2), 249-264.
Aggarwal, C. C. (2014). Data Classification: Algorithms and Applications. Minneapolis, Minnesota, U.S.A.: Chapman and Hall/ CRC.
Atsalakis, G., & Valavanis, K. A. (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Journal of Expert Systems with Applications, 36, 10696–10707.
Chambers, L. (2016). Daily stock movement’s prediction using and integrating three models Of analytical prediction of adaptive-fuzzy inductive inference system. artificial neural networks and supporting vector machines. London, Champan & Hall.
Fakhari, H. Valipour Khatir, M. & Mousavi, M. (2017). Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting. Financial Research Journal, 19 (2), 229-318. (in Persian)
Fallahpour, S., Golzarzi, GH. and Fatorehchian, N. (2013). Predicting the trend of stock prices using support vector machine based on genetic algorithm in Tehran Stock Exchange. Financial Research, 15 (2), 288-269. (in Persian)
Farid, D. M., Zhang, L., Rahman, C. M., Hossain, M. A., & Strachan R. (2014). Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks. Expert Systems with Applications, 41(4), 1937-1946.
Ford, N., Batchelor, B., & Wilkins, B. R. (1970). A learning scheme for the Nearest Neighbor Classifier. Information Sciences, 2 (2), 139-157.
George, S., Emmanouil, M and Constantinos D. (2011). ElliottWace Theory and neuro-fuzzy systems, in stock market prediction, the WASP system, Expert Systems with Applications, 38(8), 9196-9206.
Gholamian, Elham, Davoodi, Mohammad Reza. (2018). Predict price trends in the stock market using a random forest algorithm. Journal of Financial Engineering and Securities Management, No. 35 / summer. (in Persian)
Ismaili, M. (2012). Concepts and techniques of data mining. First Edition. Tehran: Niaz Danesh Publications. 20-30. (in Persian)
Khan, W., Malik, U., Mustansar, A.GH., Awais Azam, M. (2019). Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis, Soft Computing
, 24, 11019-11043. https://doi.org/10.1007/s00500-019-04347-y.
Larson, C. (1931). The shrinkage of the coefficient of multiple correlations. Journal of Educational Psychology, 22(1), 45–55.
Liu, C., Wang, J., Xiao, D., & Liang, Q. (2016). Forecasting S&P 500 Stock Index Using Statistical Learning Models. Open Journal of Statistics, 6 (06), 1067.
Masry, M. (2017). The Impact of Technical Analysis on Stock Returns in an Emerging Capital Markets (ECM¡¯s) Country: Theoretical and Empirical Study. International Journal of Economics and Finance, 9(3), 91-107.
Murphy, J. (1999). Technical Analysis in the Capital Market. New York Institute of Finance, 45-48.
Murphy, J. (2018). Technical Analysis in Capital Markets. (Kamyar Farhanifard and Reza Ghasemian Langroudi, Trans) (12th ed), Tehran: Chalesh Publications. (in Persian)
Peymany Foroushany, M., Erzae, M.H., Salehi, M., & Salehi, A. (2020). Trades Return Based on Candlestick Charts in Tehran Stock Exchange. Financial Research Journal, 22(1), 69-89. (in Persian)
Pourzamani, Z., Rezvani Aghdam, M. (2017). Comparison comparing the effectiveness of combined technical analysis strategies with buying and holding methods to buy stocks in uptrends and downtrends. Quarterly Journal of Financial Research in Securities Analysis, 10 (33). (in Persian)
Prechter, R. R. (2013). Elliot Wave Analysis. John Wiley & Sons.
Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI 2001. Workshop on empirical methods in artificial intelligence, 3(22), 41-46.
Roberto Cervelló-Royo, Francisco Guijarr. (2020). Forecasting stock market trend: a comparison of machine learning algorithms Finance, Markets and Valuation, 6, 37–49.
Shahrabadi, A., Bashiri, N. (2010). Investment Management in Tehran Stock Exchange, Stock Exchange and Securities Organization. (in Persian)
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistics Society, 36(2), 111-133.
Tabar, S. (2018). Stock Market Prediction Using Elliot Wave Theory and Classification. Faculty of the Graduate College at the University of Nebraska in Partial Fulfillment of Requirements. Degree of Doctor of Philosophy Major: Information Technology Omaha.
Tsaih, R., Hsu, Y. and Lai, C.C. (1998). Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems, 23(2), 161-174.
Wagner, L. D. (1979). Distribution-free performance bounds for potential function rules. IEEE Transactions in Information Theory, 601-604.
Wan, L.X. (2000). Eliot wave theory research. Shanghai: Fudan University.
Wang, L.X. (2012). An Empirical Analysis of Eliot Wave Theory in China's Futures Market, China’s Foreign Investment, 4, 253–254.
Yang, H., Chan, L., & King, I. (2002). Support vector machine regression for volatile stock market prediction. International Conference on Intelligent Data Engineering and Automated Learning. Manchester, UK.
Yu, G., & Wenjuan, G. (2010). Decision tree method in financial analysis of listed logistics companies. International conference on intelligent computation technology and automation.