-ALai, R. K. Fan, C. Y. Huang, W. H. & Chang, P. C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36(2), 3761-3773.
-Aitken, Alexander Craig (1957) Statistical Mathematics 8th Edition. Oliver & Boyd. ISBN 9780050013007 (Page 95)
-Al-Hmouz, R. Pedrycz, W. & Balamash, A. (2015). Description and prediction of time series: a general framework of granular computing. Expert Systems with Applications, 42(10), 4830-4839.
-Alvarez-Ramirez, J. Fernandez-Anaya, G. & Ibarra-Valdez, C. (2004). Some issues on the stability of trading based on technical analysis. Physica A: Statistical Mechanics and its Applications, 337(3), 609-624.
Appel, G. (2005). Technical analysis: power tools for active investors. FT Press.
-AraúJo, R. D. A. & Ferreira, T. A. (2013). A morphological-rank-linear evolutionary method for stock market prediction. Information Sciences, 237, 3-17.
-Atsalakis, G. S. & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932-5941.
-Baker, D. N. Lambert, J. R. & McKinion, J. M. (1983). GOSSYM: a simulator of cotton crop growth and yield. South Carolina. Agricultural Experiment Station. Technical bulletin (USA).
-Ballings, M. Van den Poel, D. Hespeels, N. & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056.
-Barak, S. Dahooie, J. H. & Tichý, T. (2015). Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Systems with Applications, 42(23), 9221-9235.
-Bollerslev, T. Marrone, J. Xu, L. & Zhou, H. (2014). Stock return predictability and variance risk premia: statistical inference and international evidence. Journal of Financial and Quantitative Analysis, 49(3), 633-661.
-Booth, E. Mount, J. & Viers, J. H. (2006). Hydrologic variability of the Cosumnes River floodplain. San Francisco Estuary and Watershed Science, 4(2).
-Chang, P. C. (2012). A novel model by evolving partially connected neural network for stock price trend forecasting. Expert Systems with Applications, 39(1), 611-620.
-Chen, Y. S. Cheng, C. H. Chiu, C. L. & Huang, S. T. (2016). A study of ANFIS-based multi-factor time series models for forecasting stock index. Applied Intelligence, 45(2), 277-292.
-Chong, E. Han, C. & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
-Chou, Y. L. (1963). Statistical analysis. Holt, Rinehart & Winston.
-Choudhry, R. & Garg, K. (2008). A hybrid machine learning system for stock market forecasting. World Academy of Science, Engineering and Technology, 39(3), 315-318.
-Cruz-Ramírez, N. Acosta-Mesa, H. G. Carrillo-Calvet, H. Nava-Fernández, L. A. & Barrientos-Martínez, R. E. (2007). Diagnosis of breast cancer using Bayesian networks: A case study. Computers in Biology and Medicine, 37(11), 1553-1564.
-Dan Wu (2006)Maximal Prime Subgraph Decomposition Of Bayesian Networks: a Relational Database perspective.ELSEVIER.16November 2006
-David Allen,Adnan DarwicheRc_Link (2007): Genetic Linkage Analysis using Bayesiannetworks.ELSEVIER. 3october2007
-Davies, P. E. (2007). Bayesian Decision Networks for Management of High Conservation Assets. Report to the Conservation of Freshwater Ecosystem Values Project. Report 6 of 6. Water Resources Division, Department of Primary Industries and Water, Hobart, Tasmania.
-Deng, S. & Sakurai, A. (2013, March). Foreign exchange trading rules using a single technical indicator from multiple timeframes. In Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on (pp. 207-212). IEEE
-Dupieux, P. Alard, J. P. Augerat, J. Babinet, R. Bastid, N. Brochard, F. ... & Fraysse, L. (1988). Proton-proton correlations at small relative momentum in neon-nucleus collisions at E/A= 400 and 800 MeV. Physics Letters B, 200(1-2), 17-21.
-Fama, E. F. Fisher, L. Jensen, M. C. & Roll, R. (1969). The adjustment of stock prices to new information. International economic review, 10(1), 1-21.
-Fenghua, W. E. N. Jihong, X. I. A. O. Zhifang, H. E. & Xu, G. O. N. G. (2014). Stock price prediction based on SSA and SVM. Procedia Computer Science, 31, 625-631.
-Fletcher, Robert H. Fletcher; Suzanne W. (2005). Clinical epidemiology: the essentials (4th ed.). Baltimore, Md.: Lippincott Williams & Wilkins. p. 45. ISBN 0-7817-5215-9.
-Goodwin, P. Önkal-Atay, D. Thomson, M. E. Pollock, A. C. & Macaulay, A. (2004). Feedback-labelling synergies in judgmental stock price forecasting. Decision Support Systems, 37(1), 175-186.
-Goubanova, O. & King, S. (2008). Bayesian networks for phone duration prediction. Speech communication, 50(4), 301-311.
-Hadavandi, E. Shavandi, H. & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), 800-808.
-Hafezi, R. Shahrabi, J. & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196-210.
-Hamzaçebi, C. Akay, D. & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications, 36(2), 3839-3844.
-hang, Z. Y. Shi, C. Zhang, S. L. & Shi, Z. Z. (2006). Stock time series forecasting using support vector machines employing analyst recommendations. Advances in Neural Networks-ISNN 2006, 452-457.
-Hsieh, D. A. (1991). Chaos and nonlinear dynamics: application to financial markets. The journal of finance, 46(5), 1839-1877.
-Hsu, C. M. (2011). A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Systems with Applications, 38(11), 14026-14036.
-Huang, C. J. Yang, D. X. & Chuang, Y. T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870-2878.
-Huang, C. J. Yang, D. X. & Chuang, Y. T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870-2878.
-Huang, W. Nakamori, Y. & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
-Huarng, K. & Yu, H. K. (2005). A type 2 fuzzy time series model for stock index forecasting. Physica A: Statistical Mechanics and its Applications, 353, 445-462.
-Kara, Y. Boyacioglu, M. A. & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
-Kim, K. J. & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2), 125-132.
-Kim, Y. Ahn, W. Oh, K. J. & Enke, D. (2017). An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms. Applied Soft Computing, 55, 127-140.
-Kocadağlı, O. (2015). A novel hybrid learning algorithm for full Bayesian approach of artificial neural networks. Applied Soft Computing, 35, 52-65.
-Kuo, R. J. Chen, C. H. & Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy sets and systems, 118(1), 21-45.
-Lahmiri, S. (2016). Intraday stock price forecasting based on variational mode decomposition. Journal of Computational Science, 12, 23-27.
-Malkiel, B. G. (2003). The efficient market hypothesis and its critics. The Journal of Economic Perspectives, 17(1), 59-82.
-Mlynarski, J. J. (2012). Selective fuel hedging in aviation based on trend lines and the fast stochastic oscillator (Doctoral dissertation, Purdue University).
-Murphy, J. J. (2009). The visual investor: how to spot market trends (Vol. 443). John Wiley & Sons
-Myers, Jerome L. Well, Arnold D. (2003). Research Design and Statistical Analysis (2nd ed.). Lawrence Erlbaum. p. 508. ISBN 0-8058-4037-0.
-Nassim, N. T. (2007). The black swan: the impact of the highly improbable. NY: Random House.
Nayak, A. Pai, M. M. & Pai, R. M. (2016). Prediction Models for Indian Stock Market. Procedia Computer Science, 89, 441-449.
-Nayak, S. C. Misra, B. B. & Behera, H. S. (2015). Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Engineering Journal.
-Neil, M. Tailor, M. Marquez, D. Fenton, N. & Hearty, P. (2008). Modelling dependable systems using hybrid Bayesian networks. Reliability Engineering & System Safety, 93(7), 933-939.
-Nelsen, R.B. (2001) [1994], "Kendall tau metric", in Hazewinkel, Michiel, Encyclopedia of athematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 978-1-55608-010-4
-Oh, K. J. & Kim, K. J. (2002). Analyzing stock market tick data using piecewise nonlinear model. Expert Systems with Applications, 22(3), 249-255.
-Olson, D. L. & Delen, D. (2008). Advanced data mining techniques. Springer Science & Business Media.
-Olson, D. & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465.
-Patel, J. Shah, S. Thakkar, P. & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
-Pavón, R. Díaz, F. & Luzón, V. (2008). A model for parameter setting based on Bayesian networks. Engineering Applications of Artificial Intelligence, 21(1), 14-25.
-Phan, D. H. B. Sharma, S. S. & Narayan, P. K. (2015). Stock return forecasting: some new evidence. International Review of Financial Analysis, 40, 38-51.
-Ruggeri, F. Kenett, R. S. & Faltin, F. W. (Eds). (2007). Encyclopedia of statistics in quality and reliability.
-Shynkevich, Y. McGinnity, T. M. Coleman, S. A. & Belatreche, A. (2016). Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems, 85, 74-83.
-Shynkevich, Y. McGinnity, T. M. Coleman, S. Belatreche, A. & Li, Y. (2017). Forecasting price movements using technical indicators: investigating the impact of varying input window length. Neurocomputing.
-Skabar, A. & Cloete, I. (2002). Neural networks, financial trading and the efficient markets hypothesis. Australian Computer Science Communications, 24(1), 241-249.
- Smeeton, N. C. (1985). Early history of the kappa statistic. Biometrics, 41, 795.
-Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote sensing of Environment, 62(1), 77-89
-Sun, L. & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research, 180(2), 738-753.
-Tan, L. Wang, S. & Wang, K. (2017). A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting. Information Processing Letters, 127, 32-36
-Tang, C. F. & Lean, H. H. (2007). Is the Phillips curve stable for Malaysia? New empirical evidence. Malaysian Journal of Economic Studies, 44(2), 95.
-Taylor, John Robert (1999). An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books. pp. 128–129. ISBN 0-935702-75-X.
-Thomsett, M. C. (2010). GLOBAL SUPPLY CHAIN RISK MANAGEMENT: VIEWING THE PAST TO MANAGE TODAY ‘S RISKS FROM AN HISTORICAL PERSPECTIVE. nternational Handbook of Academic Research and Teaching, 49
-Timmermann, A. & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of forecasting, 20(1), 15-27.
-Titterington, D. M. Murray, G. D. Murray, L. S. Spiegelhalter, D. J. Skene, A. M. Habbema, J. D. F. & Gelpke, G. J. (1981). Comparison of discrimination techniques applied to a complex data set of head injured patients. Journal of the Royal Statistical Society. Series A (General), 145-175.
-Tobback, E. Moeyersoms, J. Stankova, M. & Martens, D. (2016). Bankruptcy prediction for SMEs using relational data (No. 2016004).
-Tsang, P. M. Kwok, P. Choy, S. O. Kwan, R. Ng, S. C. Mak, J. ... & Wong, T. L. (2007). Design and implementation of NN5 for Hong Kong stock price forecasting. Engineering Applications of Artificial Intelligence, 20(4), 453-461.
-Wang, Y. F. (2003). Mining stock price using fuzzy rough set system. Expert Systems with Applications, 24(1), 13-23.
-Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
-Wiles, P. & Enke, D. (2015, January). A hybrid neuro-fuzzy model to forecast the soybean complex. In Proceedings of the International Annual Conference of the American Society for Engineering Management. (p. 1). American Society for Engineering Management (ASEM).
-Yeh, C. Y. Huang, C. W. & Lee, S. J. (2011). A multiple-kernel support vector regression approach for stock market price forecasting. Expert Systems with Applications, 38(3), 2177-2186.
-Zhang, X. Hu, Y. Xie, K. Zhang, W. Su, L. & Liu, M. (2015). An evolutionary trend reversion model for stock trading rule discovery. Knowledge-Based Systems, 79, 27-35.
-Zuo, Y. & Kita, E. (2012). Stock price forecast using Bayesian network. Expert Systems with Applications, 39(8).