Investigating the Predictability of Starting Point and Ending Short-Term Trend of Stock Price Using the Bayesian Likelihood Network

Document Type : Research Paper

Authors

1 Department of Financial Management, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran

2 Industrial Engineering Department, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran

3 Industrial Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran

4 Associate Professor, AlZahra University

Abstract

The main objective of this research is to investigate the predictability of the starting points (floor) and the end (roof) of the short-term stock price trend using the Naibouz model for providing a decision support system. In this research, two variables including calendar and technical variables for modeling were used. The results of this study showed that the model used to identify and predict the end points (roof) in the stock price chart has a better performance than the other, and also the accuracy of the model used in the thirty of the industry survey has discrepancy differences. This is a generalization of the results of this research and the use of the Naibouz model to predict the points in the various industries. We can confidently comment on the turbulence behavior of the starting and ending points in the stock price chart, but we need to pay careful attention to probable behavior.

Keywords

References

 
-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).

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History

  • Receive Date: 05 November 2018
  • Revise Date: 01 December 2019
  • Accept Date: 15 March 2020

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