An Optimization of Moving Average Stock Price in Tehran Stock Exchange: Meta-heuristic approach Adaptive Improved Genetic Algorithm

Subject Areas : Journal of Investment Knowledge

Mahboobeh Asghartabar ledari ¹ (Master Of Science In Rahedanesh Institute Of Higher Education (Corresponding Author))
Ahmad Jafari Samimi ² (Professor Of Economics-Faculty Of Economics-Mazandaran University-Babolsar-Instructor Of Rahedanesh Institute Of Higher Education)

Received: 2017-02-13 Accepted : 2017-04-05 Published : 2018-03-21

Keywords: Prediction of Stock Price – Op, Moving Average – Tehran Stock , Adaptive Improved Genetic Algo,

Abstract :

Predict the stock price is an important topic in financial markets. Is commonly use of technical tools in this area and one of them most functional, are moving averages. The use of two moving averages, the most common method to predict trends, which is in need of two periods. The optimal lengths for both short-term and long-term period for each stock, according to a recent trend, they are different. Find the optimal lengths with traditional methods of costly and often do not reach the global optimal answer. The perfect solution are using of smart tools such as genetic algorithms. Genetic algorithm have been used in this study, is Adaptive Improved Genetic Algorithm that much faster finds a global optimal answer. In this study, data's of the selected companies in diverse industries in Tehran Stock Exchange from April 2011 to March 2016 have been evaluated. The results show, when the algorithm reaches the optimal time period, which its parameters are correctly set.  

References:

*       مورفی، جان (1999) ، تحلیل تکنیکال در بازار سرمایه، ترجمه: فراهانی فرد. کامیار، قاسمیان. رضا، چاپ دهم1393، انتشارات نشر چالش.

*       سلطان زالی، مسعود (1386) ، سودمندی استفاده از روشهای تحلیل تکنیکی در بورس اوراق بهادار تهران، فصلنامه بررسی های حسابداری و حسابرسی، 49، 91-110.

*       *     نجارزاده، رضا و گداری، اکبر (1387) ، بررسی سودآوری قواعد مبادلاتی میانگین متحرک در بورس اوراق بهادار تهران، فصلنامه      پژوهش های اقتصادی، 8، 43-58.   

*       صمدی، سعید و ایزدی نیا، ناصر و داورزاده، مهتاب (1389) ، کاربرد بهره گیری از تحلیل تکنیکی در بورس اوراق بهادار تهران (رویکردی بر میانگین متحرک) ، فصلنامه پیشرفت های حسابداری دانشگاه شیراز، 2، 154-121.   

*       نبوی چاشمی، علی و حسن زاده، آیت اله (1390) ، بررسی کارایی شاخص MA در تحلیل تکنیکال در پیش بینی قیمت سهام، مجله دانش مالی تحلیل اوراق بهادار، 10، 83-106.

*       علیرضا، مهدی (1393) ، مقدمه ای بر الگوریتم های ژنتیک و کاربردهای آن، چاپ ششم، انتشارات: ناقوس، صفحه 14-15.

*       Brock, W, J.Lakonishok and B.Lebaron. (1992) simple Technical Trading Rules and the Stochastic Properties        of stock Returns. Journal of finance.47 (5):1731-1764.

*       Marshall, B. R. & Cahan, R. H. (2005). Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient? Research in International Business and Finance, 19 (3): 384-398. 

*       Liu Xiaojia, An HZ, Wang L, " Performance of generated moving average strategies in natural gas futures prices at different time scales.", Journal of Natural Gas Science and Engineering 24 (2015) 337-345.

*       Kaufman. PA, "Guide to smarter trading-Perry Kaufman on market analysis." Tech Anal Stock Commode, 1995; 13.

*       D. E. Goldberg, "Genetic algorithm in search, optimization & machine learning." New York: Addison Wisely, 1989.

*       R.J. Bauer, G.E. Liepins, "Genetic algorithms and computerized trading strategies." in D.E. O'Leary, P.R

*       Watkins (Eds.), Expert Systems in Finance, Elsevier Science publishers, Amsterdam, The Netherlands, 1992.

*       S. Mahfud, G. Mani, "Financial forecasting using genetic algorithms", Journal of Applied Artificial Intelligence. 10(6) (1996) 543-565.

*       F. Allen, R. Karjalainen, "Using genetic algorithms to find technical trading rules." Journal of Financial Economics 51 (1999) 245-271.

*       Wang J, "Trading and hedging in S&P 500 spot and futures markets using genetic programming." J Futures Mark 2000; 20:911–42.

*       Ramon Lawrence, "Using Neural Networks to Forecast Stock Market Prices.", (1997)

*       R.J. Kuo, C.H. Chen, Y.C. Hwang, "An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network.", Fuzzy sets & systems 118 (2001) 21-45.

*       P.-C. Chang, C.-Y. Fan, and J.-L. Lin, "Trend discovery in financial time series data using a case based fuzzy decision tree", Expert Systems with Applications, vol. 38, no. 5, pp. 6070–6080, 2011.

*       W. L. Tung and C. Quek, "Financial volatility trading using a self-organizing neural-fuzzy semantic network and option straddle-based approach,” Expert Systems with Applications, vol. 38, no. 5, pp. 4668–4688, 2011.

*       Kapoor. V, Dey. S, Khurana. A.P, "Genetic algorithm: An Application to Technical Trading System Design.", International Journal of Computer Applications. (0975-8887) Volume 36– No.5, December 2011.

*       Liu Xiaojia, An HZ, Wang Y, Jia Xiaoliang, "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms.", Appl Energy (2016),    j.apenergy.01.04.