ارائه مدل معاملاتی با فراوانی زیاد، همراه با مدیریت پویای سبد سهام به روش یادگیری تقویتی در بورس اوراق بهادار تهران

بهلولی خدادادی، محمد (1391). مدیریت پویای سبد سهام با استفاده از یادگیری تقویتی. پایان‎نامه کارشناسی ارشد، تهران: دانشکده علوم اقتصادی.

راعی، رضا؛ باجلان، سعید (1387). شناسایی و مدل‎سازی اثرات تقویمی بورس اوراق بهادار تهران با استفاده از مدل های ARCH و GARCH. فصلنامه پژوهش‎های اقتصادی (رشد و توسعه پایدار)، 8(4)، 21-47.

رزمی، جعفر؛ جولای، فریبرز؛ امامی، امیرعباس (1386). یک رویکرد «بوت استرپ» برای مقایسه سودآوری شاخص‌های تحلیل تکنیکی ـ بورس اوراق بهادار تهران. تحقیقات اقتصادی، 42(4)، 1-26.

ستایش، محمدرضا؛ تقی‎زاده شیاده، تیمور؛ پورموسی، علی اکبر؛ ابوذری لطف، علی. (1388). امکان‎سنجی به کارگیری شاخص‎های تحلیل تکنیکی ـ فنی ـ در پیش‎بینی روند قیمت سهام در بورس اوراق بهادار تهران. فصلنامه بصیرت، 42 (1)، 155-177.

صمدی، سعید؛ ایزدی نیا، ناصر؛ داورزاده، مهتاب (1389). کاربرد بهره‌گیری از تحلیل تکنیکی در بورس اوراق بهادار تهران (رویکردی بر میانگین متحرک). پیشرفت‌های حسابداری، 2(1)، 121-154.

محمدى، شاپور (1383). تحلیل تکنیکی در بورس اوراق بهادارتهران. فصلنامه تحقیقات مالی، 6(1)، 97- 129.

References

Bohluli Khodadadi, M. (2010). Dynamic portfolio management using reinforcement learning. Master's Thesis.University of Economic Sciences, Tehran. (in Persian)

De la Fuente, D., Garrido, A., Laviada, J., & Gómez, A. (2006). Genetic algorithms to optimise the time to make stock market investment. InProceedings of the 8th annual conference on Genetic and evolutionary computation, 1857-1858. ACM.

Dempster M.A.H. & Romahi Y. (2002). Intraday FX Trading: An Evolutionary Reinforcement Learning Approach. In: Yin H., Allinson N., Freeman R., Keane J., Hubbard S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg

Dempster, M. A. H. & Jones, C. M. (2002). Can channel pattern trading be successfully automated? The European Journal of Finance, 8 (3), 275-301.

Dempster, M. A. H., & Jones, C. M. (2000). The profitability of intra-day FX trading using technical indicators. Judge Institute of Management, University of Cambridge.

Dempster, M. A. H., Payne, T. W., Romahi, Y., & Thompson, G. W. P. (2001). Computational learning techniques for intraday FX trading using popular technical indicators. IEEE Transactions on Neural Networks, 12(4), 744-754.

Duda, R. O., Hard, P. E. & Stork, D. G. (2000). Pattern Classification. New York, Wiley-Interscience.

Duvinage, M., Mazza, P., & Petitjean, M. (2013). The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks. Quantitative Finance, 13(7), 1059-1070.

Fan, A. & Palaniswami, M. (2001). Stock selection using support vector machines. Proceedings of the International Joint Conference on Neural Networks, 3, 1793-1798.

Gao, X. & Chan, L. (2000). An algorithm for trading and Portfolio Optimization using Q-Learning and Sharp Ration Maximization. Proceedings of the international conference on neural information processing, 832-837.

Jangmin, O., Lee, J. W., Lee, J., & Zhang, B. T. (2004). Dynamic asset allocation exploiting predictors in reinforcement learning framework. In European Conference on Machine Learning, Springer Berlin Heidelberg, 298-309.

Jangmin, O., Lee, J., Lee, J. W. & Zhang, B. (2005). Dynamic Asset Allocation for Stock Trading Optimized by Evolutionary Computation. IEICE Transactions on Information and Systems, 88 (6), 1217-1223.

Jangmin, O., Lee, J., Lee, J. W. & Zhang, B. T. (2006). Adaptive stock trading with dynamic asset allocation using reinforcement learning. Information Sciences, 176, 2121-2147.

Jones, C. M. (1999). Automated technical foreign exchange trading with high frequency data. Doctoral dissertation, University of Cambridge.

Kendall, S. M., & Ord, K. (1997). Time Series. New York, Oxford.

Lee, J. W., & Zhang, B. T. (2002). Stock trading system using reinforcement learning with cooperative agents. In Proceedings of the Nineteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc, 451-458.

Lee, J. W., Park, J., Jangmin, O., Lee, J., & Hong, E. (2007). A multiagent approach to Q-learning for daily stock trading. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(6), 864-877.

Lee, J. W., Sung-Dong, K. I. M., Jongwoo, L. E. E., & Jinseok, C. H. A. E. (2003). An intelligent stock trading system based on reinforcement learning. IEICE Transactions on Information and Systems, 86(2), 296-305.

Manahov, V., Hudson, R., & Gebka, B. (2014). Does high frequency trading affect technical analysis and market efficiency? And if so, how? Journal of International Financial Markets, Institutions and Money, 28, 131-157.

Mohamadi, Sh. (2004). Technical analysis in Tehran Stock Exchange. Financial Research Journal, 6(1), 97-129. (in Persian)

Moody, J. & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875, 889.

Neely, C. J., & Weller, P. A. (2003). Intraday technical trading in the foreign exchange market. Journal of International Money and Finance, 22(2), 223-237.

Neuneier, R. (1998). Enhancing Q-learning for optimal asset allocation. Advances in Neural Information Processing Systems, 10, 936-942.

Oliver Mihatsch, R. N. (2002). Risk-Sensitive Reinforcement Learning. Machine Learning, 49, 267-290.

Raei, R. & Bajelan, S. (2007). Detecting and modeling of calendar effects in Tehran Stcok Exchange. Quarterly Journal of The Economic Research, 8 (4), 21-47. (in Persian)

Razmi, J., Julay, F., & Emami, A. (2007). A Bootstrap approach for comparing the profitability of technical analysis indicators – Tehran Stock Exchange. Journal of Economic Researchs, 85, 85-110. (in Persian)

Rodríguez-González, A., García-Crespo, Á., Colomo-Palacios, R., Iglesias, F.G. and Gómez-Berbís, J.M. (2011). CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Expert systems with applications, 38(9), 11489-11500.

Saad, E. W., Prokhorov, D. V. & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks, 9(6), 1456-1470.

Samadi, S., Izadinia, N., & Davarzadeh, M. (2010). The application of exploiting technical analysis in Tehran Stock Exchange (an approach to moving average). Journal of Accounting Advances, 2(1), 121-154. (in Persian)

Setayesh, M., Taghizadeh, T., Poormoosa, A., & Abuzari, A. (2008). Feasibility of exploiting technical analysis indicators in predicting the price trend of stocks in Tehran Stock Exchange. Quarterly Basirat, 7, 155-177. (in Persian)

Sutton, R. S. & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge, MIT Press.

Tanaka-Yamawaki, M., & Tokuoka, S. (2007). Adaptive use of technical indicators for the prediction of intra-day stock prices. Physica A: Statistical Mechanics and its Applications, 383(1), 125-133.

Watkins, C. (1989). Learning from delayed rewards, Ph.D, Cambridge University.

Yamamoto, R. (2012). Intraday technical analysis of individual stocks on the Tokyo Stock Exchange. Journal of Banking & Finance, 36(11), 3033-3047.