طراحی الگوی تعیین راهبردهای معاملاتی سهام با رویکرد مبتنی بر آینده‌پژوهی، تحلیل بنیادی، مهندسی ویژگیها و الگوریتم های یادگیری ماشین
محورهای موضوعی : مهندسی مالی
سید مجید موسوی انزهایی
1
(
گروه مدیریت مالی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
)
هاشم نیکو مرام
2
(
گروه مدیریت مالی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
)
کلید واژه: آینده‌پژوهی, راهبردهای معاملاتی, الگوریتم LGBM, قواعد معاملاتی خبرگان, سیگنالهای تابلوخوانی سهم,
چکیده مقاله :
سرمایه‌گذاران در بازار سهام همواره به دنبال روش های نوین و کارآمد جهت پیش بینی روند حرکت قیمت سهم و اتخاذ استراتژیهای معاملاتی مناسب بوده‌اند. این پژوهش با بهره‌گیری از مدلی مرکب از آینده پژوهی ، تحلیل بنیادی ، قواعد معاملاتی خبرگان و الگوریتم های یادگیری ماشین، الگویی جهت اتخاذ راهبردهای معاملاتی مناسب پیشنهاد می‌نماید. ابتدا با استفاده از نظر خبرگان وآینده پژوهی، سناریو های پیش روی بازار سهام طراحی و با انجام تحلیل بنیادی سبدی شامل شش سهم تشکیل می‌گردد . در مرحله بعد با استفاده از 7 الگوریتم یادگیری ماشین و داده های شرکت های منتخب در بازه زمانی 1393 تا 1398، مدلسازی جهت پیش بینی روند قیمت هر سهم منتخب صورت می‌گیرد. متغیرهای ورودی مدل شامل شاخص های تکنیکال، قواعد تکنیکال، قواعد تابلوخوانی و داده‌های معاملاتی سهم می‌باشد. نتایج نشان می‌دهد، بکارگیری الگوی پیشنهادی برای سرمایه گذاری در بازار سهام بازدهی بالاتری را نسبت به شاخص کل بورس تهران ایجاد می‌نماید. همچنین بکارگیری راهبردهای معاملاتی کوتاه مدت مبتنی بر سیگنالهای مدل آموزش داده شده توسط الگوریتم تقویت گرادیان سبک (LGBM)، بازدهی بالاتری را در مقایسه با استراتژیهای خرید - نگهداری و تکنیکال برای سبد سهام منتخب ارایه می دهد.
Investors in stock market, always seeking for novel and efficient methods to predict stocks price and make the appropriate trading strategies. This paper propose a pattern for implementing appropriate strategies with a model composed of future studies, Fundamental analysis, expert trading rules and machine learning algorithms. First with use of feature study and expert’s opinion, stock market scenarios designed and a portfolio consist of 6 fundamental stocks is built. In next step for each selected stocks a model for stock price movement prediction is developed by means of related stocks data from 1393 to 1398 and 7 machine learning algorithms. Model inputs includes, technical indicators, technical trading rules, stock sign reading rules and stocks trading data. Results show that implementing proposed composed model for investment in stock market led to greater performance compared with Tehran stock market index and implementing short term trading strategies based on the model trained with light gradient boosting machine (LGBM) algorithm cause better performance in comparison with Buy& Hold and Technical trading Strategies.
فهرست منابع
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11) Amer M, Daim TU, Jetter A.(2013). Scenario planning for the national wind energy Sector through Fuzzy Cognitive Maps. In: Portland international center for Management of engineering and technology (PICMET): technology management in the it-driven services, 2013. p. 2153e62. San Jose, CA.
12) Atsalakis, G.S., Valavanis, K.P., (2009). Surveying stock market forecasting techniques part II: soft computing methods, Expert Syst. Appl.36 (3), 5932-5941.
13) Barak, Sasan, Arjmand, Azadeh, Ortobelli, Sergio (2016). Fusion of Multiple Diverse Predictors in Stock Market. Journal of Information Fusion, Accepted Manuscript.
14) Chermack, T.J. (2005). Studying scenario planning: theory, research suggesti and hypotheses Technol. Forecast. Soc. Chang. 72 (1), 59–73.
15) Choudry,R, Grag, K. (2008). A Hybrid Machin Learning System for Stock Market Forecasting.Word Academy of Science, Engineering and Technology, 39.
16) Fama, E.-F., Blume, M.-E., (1966), Filter rules and stock market trading, J. Bus. 39 (1), 226–241.
17) Gulin, Ke, et.al, (2017), LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Neural Information Processing Systems, 30.
18)Jing, Zhang, et.al.(2018), A novel data- driven stock price trend prediction system, Expert system with applications, 97,60 - 69.
19) Lawrence, R. (1997), Using Neural Networks to Forecast Stock Market Prices, 1-12.
20) Leung, M, T., Chen, A. S., & Daouk, H. (2000). Forecasting exchange rates general regression neural networks.
21) Nanni, L., & Lumini, A. (2009). An experimental comparison of ensemble of classi- fiers for bankruptcy prediction and credit scoring. Expert Systems with Applica- tions, 36, 3028–3033.
22) Pamučar, Ddragan, Ćirović, Goran, (2015), the selection of transport and handling resources in logistics centres using Multi-Attributive Border Approximation area Comparison (MABAC), Expert system with applications, 42, 3016- 3028
23) Patel, J, et.al. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machinelearnning techniques. Expert Systems with Applications, 42 (1), 259–268.
24) Shearer C. (2000), The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13—22.
25) Sarkar, Sobhan, et al. (2019), Application of optimized machine learning techniques for prediction of Occupational accidents,Computer & Operation research, 106, 210-224.
26) Shynkevich, Yauheniya, et al. (2017), forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264 71–88.
27) Suryoday, Basak, et al., (2018), predicting the direction of stock market prices using tree-based classifiers, North American Journal of Economics and finance, In Press.
28) Trappey, C., Shin, T. and Trappey, A. (2007), “Modeling international investment decisions for financial holding companies”, European Journal of Operational Research, Vol. 180, pp. 800-14.
29) Xiaolei, Sun, Mingxi, Liu , Zeqian, Sima, (2020). A novel cryptocurrency price trend forecasting model base on LightGBM, Finance Research Letters, 32.
30) Xiao-dan Z., Ang L., Ran P., (2016), Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine, Journal of Applied Soft Computing, 385-398.
31) Yufei, Xia, Chuanzhe, liu, YuYing, Li, Nana, Liu, (2017). Aboosted decision tree approach using Bayesian hyper_parameter optimization for credit scoring, Expert System with Application, 78, 225-241.
_||_) Bajlan, Saeed, Fallahpour, Saeed, Dana, Naheed (2016), predicting the trend of share price changes using weighted support vector machine and choosing hybrid features in order to provide an optimal trading strategy, financial management strategy, 14th issue, autumn 2015.
2) Tehrani, Reza, Handijanizadeh, Mohammad, Norouzian, Isa (2014), presenting a new approach for active portfolio management and making smart stock transactions with an emphasis on the attitude of feature selection, Investment Knowledge, No. 13, Spring 2015.
3) Rai, Reza, Hosseini, Farhang (2014), comparing buying and selling efficiency based on technical indicators and fuzzy logic and the combined method of genetic algorithm-fuzzy logic. Financial engineering and securities management. Number twenty-four, autumn 2014.
4) Rahnema Roudpashti, Fereydoun, Shirin Bayan, Neda (2015), Designing an investment portfolio using a scenario approach using the planning method based on assumptions, financial engineering and securities management, number twenty-eight, autumn 2015.
5) Saranj, Alireza et al. (2019), Designing a technical stock trading system using MLP neural network hybrid model and evolutionary algorithms, financial knowledge and securities analysis.
6) Shah Mansouri, Esfandiar (2016), the test of the portfolio of securities based on fundamental, technical and intuitive strategies with the goals and behavioral characteristics of Tehran Stock Exchange investors, Investment Knowledge, 6th year, winter issue of 2016.
7) Gholamian, Elham, Davodi, Seyed Mohammad Reza (2017), forecasting the price trend in the stock market using the random forest algorithm, financial engineering and securities management, number 35.
8) Fallahpour, Saeed, Gol Arzi, Gholam Hossein, Faturchian, Nasser (2013), predicting the stock price trend using support vector machine based on genetic algorithm in the stock exchange. period 15,
9) Mashari, Mohammad, et al. (2018), Designing a hybrid intelligent model to predict the golden points of stock prices, Investment Knowledge, 8th year, 29th issue.
10) Alipour, M., Hafezi, V., M. Amer, M., Akhavan. A.N. (2017). A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post-sanction period. Energy 135, 851-864.
11) Amer M, Daim TU, Jetter A.(2013). Scenario planning for the national wind energy Sector through Fuzzy Cognitive Maps. In: Portland international center for Management of engineering and technology (PICMET): technology management in the it-driven services, 2013. p. 2153e62. San Jose, CA.
12) Atsalakis, G.S., Valavanis, K.P., (2009). Surveying stock market forecasting techniques part II: soft computing methods, Expert Syst. Appl.36 (3), 5932-5941.
13) Barak, Sasan, Arjmand, Azadeh, Ortobelli, Sergio (2016). Fusion of Multiple Diverse Predictors in Stock Market. Journal of Information Fusion, Accepted Manuscript.
14) Chermack, T.J. (2005). Studying scenario planning: theory, research suggesti and hypotheses Technol. Forecast. Soc. Chang. 72 (1), 59–73.
15) Choudry,R, Grag, K. (2008). A Hybrid Machin Learning System for Stock Market Forecasting.Word Academy of Science, Engineering and Technology, 39.
16) Fama, E.-F., Blume, M.-E., (1966), Filter rules and stock market trading, J. Bus. 39 (1), 226–241.
17) Gulin, Ke, et.al, (2017), LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Neural Information Processing Systems, 30.
18)Jing, Zhang, et.al.(2018), A novel data- driven stock price trend prediction system, Expert system with applications, 97,60 - 69.
19) Lawrence, R. (1997), Using Neural Networks to Forecast Stock Market Prices, 1-12.
20) Leung, M, T., Chen, A. S., & Daouk, H. (2000). Forecasting exchange rates general regression neural networks.
21) Nanni, L., & Lumini, A. (2009). An experimental comparison of ensemble of classi- fiers for bankruptcy prediction and credit scoring. Expert Systems with Applica- tions, 36, 3028–3033.
22) Pamučar, Ddragan, Ćirović, Goran, (2015), the selection of transport and handling resources in logistics centres using Multi-Attributive Border Approximation area Comparison (MABAC), Expert system with applications, 42, 3016- 3028
23) Patel, J, et.al. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machinelearnning techniques. Expert Systems with Applications, 42 (1), 259–268.
24) Shearer C. (2000), The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13—22.
25) Sarkar, Sobhan, et al. (2019), Application of optimized machine learning techniques for prediction of Occupational accidents,Computer & Operation research, 106, 210-224.
26) Shynkevich, Yauheniya, et al. (2017), forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264 71–88.
27) Suryoday, Basak, et al., (2018), predicting the direction of stock market prices using tree-based classifiers, North American Journal of Economics and finance, In Press.
28) Trappey, C., Shin, T. and Trappey, A. (2007), “Modeling international investment decisions for financial holding companies”, European Journal of Operational Research, Vol. 180, pp. 800-14.
29) Xiaolei, Sun, Mingxi, Liu , Zeqian, Sima, (2020). A novel cryptocurrency price trend forecasting model base on LightGBM, Finance Research Letters, 32.
30) Xiao-dan Z., Ang L., Ran P., (2016), Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine, Journal of Applied Soft Computing, 385-398.
31) Yufei, Xia, Chuanzhe, liu, YuYing, Li, Nana, Liu, (2017). Aboosted decision tree approach using Bayesian hyper_parameter optimization for credit scoring, Expert System with Application, 78, 225-241.
کل
1) Bajlan, Saeed, Fallahpour, Saeed, Dana, Naheed (2016), predicting the trend of share price changes using weighted support vector machine and choosing hybrid features in order to provide an optimal trading strategy, financial management strategy, 14th issue, autumn 2015.
2) Tehrani, Reza, Handijanizadeh, Mohammad, Norouzian, Isa (2014), presenting a new approach for active portfolio management and making smart stock transactions with an emphasis on the attitude of feature selection, Investment Knowledge, No. 13, Spring 2015.
3) Rai, Reza, Hosseini, Farhang (2014), comparing buying and selling efficiency based on technical indicators and fuzzy logic and the combined method of genetic algorithm-fuzzy logic. Financial engineering and securities management. Number twenty-four, autumn 2014.
4) Rahnema Roudpashti, Fereydoun, Shirin Bayan, Neda (2015), Designing an investment portfolio using a scenario approach using the planning method based on assumptions, financial engineering and securities management, number twenty-eight, autumn 2015.
5) Saranj, Alireza et al. (2019), Designing a technical stock trading system using MLP neural network hybrid model and evolutionary algorithms, financial knowledge and securities analysis.
6) Shah Mansouri, Esfandiar (2016), the test of the portfolio of securities based on fundamental, technical and intuitive strategies with the goals and behavioral characteristics of Tehran Stock Exchange investors, Investment Knowledge, 6th year, winter issue of 2016.
7) Gholamian, Elham, Davodi, Seyed Mohammad Reza (2017), forecasting the price trend in the stock market using the random forest algorithm, financial engineering and securities management, number 35.
8) Fallahpour, Saeed, Gol Arzi, Gholam Hossein, Faturchian, Nasser (2013), predicting the stock price trend using support vector machine based on genetic algorithm in the stock exchange. period 15,
9) Mashari, Mohammad, et al. (2018), Designing a hybrid intelligent model to predict the golden points of stock prices, Investment Knowledge, 8th year, 29th issue.
10) Alipour, M., Hafezi, V., M. Amer, M., Akhavan. A.N. (2017). A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post-sanction period. Energy 135, 851-864.
11) Amer M, Daim TU, Jetter A.(2013). Scenario planning for the national wind energy Sector through Fuzzy Cognitive Maps. In: Portland international center for Management of engineering and technology (PICMET): technology management in the it-driven services, 2013. p. 2153e62. San Jose, CA.
12) Atsalakis, G.S., Valavanis, K.P., (2009). Surveying stock market forecasting techniques part II: soft computing methods, Expert Syst. Appl.36 (3), 5932-5941.
13) Barak, Sasan, Arjmand, Azadeh, Ortobelli, Sergio (2016). Fusion of Multiple Diverse Predictors in Stock Market. Journal of Information Fusion, Accepted Manuscript.
14) Chermack, T.J. (2005). Studying scenario planning: theory, research suggesti and hypotheses Technol. Forecast. Soc. Chang. 72 (1), 59–73.
15) Choudry,R, Grag, K. (2008). A Hybrid Machin Learning System for Stock Market Forecasting.Word Academy of Science, Engineering and Technology, 39.
16) Fama, E.-F., Blume, M.-E., (1966), Filter rules and stock market trading, J. Bus. 39 (1), 226–241.
17) Gulin, Ke, et.al, (2017), LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Neural Information Processing Systems, 30.
18)Jing, Zhang, et.al.(2018), A novel data- driven stock price trend prediction system, Expert system with applications, 97,60 - 69.
19) Lawrence, R. (1997), Using Neural Networks to Forecast Stock Market Prices, 1-12.
20) Leung, M, T., Chen, A. S., & Daouk, H. (2000). Forecasting exchange rates general regression neural networks.
21) Nanni, L., & Lumini, A. (2009). An experimental comparison of ensemble of classi- fiers for bankruptcy prediction and credit scoring. Expert Systems with Applica- tions, 36, 3028–3033.
22) Pamučar, Ddragan, Ćirović, Goran, (2015), the selection of transport and handling resources in logistics centres using Multi-Attributive Border Approximation area Comparison (MABAC), Expert system with applications, 42, 3016- 3028
23) Patel, J, et.al. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machinelearnning techniques. Expert Systems with Applications, 42 (1), 259–268.
24) Shearer C. (2000), The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13—22.
25) Sarkar, Sobhan, et al. (2019), Application of optimized machine learning techniques for prediction of Occupational accidents,Computer & Operation research, 106, 210-224.
26) Shynkevich, Yauheniya, et al. (2017), forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264 71–88.
27) Suryoday, Basak, et al., (2018), predicting the direction of stock market prices using tree-based classifiers, North American Journal of Economics and finance, In Press.
28) Trappey, C., Shin, T. and Trappey, A. (2007), “Modeling international investment decisions for financial holding companies”, European Journal of Operational Research, Vol. 180, pp. 800-14.
29) Xiaolei, Sun, Mingxi, Liu , Zeqian, Sima, (2020). A novel cryptocurrency price trend forecasting model base on LightGBM, Finance Research Letters, 32.
30) Xiao-dan Z., Ang L., Ran P., (2016), Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine, Journal of Applied Soft Computing, 385-398.
31) Yufei, Xia, Chuanzhe, liu, YuYing, Li, Nana, Liu, (2017). Aboosted decision tree approach using Bayesian hyper_parameter optimization for credit scoring, Expert System with Application, 78, 225-241.
