روشی جهت پیش‌بینی قیمت سهام بازار بورس تهران مبتنی بر یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشگاه پیام نور، تهران، ایران

2 استادیار، دانشگاه پیام نور، تهران، ایران

چکیده

در سال‌های اخیر با توجه به سوددهی بازار بورس اوراق بهادار در ایران سرمایه‌های خرد و کلان جذب این بازار شدند ، اما متأسفانه به دلیل دانش کم این افراد از بورس و پیش‌بینی قیمت‌ها تعداد فراوانی از مردم ایران ضرر و زیان زیادی را متحمل شدند . در این تحقیق بر آن شدیم تا با استناد به تحقیق قبلی خود که از شبکه عصبی با دولایه LSTM استفاده می‌کرد .کار خود را قوت بخشیده و شبکه عصبی ترکیبی کانولوشن وlstm را جهت پیش‌بینی قیمت سهام بر روی مجموعه دیتاست وب ملت از بازار بورس اوراق بهادار تهران و سه دیتاست موجود در آن شامل آث پ ،خودرو و وساخت به کار ببریم. در انتها جهت ارزیابی روش پیشنهادی و دو روش دیگر ازنظر سه تابع خطا ،تابع میانگین مربع خطا (MSE)، تابع میانگین خطای مطلق (MAE) و تابع میانگین مربع ریشه (RMSE) بررسی شد . نتایج حاصله نشان داد در دیتاست های بزرگ با تعداد داده‌های سهام بالا بسیار بهتر عمل کرده و خطای کمتری به دنبال دارد.

کلیدواژه‌ها

عنوان مقاله [English]

A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge

نویسندگان [English]

  • tuba toraby pour 1
  • safieh siadat 2
1 Master's degree, Payam Noor University, Tehran, Iran
2 Assistant Professor, Payam Noor University, Tehran, Iran
چکیده [English]

In recent years, due to the profitability of the stock market in Iran, small and large investments were attracted to this market, but unfortunately, due to their lack of knowledge of the stock market and price forecasting, a large number of Iranians suffered great losses. In this study, we decided to use our previous research, which used a two-layer LSTM neural network, to strengthen its work and use a combination of convolution and lstm neural networks to predict stock prices on the Web Nation data set from the stock market. Use Tehran and its three databases, including ASP, car and construction. Finally, in order to evaluate the proposed method and the other two methods, three error functions, mean square error function (MSE), mean absolute error function (MAE) and root mean square function (RMSE) were evaluated. The results showed that it works much better in large datasets with high stock data and leads to fewer errors.

کلیدواژه‌ها [English]

  • Convolution neural network
  • stock price
  • LSTM neural network
  • deep learning

Smiley face

مراجع

[1] Ismaili, Z and Torabipour, T, “Providing a solution for stock price forecasting using LSTM neural network,” Fifth National Conference on Technology in Electrical and Computer Engineering, 2021. (in persian)
[2] Zhong  X. and Enke D. “Forecasting daily stock market return using dimensionality reduction," International Journal of  Expert Systems with Applications, IJESWA, vol. 67, no. 4, pp. 126-139, 2017.
[3] Seng J. and Yang H. “The association between stock price volatility and financial news-Asentiment analysis approach," International Journal of Kybernetes, IJK, vol. 46, no. 1, pp. 1341-1365, 2017.
[4] Kim K. young-jae K. and Han I. “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” International Journal Of  Expert Systems With Applications, IJESWA, vol. 19, no. 2, pp. 125-132, 2000.
[5] Zarandi M. Rezaee B. and Turksen B. “A type-2 fuzzy rule-based expert system model for stock price analysis," International Journal Of  Expert Systems With Applications, IJESWA, vol. 37, no. 4, pp. 3366-3372, 2010.
[6] JingTao Y. Chew Lim. and Liu N. “Guidelines for Financial Prediction with Artificialneural networks,” International Journal Of Neural Computing and Applications, IJNCA, vol. 32, no. 3, pp 9723-9733, 2009.
[7] Kannan, S. Sekar, M. Sathik and Arumugam, P. “Financial stock market forecast using data mining techniques,” In Proceedings Of The International Multiconference Of Engineers And Computer Scientists (IPIMECS), pp. 724-730, 2010.
[8] Yu T. Kuang H. and Huarng K. “A neural network-based fuzzy time series model to improve forecasting,” International Journal Of Expert Systems With Applications, IJESWA, vol. 37, no. 4, pp. 3366-3372, 2010.
[9] Cheng C. Chen T. and Wei L. “A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting,” International Journal of Computer Science, IJCS, vol. 180, no. 12, pp. 1610-1629, 2010.
[10] Deng M. Shigan B. and Yeh T. “Using least squares support vector machines for the airframe structures manufacturing cost estimation,” International Journal Of Production Economics, IJOPE, vol. 131, no. 2, pp. 701-708, 2011.
[11] Patel J. Shah S. Thakkar P. and Kotecha K. “Predicting stock market index using fusion of machine learning techniques,” International Journal Of Expert Systems With Applications, IJESWA, vol. 42, no. 4, pp. 2162-2172, 2015.
[12] Zhang, Z. Yuan and X.Shao. “A new combined cnn-rnn model for sector stock price analysis,” In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 546-551, 2018.
[13] Kim Y. Young H. and Won C. “Forecasting the volatility of stock price index:A hybrid model integrating LSTM with multiple GARCH-type models,” International Journal Of Expert Systems With Applications, IJESWA, vol. 103, no. 5, pp. 25-37, 2018.
[14] Jin Z. Yang Y. and Liu Y. “Stock closing price prediction based on sentiment analysis and LSTM,” International Journal Of Neural Computing and Applications, IJNCA, vol. 32, no. 2, pp. 9713-9729, 2019.
[15] Rajakumari k. Kalyan S. and Bhaskar M. “Forward Forecast of Stock PriceUsing LSTM Machine Learning Algorithm,” International Journal of Computer Theory and Engineering, IJCTE, vol. 12, no. 3, pp. 12-20, 2020.
[16] Ferdiansyah, F. Kazuki, F. and Kazuhiro, S. “A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market,” International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 257-272, 2019.
[17] Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer, New York, 2009.
[18] Bathla, G. Rani, R., & Aggarwal, H. “Stocks of year 2020: prediction of high variations in stock prices using LSTM,” Multimedia Tools and Applications, pp. 1-17, 2022.
[19] Mehtab, S., & Sen, J. “Analysis and forecasting of financial time series using CNN and LSTM-based deep learning models,” In Advances in Distributed Computing and Machine Learning, pp. 405-423, Springer, Singapore.
[20] Asadi, P, Jibril J, & Majidnejad. “Identify peer-to-peer networks using the deep learning method,” Electronic and Cyber Defense, vol. 8, no. 2, pp. 1-14, 2020.(In Persian)
پدافند الکترونیکی و سایبری
دوره 10، شماره 4 - شماره پیاپی 40
شماره پیاپی 40، فصلنامه زمستان
بهمن 1401
صفحه 91-100

فایل ها

سابقه مقاله

  • تاریخ دریافت: 15 اردیبهشت 1401
  • تاریخ بازنگری: 08 خرداد 1401
  • تاریخ پذیرش: 18 مرداد 1401
  • تاریخ انتشار: 01 بهمن 1401

هم رسانی

ارجاع به این مقاله

آمار