Kuznetsov R.S., Tumarova T.G. PAO GAZPROM STOCK PRICE PREDICTION USING LSTM NEURAL NETWORKS

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 3/2023. Finance.

Roman S. Kuznetsov

postgraduate student, Saint Petersburg State University of Economics, Saint Petersburg, Russia

Tatiana G. Tumarova

Cand. Sci. (Econ.), Professor, Director of the Graduate Institute of Saint Petersburg State University of Economics, Saint Petersburg, Russia

PAO GAZPROM STOCK PRICE PREDICTION USING LSTM NEURAL NETWORKS

84-98

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Abstract

Artificial intelligence and machine learning are increasingly being used in many areas of economy and finance. Developments in technology and the increasing accessibility of computing facilities allow for the wider use of various programming tools. For many companies operating in the field of stock trading and other derivatives, availability of an effective forecasting mechanism is an important competitive advantage, and increasing this advantage is now possible by using neural network models such as LSTM. The authors present the results of testing the LSTM model on the basis of actual data (quotations of Gazprom shares at Moscow Exchange): quotation values were predicted and the trend of Gazprom stocks at Moscow Exchange, starting from September 2019, was revealed.

Keywords: stock exchange, neural networks, LSTM, forecasting, PAO Gazprom shares.

JEL: F21, F37, F47

EDN: RTZFON

DOI: https://doi.org/10.52180/2073-6487_2023_3_84_98

References

Alkhatib K., Khazaleh H., Abualigah L. A new stock price forecasting method using active deep learning approach // ELSEVIER. 2022. No. 8. 117–134.
Alzahrani A., Aldhyani H. Framework for predicting and modelling stock market prices based on deep learning algorithms // Electronics. 2022. No. 11. Pp. 15–19.
Anand C. Comparison of stock price prediction models using pre-trained neural networks // Journal of ubiquitous computing and communication technologies (UCCT). 2021. Pp. 122–134.
Banik S., Sharma N., Mangla M. LSTM based decision support system for swing trading in stock market // ELSEVIER. 2022. No. 239. 10–27.
Bathla G., Rani R., Aggarwal H. Stock of year 2020: prediction of high variations in stock prices using LSTM // ELSEVIER. 2022. No. 97. 97–103.
Bhandari H.N., Rimal B., Pokhrel N.R. Predicting stock market index using LSTM // ELSEVIER. 2022. No. 9. Pp. 1–15.
Chandar K. Convolutional neural network for stock trading using technical indicators // ELSEVIER. 2022. No. 39. Pp. 29–35.
Chen Y., Wu J., Wu Z. China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach // ELSEVIER. 2022. No. 202. Pp. 103–117.
Dami S., Esterabi M. Predicting stock returns of Tehran exchange using LSTM neural network and feature engineering technique // ELSEVIER. 2021. No. 80. Pp. 1–17.
Ghosh P., Neufeld A., Sahoo J. Forecasting directional movements of stock prices for intraday trading using LSTM and random forest // ELSEVIER. 2022. No. 46. Pp. 47–65.
Gupta A., Kumar Y. Stock market analysis and prediction for Nifty50 using LSTM deep learning approach // International conference of innovative practices in technology and management (ICIPTM). 2022. Pp. 1–2.
Jafari H., Lashgari A., Rabiee E. Cryptocurrency price prediction with Neural Networks of LSTM and Bayesian optimization // European journal of business and management research. 2022. No. 7. Pp. 20–27.
Shah J., Vaidya D., Shah M. A comprehensive review on multiple hybrid deep learning approaches for stock prediction // ELSEVIER. 2022. No. 16. Pp. 1–14.
Soni P., Tewari Y., Krishan D. Machine learning approaches in stock price prediction: a systematic review // Journal of physics: conference series. 2022. No. 2161. Pp. 1–10.
Zaheer S., Anjum N., Algarni A. A multi parameter forecasting for stock time series data using LSTM and deep learning model // Mathematics. 2023. No. 11(3). Pp. 1–24.

Manuscript submission date: 22.03.2023

For citation:

Kuznetsov R.S., Tumarova T.G. PAO Gazprom stock price prediction using LSTM neural networks // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2023. № 3. Pp. 84-98. (In Russ.). https://doi.org/10.52180/2073-6487_2023_3_84_98 EDN: RTZFON

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