Predicting Novatek Share Prices Using the Models of Decision Tree and Linear Regression
- Autores: Kuznecov R.S.1, Tumarova T.1
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Afiliações:
- Saint-Petersburg State University of Economics
- Edição: Nº 7 (2023)
- Páginas: 56-70
- Seção: Articles
- URL: https://ogarev-online.ru/0207-3676/article/view/142818
- DOI: https://doi.org/10.31857/S020736760026686-3
- ID: 142818
Texto integral
Resumo
The development of technology and the emergence of various machine learning models influence social life in many ways, including the analysis and forecasting of the stock market. The ability to competently select and use machine learning models in predicting stock quotes is one of the key competitive advantages that allow large investment companies and individuals to increase their profits from the market activity. The study reveals the effectiveness of using decision tree and linear regression models in predicting daily quotes of NOVATEK.
Palavras-chave
Sobre autores
Roman Kuznecov
Saint-Petersburg State University of Economics
Email: socpol@mail.ru
St. Petersburg, Russian Federation
Tatiana Tumarova
Saint-Petersburg State University of Economics
Email: socpol@mail.ru
St. Petersburg, Russian Federation
Bibliografia
- Agrawal M., Shukla P., Nair R., Nayyar A. Stock prediction based on technical indicators using deep learning model // Tech Science Press. 2022. No. 1. Pp. 287–304.
- Eguz B., Kaya T. Stock price prediction of Turkish banks using machine learning methods // International conference on intelligent and fuzzy systems. 2021. No. 308. Pp. 1–17.
- Ghani M., Awais M., Muzammul M. Stock market prediction using machine learning (ML) algorithms // ADCAIJ: advances in distributed computing and artificial intelligence journal. 2019. No. 4. Pp. 97–116.
- Gururaj V., Ashwini K. Stock market prediction using linear regression and support vector machines // International journal of applied engineering research. 2019. No. 14. Pp. 1931–1934.
- Hota J., Chakravarty S., Paikaray B., Bhoyar H. Stock market prediction using machine learning techniques // CEUR. 2022. No. 3283. Pp. 163–171.
- Kumar M., Chandra K., Gupta K. Stock analysis and prediction of Indian oil trading using big data analytics // International journal of mechanical engineering. 2022. No. 1. Pp. 6734–6738.
- Lv P., Wu Q., Xu J. Stock index prediction based on time series decomposition and hybrid model // Entropy. 2022. No. 24. Pp. 1–18.
- Majumder A., Rahman M., Biswas A. Stock market prediction: a time series analysis // Smart systems: innovations in computing. 2021. No. 235. Pp. 389–401.
- Rajeswar S., Ramalingam P., Sudalai T. Comparative analysis of stock market price behaviors using machine learning techniques // International conference on advances in materials, computing and communication technologies. 2020. No. 2385. Pp. 25–37.
- Raubitzek S., Neubauer T. An exploratory study on the complexity and machine learning predictability of stock market data // Entropy. 2022. No. 332. Pp. 1–34.
- Serrano W. The random neural network in price predictions // Neural computing and applications. 2022. No. 34. Pp. 855–873.
- 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.
- Singh G. Machine learning models in stock market prediction // International Journal of innovative technology and exploring engineering (IJITEE). 2022. No. 11. Pp. 18–28.
- Singh S., Rehan S., Kumar V. Stock price prediction using linear regression, LSTM and decision tree // EasyChair Preprint. 2022. No. 7805. Pp. 1–5.
- Zhao A., Cheng T. Stock return prediction: stacking a variety of models // ELSEVIER. 2022. No. 67. Pp. 1–12.
