Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries

Ivan Borisov Todorov and Fernando Sánchez Lasheras ()
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Ivan Borisov Todorov: Department of Drilling, Oil and Gas Production & Transport, Faculty of Geology & Exploration, University of Mining and Geology St. Ivan Rilski, 1700 Sofia, Bulgaria
Fernando Sánchez Lasheras: Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain

Energies, 2023, vol. 16, issue 9, 1-21

Abstract: In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The database employed for this research contained the IBEX35 index values and stock closing prices from 3 January 2000 to 30 December 2022. The models trained with this data were employed in order to forecast the index value and the closing prices of the stocks under study from 2 January 2023 to 24 March 2023. The results obtained confirmed that although none of the proposed models outperformed the rest for all the companies, it is possible to calculate forecasting models able to predict a 95% confidence interval about real stock closing values and where the index will be in the following three months.

Keywords: stock price forecasting; publicly traded companies; electricity and gas companies; petroleum companies; time series forecasting; exponential smoothing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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