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

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.

Evaluation of the Improvements of Transformer Prediction with Convolution

Proposed and developed 5 years ago, Transformer has been a prevailing machine learning method and is widely used to solve various kinds of practical problems [1]. According to relevant works, Transformer has performed well in both natural language processing and computer vision tasks, so we would like to test its effectiveness in prediction, specifically, time series prediction. Over the past two years, COVID-19 is no doubt one of the major factors that influences the changes in the stock prices, and the medical industry should be among the most significantly affected, which would provide an ideal sample for us to study transformer on time series prediction. In this paper, we not only construct a machine learning model using Transformer to predict the stock prices of one medical company but also add a convolution layer to try to optimize the predictions. The comparison of the outcome from the two models suggests that the convolution layer could improve the performance of the naive transformer in several ways. © 2022 IEEE.

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.