Year: 2025
Type: article
Abstract: Investing in the stock market has always been an exciting topic for people. Many specialists have tried to develop tools to predict future stock prices in order to make high profits and avoid big losses. However, predicting prices based on the dynamic characteristics of stocks seems to be a non-trivial problem. In practice, the predictive models are not expected to provide the most accurate forecasts of stock prices, but to highlight changes and discrepancies between the predicted and observed values, to warn against threats, and to inform users about upcoming opportunities. In this paper, we discuss the use of frequent sequences as well as association rules in WIG20 stock price prediction. Specifically, our study used two methods to approach the problem: correlation analysis based on the Pearson correlation coefficient and frequent sequence mining with temporal constraints. In total, 43 association rules were discovered, characterized by relatively high confidence and lift. Moreover, the most effective rules were those that described the same type of trend for both companies, i.e., rise ⇒ rise, or fall ⇒ fall. However, rules that showed the opposite trend, namely fall ⇒ rise or rise ⇒ fall, were rare.
DOI: 10.3390/ijfs13020055
Institution: Gdańsk University of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4409155066