The Synergy of Interpolative Boolean Algebra and Ordinal Sums of Conjunctive and Disjunctive Functions in Stock Price Trend Prediction

Abstract

Stock price prediction is crucial for accurate investment decision-making and widely regarded as one of the most important tasks in finance. Investors and financial professionals rely on a wide range of input data, such as market information, technical analysis, and fundamental analysis, to make informed decisions. When it comes to financial data, it is important to incorporate the logical dependencies of inputs into the modeling and prediction process. Therefore, logic-based approaches are considered adequate for solving such problems. This paper proposes a novel logic-based approach to stock price trend prediction based on Interpolative Boolean algebra (IBA) and ordinal sums of conjunctive and disjunctive (OSCD) functions. This is the very first paper that aims to explore the synergy of these two approaches in a real-world setting, utilizing their comparative advantages in different phases of modeling. The proposed approach is tested on a sample of 23 companies from the S&P500 over the past three years. The paper also presents the results of the application of the proposed model for the analyzed companies.