Research Information System
International Journal of Information Technology and Decision Making, 2026 (SCI-Expanded, Scopus)
We propose stock selection models that guide the decision maker (DM) by integrating multiple financial evaluation criteria into a linear programming framework. We provide a robust framework for stock selection and offer practical insights for investment decision-making. The models aim to achieve the most coherent sequence of alternatives (stocks) with a DM’s ranking by maximizing Kendall’s Tau score. We assume an underlying value function that represents the DM’s preferences. We then develop two mixed integer linear programming models; the first model assumes an underlying linear value function, while the second assumes a Tchebycheff value function. We conduct experiments on stocks listed in the Standard and Poor’s 500 index and compare the performance of our models against three benchmark methods from the literature, using various performance metrics. The results demonstrate that our models achieve high-quality solutions, outperforming benchmark methods in terms of ranking coherence and overall performance.