A1880
Title: Quantile regression and fuzzy-probabilistic inference in financial decision-making
Authors: Tomas Tichy - VSB-TU Ostrava (Czech Republic) [presenting]
David Nedela - VSB - TU Ostrava (Czech Republic)
Michal Holcapek - University of Ostrava (Czech Republic)
Abstract: Weighted quantiles are essential tools in statistical analysis and regression, particularly for time-series data and moving quantile functions. Traditionally, their computation has relied on linear programming methods, which, while effective, can be computationally intensive. An alternative approach utilizing the right derivatives of the associated piecewise linear function is introduced. The corresponding weighted quantile is derived by minimizing this function. This alternative approach retains the precision and reliability of traditional techniques while simplifying the computation. Furthermore, this approach is combined with probabilistic fuzzy rules and inverse quantile fuzzy transforms to propose a framework for estimating running quantiles and predictive modelling. Additional results on financial decision-making are provided, which strengthen the dimensionality reduction and robust regression techniques. For comparison purposes, parametric and nonparametric approximation techniques are also considered. The proposed models are applied to a real-world dataset of financial assets, demonstrating their effectiveness in outperforming traditional approaches.
