Abstract Gerrymandering—the deliberate manipulation of electoral district boundaries for political advantage—is a persistent challenge in U.S. elections. In this work, we introduce and analyze Votemandering, a strategic blend of gerrymandering and targeted political campaigning devised to gain more seats by circumventing fairness measures. Votemandering leverages accurate demographic and socio-political data, bolstered by advancements in technology and data analytics, to influence voter decisions in pursuit of subtle gerrymandering strategies. We formulate votemandering as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A case study of Wisconsin State Senate redistricting reveals significant votemandering potential. Our findings underscore the need for reforms in the redistricting process beyond enforcing thresholds for specific fairness metrics.

Abstract In this paper, we re-examine 166 previously identified asset pricing characteristics and their ability to successfully predict stock returns. We use Explainable Artificial Intelligence to rank these return predictors based on their importance in various asset pricing model settings. Our findings suggest that ensemble and deep learning-based models have an advantage in providing generalized predictions across different return measures. Using SHapley Additive exPlanations, we also find that momentum and trading-based features possess higher predictive power in estimating asset returns. The long-short portfolio analysis reveals that key return predictors exhibit substantial economic significance, reflected in the large differences in out-of-sample $$R^2$$ . These findings remain robust across various models and persist even after controlling for characteristics-based predictors.