Predicting the Monthly Nasdaq-100 Index Level from Macroeconomic and Risk Indicators Using Extreme Gradient Boosting and Explainable Artificial Intelligence (XAI)

Equity index models usually rely on the index’s own history, which leaves open how far macroeconomic and risk conditions alone can track its level. This study maps five contemporaneous United States predictors, the economic, financial, and political risk indices, the M2 money supply and industrial production, onto the monthly closing level of the Nasdaq-100 index, and interprets the resulting fit with post hoc attribution. The data span 273 months from May 2003 to January 2026, with training to December 2019 and 73 evaluation months, 97.3 per cent of which lie above the largest training level. Fourteen learners are compared in a two-by-two design crossing direct-level and anchored one-month-change targets with static and recursive fitting. Hyperparameters are searched by a tree-structured Parzen estimator and by differential evolution under a matched budget of 60 evaluations. Three attribution methods are then ranked by the stability of the predictor ordering they reproduce, and accuracy differences are tested by a moving-block bootstrap and a Diebold–Mariano statistic. Every learner returns a negative coefficient of determination under the static direct specification, where the mean root mean squared error is 0.9052, against 0.3102 recursively. Extreme gradient boosting, retained at its default configuration because the search worsened it, records an error of 0.1039 and a coefficient of determination of 0.8677, though no test separates it from gradient boosting or extremely randomised trees. Drift (0.0584) and no-change (0.0600) benchmarks, which use index history withheld from the learners, are more accurate than every learner in that configuration. Permutation feature importance is the most stable method at 0.9374, against 0.8289 for Shapley additive explanations and 0.8063 for leave-one-covariate-out, and assigns the money supply 68.28 per cent of importance; placebo and detrending checks show that this reliance reflects the trend the series shares with the index rather than information specific to money, and the ranking is unchanged under grouped and block permutation. An untouched 2015–2019 window and a nested split of the evaluation period confirm these orderings. Retraining cadence and the information set implied by the target formulation therefore outweigh the choice of learner, and because the predictors are contemporaneous, the result describes a concurrent mapping rather than an ex ante forecast.