Screening and Optimal Allocation of Leveraged Buyout Targets: Point-in-Time Backtesting and Chance-Constrained Mixed-Integer Programming

Selecting acquisition targets under a capital budget is a discrete optimization problem whose inputs, company-level attractiveness scores, are themselves uncertain. This paper builds an end-to-end decision pipeline for leveraged buyout (LBO) target selection. It combines a sector-relative composite screening score built from five standard LBO ratios; a point-in-time backtest against 22 real public-to-private transactions from 2022 to 2024, using financial statements filed with the U.S. Securities and Exchange Commission (SEC) strictly before each announcement; a budget- and diversification-constrained mixed-integer program with linear programming (LP) relaxation shadow prices; and a chance-constrained extension solved by sample average approximation. Only 4.5 percent of realized targets scored in the top quintile of the universe ahead of their deals (95 percent Wilson interval 0.8 to 21.8 percent, 22 targets). Healthcare targets scored markedly higher (median percentile 76.8, three targets) than technology targets (27.6, fourteen targets), but the technology versus non-technology difference is not statistically significant under a Mann-Whitney test. Supervised recalibration of the scoring weights raised the in-sample area under the receiver operating characteristic curve to 0.739 while lowering the top-quintile hit rate from 4.5 percent to zero, a cautionary result on metric misalignment in small-sample calibration. All results are reproducible from public data sources.