The study disentangles founder-led from governance-entrenched family control and links them to regulator-observable trading patterns around firms’ stocks. Analyzing 8,634 U.S. firm-years (2007–2018) using NASDAQ SMARTS continuous-trading-manipulation (CTM) flags, we find Founder-CEO Control associated with approximately 9.5% fewer CTM flags, Family Governance Involvement with about 21.3% more, and Deep Multi-Generational Family Control with about 47.1% more. These results persist across specifications and controls, indicating heterogeneous governance mechanisms with opposite implications for market integrity. External monitoring via institutional ownership attenuates the entrenchment channel, while descendant management does not drive the positive governance-entrenchment results, suggesting governance structure–dependent effects on the trading environment surrounding the firm [page::16][page::17][page::19][page::20][page::22][page::29].
This work adapting Pauli Correlation Encoding (PCE) to quantum topological data analysis reframes Betti-number estimation as a depth-efficient, variational Rayleigh-quotient minimization over a compressed qubit register, enabling Betti counting on realistic market datasets [page::1]. It demonstrates classical validation against ripser across 190 sliding windows (2007–2009) and shows that, with warm-start from a classical null-space surrogate, PCE-VQE can recover β1 exactly for real-market Laplacians (β1 = 1–22) at every scale, highlighting the obstacle as the optimization landscape rather than the encoding itself [page::1][page::6]. Chronologically split classification delivers in-regime ROC AUC of 0.818, while out-of-distribution crises (COVID shock, rate cycle) show calibration failure, indicating regime-aware deployment is necessary [page::9]. The paper provides resource models and end-to-end workflow for near-term quantum TDA on financial stress, concluding with a call for quantum-native warm starts and subspace strategies to extend beyond β1<‑=22 and to real-market scales [page::9].
This paper develops a structural model of maritime chokepoint closures, showing that losses extend beyond the value of trade crossing a passage because disruptions to intermediate inputs propagate through global production networks [page::0]. Across countries, exposure is heavy-tailed, and the exporter end bears outsized direct losses relative to buyers due to concentrated commodity exports, while buyers face productivity losses via bundle penalties and network propagation [page::4]. Joint closures exhibit non-additive behavior: sub-additive losses for Middle East scenarios (shared detours) and super-additive losses for East Asia and Russia–Europe scenarios (distinct inputs starving common buyers), with the nonlinear CES cascade driving the latter while the former is dominated by direct severance and limited reallocation [page::5]. The analysis covers twelve chokepoints and three joint geopolitical scenarios, provides a four-channel decomposition (direct severance, network propagation, autonomous-output bundle penalty, intermediate-sales bundle penalty), and validates the model against historical disruptions and the 2023–24 Red Sea episode, highlighting tail risk and geographic concentration in the exposure distribution [page::0][page::4][page::5].
