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Abstract
<jats:p>Financial forecasting systems prioritize directional classification or point-forecast accuracy, yet capital processes in nonstationary markets must also withstand risk-boundary breaches and recover from recurrent shocks. Physiological allostasis offers a design logic: anticipatory regulation, coordination across fast and slow timescales, regulatory load accumulation, and path-dependent recovery. This study translates these functions into a Biomimetic Allostatic Risk and Viability Controller (BARVC). It conducts a longitudinal, single-security proof of concept using 7,109 observations for XCMG Machinery from August 28, 1996, through August 12, 2026. The sample contains 91 distinct drawdown cycles, including a maximum drawdown of 89.66%, and 124 major upward-move events. Model development used 1996–2018 for training, 2019–2021 for calibration, and locked 2022–2026 data for evaluation, with a 20-trading-day purge window separating calibration from testing. In the locked test period, the full-state model achieved 72.02% coverage for the P10–P90 interval, with an absolute coverage error of 7.98 percentage points; the non-allostatic model achieved 75.35% coverage and a 4.65-point error. Relative to buy-and-hold, BARVC reduced maximum drawdown from 36.08% to 21.77% and the daily severe-breach rate from 16.16% to 0.83%. However, a constant 45% risk exposure produced a lower maximum drawdown of 17.41% and no severe breaches. The results supported none of the five prespecified mechanistic advantage hypotheses. The study therefore establishes the computational feasibility and falsifiability of allostatic functional translation but does not establish superior investment performance. Cross-security validation and a larger sample of independent stress events are required to determine whether the findings generalize beyond the focal security.</jats:p>