When the Lights Go Out, Markets Lose Their Words: A Physics-Inspired Detector for Financial ‘Metabolic Arrest

Financial risk models have long treated the electricity grid as an invisible, frictionless constant—a silent assumption so deep in the plumbing of quantitative finance that few practitioners ever think to question it. A new study published in Frontiers in Physics dismantles that assumption with a striking empirical demonstration: when power supply becomes a binding constraint, markets do not merely slow down, they undergo something the author calls “metabolic arrest,” a collapse in information-processing capacity that conventional risk indicators fail to register until it is already underway. Drawing on South Africa’s decade-long load-shedding crisis as a natural laboratory, the research introduces TENSORnet, a detection protocol that borrows its logic from thermodynamics rather than from classical econometrics.

The concept of metabolic arrest is borrowed from biology, where it describes how organisms suppress cellular activity when energy becomes scarce. The financial analogue is grounded in a genuine physical principle: Landauer’s principle, formulated in 1961, establishes that every irreversible computational operation dissipates a minimum amount of heat, proportional to the product of Boltzmann’s constant and the system’s absolute temperature. Every trade executed on an exchange—every price signal, settlement record, and position reconciliation—is a physical computation running on silicon that requires electrical power. When the grid fails, the market’s information-processing throughput contracts. The study is careful, however, to frame Landauer’s principle as a heuristic motivation for a specific functional form rather than a literal physical derivation; no claim is made that a financial trade constitutes a strict thermodynamic erasure event, or that a meaningful temperature can be assigned to a financial system.

TENSORnet—which stands for Temporal Entropy-aware Network for Systemic Onset Recognition, and which, despite its name, contains no neural network at all—fuses two components. The first is a VIX-calibrated inhibitory gate: a sigmoid function, fitted by nonlinear least squares using only training-period data, that suppresses the effective information flux in the entropy measure as a function of infrastructure stress. The second is a cross-asset Shannon entropy layer, computed over a 20-day rolling window across seven asset classes on the Johannesburg Stock Exchange: equities, government bonds, gold, platinum, a commodity index, the VIX itself, and Brent crude. In a healthy market, return activity is distributed across assets and entropy is high. Under stress, activity concentrates, entropy collapses, and the gated signal rises—providing a continuous detection variable that the author maps onto three operational regimes, from passive surveillance to full intervention.

The empirical design is unusually disciplined for this literature. The dataset spans 2,838 JSE trading days from January 2015 to April 2026, paired with the official Eskom load-shedding stage record. Because Stage 4 load-shedding was implemented for the first time in South African history only in February 2019, and Stage 6 only in December 2019, the training window (2015–2018) contains no fragile-regime days at all. The study therefore adopts a three-way split: training for gate calibration, validation in 2019 for threshold selection, and a held-out test period from 2020 to 2026 comprising 1,581 days that was touched exactly once. On the primary Stage 3+ stress definition, TENSORnet achieves an F1 score of 99.55 percent and an area under the ROC curve of 0.9996, with just two false positives out of 1,140 resilient test days.

But the study’s most consequential finding is a negative one. A formal DeLong test comparing TENSORnet’s ROC curve against a naive classifier that simply uses the lagged infrastructure stage finds no statistically significant difference between them. In other words, the elaborate thermodynamically motivated architecture performs no better than simply knowing yesterday’s load-shedding stage—a result the author reports directly rather than burying. The honest interpretation is that infrastructure stage information is the dominant signal in this setting, and no architecture tested, including TENSORnet, exceeds it. What the architecture does provide is a validated, market-observable proxy for stage information in settings where official grid data is delayed, incomplete, or unavailable—a nontrivial value proposition for regulators in jurisdictions where grid operators publish megawatt-shortfall forecasts rather than discrete ordinal stages.

The ablation analysis nonetheless shows that the physics-inspired components are internally load-bearing. Removing the gate collapses the AUC from 0.9994 to 0.568—essentially chance level—demonstrating that unmodulated cross-entropy carries almost no discriminatory power. Substituting Shannon entropy with a rolling volatility measure, while keeping the gate, reduces F1 by several percentage points, indicating that informational diversity specifically, not just any modulated risk proxy, contributes meaningful signal. Against purely market-based benchmarks, the architecture dominates decisively: GARCH volatility models achieve an AUC of only 0.622, and network-connectedness measures reach just 0.516. Under matched small-sample conditions—with only 19 positive training examples at the narrower Stage 4+ definition—TENSORnet’s F1 of 99.7 percent also exceeds a gradient-boosted XGBoost benchmark’s 91.8 percent, a result consistent with the documented inability of tree-based learners to extrapolate beyond their training data.

The study’s most robust independent finding is the confirmation of what the author terms the densification paradox. Network theory predicts that under infrastructure stress, cross-asset correlation should rise while entropy falls—a structural inversion that conventional risk metrics, which treat rising correlation as a sign of diversification and stability, would misread as improvement. The empirical evidence is striking: the correlation between network density and entropy is −0.468 at Stage 4+ versus only −0.184 at Stage 0, with p < 0.001. Crucially, a permutation test against a null distribution of randomly matched day subsets places the true Stage 4+ correlation more than five standard deviations beyond the mechanical-artefact mean, confirming that the inversion is a genuine stage-specific phenomenon rather than an artefact of the two measures sharing overlapping rolling-window information. For stress tests calibrated on normal-conditions correlation data, the implication is sobering: infrastructure-induced fragility would be systematically underestimated precisely when it matters most.

Not everything survives honest testing. The study proposed a cross-scale divergence signal as an advance-warning mechanism—a way of detecting metabolic arrest before it manifests in prices. Evaluated on the held-out test period, the signal fired 20 times; 19 of those episodes were followed by no fragile onset within a 30-day window, a 95 percent false alarm rate. The single genuine test-period onset, the May 2022 Stage 4 escalation, was preceded by only a 4-day lead—far shorter than earlier versions of the study had claimed. The author reports this failure plainly, noting that the signal’s strong serial autocorrelation produces clustered, non-independent threshold crossings rather than independent trials, and that a revised specification requiring sustained activation remains future work. A theoretical port-Hamiltonian neural ODE extension, which would ground metabolic arrest in the second law of thermodynamics, is likewise presented as an unvalidated conceptual pathway rather than an empirical result.

The limitations are disclosed with unusual candor. The VIX proxy used for gate calibration is only modestly correlated with infrastructure stages, and an alternative calibration using JSE realised volatility produced a physically implausible, sign-inverted gate that collapsed to chance-level performance—suggesting that finding a defensible throughput proxy is itself nontrivial. Formal data requests to Eskom for operational engineering telemetry and to the JSE for settlement throughput data went unanswered, and both sources carry structural coverage restrictions that would prevent them from covering the study’s calibration window regardless. The bond-return series underlying one attempted trading-strategy backtest was found to be distorted by stale quotes during high-stress periods, and that comparison was withdrawn rather than reported. Concurrent shocks over the study period—the COVID-19 pandemic, energy price volatility, interest-rate cycles—were not separately parcelled out, a limitation the author states explicitly.

What emerges is less a new early-warning system than a methodological exemplar: a demonstration of how to test, rather than assume, whether a physics-inspired architecture adds value beyond simpler alternatives, and how to report the answer honestly when it complicates the original hypothesis. The open dataset—the only publicly available pairing of daily financial returns with official infrastructure stage labels at decade-long resolution—invites replication in other jurisdictions where infrastructure stress and multi-asset markets coexist, from the Texas grid during the February 2021 winter storm to Europe during the 2022 energy crisis. As climate change intensifies infrastructure disruptions worldwide, the question TENSORnet poses—whether markets’ informational fabric frays in thermodynamically predictable ways when the lights go out—will only grow in urgency.

Subject of Research: Thermodynamically motivated entropy-based detection of infrastructure-induced systemic risk in cross-asset financial networks during South Africa's load-shedding crisis

Article Title: TENSORnet: a thermodynamically motivated entropy protocol for infrastructure-induced metabolic arrest detection in cross-asset financial networks

Article References: TENSORnet: a thermodynamically motivated entropy protocol for infrastructure-induced metabolic arrest detection in cross-asset financial networks. (n.d.). https://doi.org/10.3389/fphy.2026.1943577

Image Credits: AI Generated

DOI: 10.3389/fphy.2026.1943577

Keywords: TENSORnet, Landauer's principle, Shannon entropy, systemic risk, load-shedding, Johannesburg Stock Exchange, financial networks, metabolic arrest, densification paradox, physics-informed machine learning, Eskom, macroprudential policy