Bernd Johannes Wuebben
Finance · Mathematics · Theoretical Physics
A price is a claim about the future that somebody was willing to fund. Most of my work asks what can be known about such claims: whether a model that earns is a model that explains, where compensation for risk ends and compensation for absorbing other people’s constraints begins, and why the part of the cross-section that pays is so often the part nobody can name.
These are one question approached from two sides. Performance and identification are governed by the same covariance spectrum, and they are complementary rather than aligned: a high-Sharpe stochastic discount factor need not identify priced risk, and the alpha tends to live in the statistically unnameable bulk, at a floor set by statistical power rather than by anyone’s skill.
That pushes outward rather than inward — toward who is on the other side of a trade and what they are solving for, toward markets as aggregation mechanisms that sometimes aggregate belief and sometimes only aggregate attention, and toward the possibility that the discretionary–quantitative divide is not a matter of technique but of what a person is willing to accept as warrant for a belief.
Separately, and for its own sake, I work across geometric topology, symplectic geometry, geometric analysis, and algebraic geometry, with particular interests in four-manifolds, gauge and Floer theory, mirror symmetry, and moduli spaces.
A newer strand asks what large-scale artificial intelligence does to markets, institutions, and the balance of power between states.
I trained as a mathematician at Cornell, where my doctoral research focused on the mathematical foundations of superstring theory.
Day to day, I run the systematic investing business at one of the world’s largest asset managers.
For in the end, what we call awareness may simply be the most efficient way for entropy to remember itself.
Research interests over the years
- Asset pricing theory — stochastic discount factors and the statistics of high-dimensional return panels; the spectral structure of the cross-section, and whether a high-Sharpe model identifies priced risk or only describes it; the equilibrium level of factor compensation; information-theoretic foundations, from Kelly and Grinold to portfolios as codes; the demand side — inelastic markets, intermediary capital, who is on the other side; and how factors are born, crowd, and decay.
- AI and the machine economy — institutional cognition as competitive advantage, prediction markets as informational infrastructure, and what disciplined discovery looks like once hypotheses are free and only validation is scarce.
- Geometric topology — exotic smooth structures on four-manifolds and the reduction of classical existence questions to finite, machine-certified computations.
- Gauge theory, Floer theory, and moduli spaces — instanton knot homology, Donaldson and Seiberg–Witten theory, SO(3)-monopole compactification and gluing, and operations in the Atiyah–Floer comparison; more broadly, the architecture by which equations become compactified moduli spaces and algebraic structures, and when that architecture can be run in reverse.
- Algebraic geometry — mirror symmetry, slope stability of tangent bundles on toric Fano varieties, deformations and smoothability of Calabi–Yau threefolds, and open FJRW theory.
- Theoretical physics — M-theory compactifications, five-dimensional supersymmetric fixed points and Higgs branches, Calabi–Yau extremal transitions, quantum gravity, holography, black hole thermodynamics and the information paradox, quantum information, random-matrix theory and free probability, critical phenomena and universality.
- International relations — how large-scale AI deployment re-centres physical constraint as a source of power, and how delegating decisions to machines erodes deterrence by blurring authorship and intent.