Main Conference Day TwoWednesday, 18 November 2026 - GMT (Greenwich Mean Time, GMTZ)

We classify return environments using turbulence measures to capture point‑in‑time relationships among CTA managers. Turbulence measures avoid rolling‑window correlations and allow decomposition into magnitude surprise and correlation surprise. Magnitude surprise reflects unusually large return movements across managers, while correlation surprise measures deviations from prevailing correlation structures. Empirically, magnitude surprise is positively related to contemporaneous and short‑term subsequent CTA returns, consistent with managed futures performing well during market stress. Magnitude surprise is also associated with elevated contemporaneous cross‑sectional dispersion, though this effect is not persistent, consistent with transitory market shocks. In contrast, correlation surprise is followed by weaker CTA performance over subsequent months, suggesting that increased disagreement across managers is detrimental to group‑level returns. Overall, the results demonstrate the value of point‑in‑time correlation measures for identifying distinct market regimes and events such as crises.

Private-firm analytics are often limited by incomplete, stale, or inconsistent financial data. Market capitalization is unavailable by definition; other financial variables such as total debt or leverage may be missing or unreliable. This paper develops a scalable framework for forecasting market capitalization and imputing missing financial quantities for private firms. The framework extends the Comparable Company Analysis by calibrating relationships on a large public-firm universe and applying them to private firms through sector, industry, region, and credit-quality information. The resulting estimates provide a internally consistent set of firm-level financial inputs that can support downstream applications such as credit-risk assessment, stress testing and private-credit portfolio analytics. The framework is designed for large-universe applications where manual valuation is infeasible, and missing values cannot simply be ignored.

We study whether directional macro-narrative scores extracted from news can be used to form cross-sectional signals for U.S. equity and factor portfolios.

Using 65 evergreen narratives over 2004-2025, we estimate expanding-window narrative betas and combine them with weekly changes in narrative scores to construct two related strategies: a characteristics-weighted portfolio that ranks assets by exposure to current narrative shifts, and a narrative momentum portfolio that ranks narratives by their recent score trends.

We compare a prompted large language model (LLM) sentiment score with a bag-of-words vector representation (BoW) attention measure and with a non-text benchmark based on principal components from the FRED-MD macro panel.

Across both asset universes considered, the LLM-based signals generate positive returns in both portfolio constructions, while the BoW baseline is generally weaker and the macro benchmark remains competitive.

The evidence is most naturally interpreted as showing that directional narrative measures extracted from text can complement traditional macro signals, rather than as a stand-alone replacement.

• Known modelling challenges of historic VaR and Monte Carlo VaR
• Reactivity challenge to joint projections. Persistence of joint tail behaviour in stress market regimes
• Modelling extreme loss scenarios. EVT distributions. Joint tail behaviour and choice of copula
• Can we derive a ‘universal’ copula within VaR/CVaR frameworks?
• Dissection of high dimensional empirical copula into analytical components
• Market regimes and implications for portfolio modelling