Research
Agenda.
Our methodology bridges the gap between quantitative finance and sociological inquiry.
01 — The Thesis
Price is a construct.
Value is a graph.
Traditional hedonic regression fails in illiquid, prestige-driven markets. We operationalize the Noll Framework to decode the sociological and institutional drivers of asset appreciation.
Active Price Management
Quantifying the "marketing premium" extracted by auction houses through catalog placement and sentiment analysis.
Institutional Gatekeeping
Tracking "Canonization" via museum exhibitions and critical reviews using NLP and causal discovery.
The Winner's Curse
Modeling bidder behavior and liquidity dry-ups to signal "Do Not Bid" when market dynamics overheat.
Provenance Networks
Detecting wash trading and "prestige transfer" through Heterogeneous Graph Neural Networks.
02 — The Engine
Deep Tech for Alternative Assets
We don't just predict what happens; we understand why. By moving from correlation to causality, we answer the counterfactuals that define alpha.
Our Structural Causal Models (SCM) and Augmented Time Series architectures allow us to answer counterfactuals: "What would the price be if this artist hadn't had a MoMA retrospective?" This moves us from correlation (Seeing) to intervention (Doing).
Our Multi-Agent Tool-augmented Multi-Modal Causal Discovery system deploys LLM agents to scrape unstructured text (catalogs, archives) and refine our causal graphs, creating a proprietary "Living SCM" of the art market.
We simulate auction rooms with heterogeneous agents (Collectors vs. Speculators) using Reinforcement Learning to identify liquidity risks and optimal bidding strategies in low-data environments.
Value is path-dependent. We map the "invisible" connections of provenance using Graph Neural Networks to detect anomalies, wash trading, and prestige transfer across generations.