Whitepaper
A Quantitative Framework for Forecasting Market Inflections via Negative-Latency Intelligence
Executive Summary
Bimini is a predictive market intelligence platform designed to identify market inflection points before they are reflected in price by detecting the statistical footprint of asymmetric (non-public) information in live market data. Our core signal category, Ghost Patterns, consists of persistent, statistically significant anomalies that are consistent with informed positioning occurring prior to public dissemination.
A critical point of rigor: we do not claim that positioning causes price movement. In our framework, price discovery is causally downstream of public information release (filings, news, policy actions, authoritative commentary, events) and subsequent market-wide assimilation. Informed positioning is treated as an observable consequence of latent information. Ghost Patterns therefore function as leading indicators of information presence and impending disclosure-driven repricing, not as a causal mechanism.
Bimini’s architecture is intentionally simple at the top level:
Ingest live exchange data and convert it into a proprietary dataset that is richer and more informative than the raw feed.
Train AI on that dataset to detect anomalies and relationships that are difficult or impossible to identify in the flattened exchange stream.
This design improves signal quality, supports confirmation across instruments and markets, and serves as the foundation for a broader multi-source intelligence stack via the Bimini Deep Data Layer.
1. Conceptual Model: From Information to Price
Markets reprice when information becomes broadly available. Before that happens, information often exists asymmetrically and can influence the behavior of informed participants.
Bimini separates three concepts:
Latent information: material facts known by a subset of participants.
Public dissemination: the moment information becomes widely available (news, filings, official actions, credible commentary).
Price discovery: the re-pricing that follows dissemination.
Our working causal chain is:
Latent information → informed positioning (a consequence) → public dissemination → price discovery
This matters operationally: Bimini is built to detect the presence and timing of latent information, using informed positioning as a measurable proxy, and to estimate the probability of disclosure-driven repricing windows.
2. System Architecture (High Level)
Stage I: Proprietary Data Foundation
Bimini ingests live market data directly from exchanges (e.g., NASDAQ) and converts the raw stream into a proprietary dataset that captures more structure and context than the flat feed alone. The goal is not to “generate a signal” immediately, but to create a better analytical substrate for downstream inference.
Stage II: Ghost Pattern Detection
Bimini’s AI models are trained on the proprietary dataset to identify Ghost Patterns; persistent anomalies that are statistically unlikely under normal market behavior and are consistent with informed positioning.
Operationally, a Ghost Pattern is:
A durable, statistically significant deviation in market behavior that is consistent with latent information presence, often appearing before public dissemination and subsequent repricing.
3. Evidence Framework: Temporal Validation and Proper Causation
Bimini validates signals using a consistent chronology that is aligned with a disclosure-centric view of causation:
Pattern emergence: measurable footprint of latent information appears.
Pattern maturation/resolution: positioning completes; the footprint often fades.
Public dissemination: information becomes broadly available.
Price discovery: broader market re-prices.
We interpret Ghost Patterns as indicators of information presence and timing, while maintaining that price discovery is causally downstream of dissemination, not of positioning.
Case Study: NVIDIA (2023) and a Disclosure Catalyst
In early 2023, following the release of ChatGPT, Bimini detected a sustained Ghost Pattern in NVIDIA consistent with informed accumulation well before broad market consensus fully reflected the scale of the coming AI infrastructure cycle.
Notably:
The Ghost Pattern terminated the day before a widely publicized statement by Steve Cohen, founder of Point72, describing AI as a transformational, multi-year opportunity.
Following this public commentary, NVIDIA entered a rapid repricing regime as institutional capital reallocated toward AI-exposed equities, ultimately producing an outsized multi-month move.
Within Bimini’s framework, the clean interpretation is:
The pattern suggests latent information and positioning ahead of dissemination.
The public statement functions as a transmission event that accelerates broad assimilation.
Price discovery follows dissemination, consistent with the system’s causation model.
4. Cross-Market Confirmation: Cross-Asset, Exchange and Border
Although Ghost Patterns are reliable on their own, we believe their reliability will improve variably when they appear coherently across independent market views. The following confirmation signals are subject to internal testing and finalization. The assumption is that “confirmation” is a core robustness filter.
Cross-Asset Confirmation
Latent information frequently expresses through multiple instruments, including:
single names and sector peers
indices and sector ETFs
derivatives (especially options and volatility instruments)
macro-sensitive assets (rates, commodities) when the information is broader in scope
When anomalies align across these instruments, Bimini can increase confidence and infer whether the driver is idiosyncratic, sectoral, or macro.
Cross-Exchange Confirmation
Informed activity may appear first in the venues where it is easiest to deploy size and minimize impact. By monitoring multiple venues and related instruments, Bimini can detect:
where the anomaly originates,
whether it propagates across venues, and
whether it persists across different microstructure environments.
Cross-exchange coherence improves signal integrity and reduces false positives driven by venue-specific artifacts.
Cross-Border Confirmation
Information often emerges unevenly across jurisdictions. Bimini looks for lead–lag and coherence across:
regions and country indices,
cross-listed securities and ADRs,
sector clusters with global linkages (e.g., semis, energy, defense, shipping).
Cross-border confirmation is particularly valuable for geopolitical, regulatory, and supply-chain-driven events, where information diffusion is inherently global.
5. The Bimini Deep Data Layer: A Unified Intelligence Stack
The proprietary market dataset is the Bimini Deep Data Layer. It is extended by layering additional structured and unstructured sources to improve inference quality and interpretability, including:
macro and financial datasets
corporate disclosures (filings, transcripts, guidance)
news flow and narrative propagation
social sentiment and attention dynamics
Government data and policy signals
The purpose is not to treat each source independently. Instead, these streams are integrated and layered on top of the Deep Data Layer to improve Bimini’s ability to:
identify second-order relationships and feedback loops,
connect market microstructure anomalies to plausible drivers, and
form a more complete view of market direction.
6. Strategic Applications (Expanded)
Bimini is designed to function as a negative-latency intelligence overlay that complements existing investment processes.
6.1 Pre-Disclosure Positioning and Better Timing
When Ghost Patterns indicate elevated probability of latent information presence, investors can:
initiate exposure earlier (when liquidity is typically better),
size positions based on signal strength and confirmation breadth, and
structure exposure to match uncertainty (e.g., single-name vs collections).
The core value is improving timing and entry quality, not making headline predictions.
6.2 Risk Management and Early Warning
Signals consistent with informed selling or distribution can serve as early warning indicators for:
idiosyncratic downside risk,
sector de-rating risk, and
emerging event risk.
This supports earlier hedging, exposure reduction, and tighter risk constraints before dissemination.
6.3 Regime and Rotation Detection
Aggregating Ghost Patterns across sectors, factors, regions, and asset classes can identify:
emerging sector rotations,
shifts in macro regime, and
changes in risk appetite
often before they become obvious in broad indices.
6.4 Strategy Conditioning (Making Existing Systems Smarter)
Ghost Patterns can be used to condition or filter existing strategies by identifying periods when:
“information risk” dominates technical mean reversion, or
traditional factor relationships are likely to break.
This can improve timing, reduce drawdowns, and increase risk-adjusted returns.
6.5 Implementation Guidance (The “How,” Not Just the “What”)
Because Bimini evaluates cross-asset and cross-market coherence, it can help determine:
the best instrument to express a view (single name, basket, ETF),
whether derivatives are preferred (convexity), and
where confirmation is strongest.
Implementation quality is often where institutional edge is realized.
Conclusion
Bimini reframes predictive analytics around an information-first model:
detect the market footprint of latent information before dissemination,
validate signals through cross-asset, cross-exchange, and cross-border coherence,
integrate additional data sources through the Bimini Deep Data Layer to improve insight, and
apply the output as an institutional overlay for timing, risk management, regime detection, and strategy enhancement, while maintaining strict conceptual clarity that price discovery is causally downstream of public dissemination.
This approach is designed to anticipate market inflection points by modeling the informational forces that precede them.
