Methodology
Midas Edge brings institutional-style portfolio analytics to individual investors. Our methodology is rooted in academic research and established practices used by quantitative asset managers.
Our Approach
We believe individual investors deserve analytical context comparable to the tooling institutional investors use. Our factor model is based on the Barra USE4 methodology, adapted for accessibility without sacrificing rigor.
Every metric we compute has a clear definition, documented formula, and transparent interpretation. No black boxes.
Documentation
19-Factor Risk Model
Barra-style factor decomposition for understanding portfolio risk exposures across market, style, and sector dimensions.
Holder IQ Scoring
Quantifying institutional investor skill from 13F filings to identify smart money signals and crowding risk.
Insider Signal Analysis
Scoring insider transactions from SEC Forms 3, 4, and 5 to detect informative vs. routine trading patterns.
Attention Queue Algorithm
Priority scoring system that surfaces positions requiring attention based on factor drift, signals, and events.
Risk Forecast Validation
How IC, bias ratio, and 95% VaR coverage are monitored against realized outcomes and freshness windows.
Anytime-Valid Evidence
How e-values keep continuously monitored research claims statistically valid as new filings and cohorts arrive.
Tax-Loss Harvesting Guardrails
How Midas Edge identifies unrealized losses, suppresses wash-sale-risk suggestions, and documents tax workflow limits.
Red Flags Detection
Automated monitoring of SEC filings for disclosure events that historically correlate with material stock underperformance.
Portfolio Health Score
Composite 0-100 score summarizing portfolio quality across factor balance, smart money alignment, and risk factors.
Method Version Changelog
Concise changes to the model, scoring rules, and prompt templates behind user-facing analytics.
Portfolio health score
health-score:2.0
Company-specific signals exclude funds, cash and unresolved company identities. History and references compare only the same persisted computation method.
Affects: portfolio_health, dashboard, score_detail
Factor model
v1.2.0
Production Barra-style model pinned for portfolio and ticker analytics.
Affects: factor_exposures, risk_attribution, performance_attribution, portfolio_health
Holder IQ scoring
holder-iq-route:1.0
Initial route-level Holder IQ scoring disclosure with 13F freshness context.
Affects: holder_iq, smart_money, portfolio_health
Insider signal rules
insider-route:1.0
Initial insider-signal scoring disclosure with role and transaction context.
Affects: insider_signals, portfolio_health, alerts
Portfolio health score
health-score:1.0
Composite score disclosure pinned to the current grade-threshold configuration.
Affects: portfolio_health, dashboard, score_detail
Factor model
v1.1.0
Expanded style-factor coverage and stricter provenance checks.
Affects: factor_exposures, risk_attribution, performance_attribution, portfolio_health
Metric Glossary
Short, action-oriented definitions for the core metrics we surface throughout the dashboard and reports.
Insider Activity
Insiders
Recent buying or selling by corporate executives, directors, and major shareholders as reported in SEC Form 4 filings.
Why it matters: Insiders have deep knowledge of their company; unusual buying often precedes positive news, while heavy selling can signal concerns.
Holder IQ Percentile
Smart Money
Percentile rank (0-100) of institutional holder quality based on Holder IQ scores.
Why it matters: Higher percentiles signal overlap with consistently skilled managers; sharp drops can flag crowding unwind risk.
Smart Money Momentum
Smart Money
Direction of net changes by top-decile managers (accumulating, holding, or distributing).
Why it matters: Shows whether skilled managers are adding to or exiting a position.
Exit Pressure
Smart Money
Weighted rate of smart-money exits based on net share reductions among top holders.
Why it matters: Rising exit pressure often precedes volatility or crowded unwind scenarios.
DP Z-Score
Crowding
Z-score of disproportionate popularity using log-odds for over/under-ownership within a segment.
Why it matters: Positive values indicate segment crowding or conviction; negative values indicate avoidance.
DP Odds Ratio
Crowding
Odds ratio of a filer segment holding the security versus the rest of the market.
Why it matters: Highlights when a specific cohort disproportionately favors a security.
Crowding Score
Crowding
Normalized crowding risk score (0-100) derived from ownership concentration and smart-money overlap.
Why it matters: Higher scores indicate crowded ownership and elevated unwind risk.
Crowding (HHI)
Crowding
Herfindahl-Hirschman Index of institutional ownership concentration (0-10,000 scale).
Why it matters: High concentration raises risk of forced selling and correlated exits.
Churn Rate
Flows
Quarterly holder turnover: (entries + exits) / (2 * average holders).
Why it matters: High churn suggests unstable ownership and higher price volatility.
Whale Flow
Flows
Quarter-over-quarter change in exposure among the largest filers.
Why it matters: Large whale flows can materially impact liquidity and price pressure.
Value vs Presence Divergence
Flows
Stealth score capturing when position value changes faster than holder count.
Why it matters: Signals stealth accumulation or distribution even when holder counts look stable.
Style Drift
Factors
Change in factor exposures over time relative to prior snapshots or benchmarks.
Why it matters: Large drift means the portfolio is behaving differently than intended.
Factor Imbalance
Factors
Degree of tilt away from neutral or benchmark exposures across the 19-factor model.
Why it matters: Imbalances drive unintended risk and can dominate performance.
Portfolio Health Score
Portfolio
Composite 0-100 score summarizing factor balance, diversification, smart-money alignment, and risk flags.
Why it matters: Use the score as a triage aid, not a trade signal: low scores tell you where to inspect exposure, concentration, or risk first.
Attention Score
Portfolio
Priority score from 0-100 that ranks positions by signal severity, position impact, recency, and actionability.
Why it matters: Higher scores help you focus review time on positions with the strongest combination of risk, change, and portfolio relevance.
Data Freshness
Portfolio
How recently the underlying brokerage, market, filing, or factor data was refreshed for the metric you are viewing.
Why it matters: Freshness tells you whether a number reflects current portfolio state or a stale upstream dataset that should be refreshed or treated cautiously.
AI Confidence
AI
0-100 estimate of signal consistency across the data sources used to generate an AI-assisted explanation.
Why it matters: Confidence helps calibrate how much context to demand before acting; it is not a probability that the recommendation is correct.
Information Coefficient
Validation
Correlation between a model signal and later realized returns over the validation window.
Why it matters: Positive IC means the signal has historically ranked future outcomes in the intended direction; weak or negative IC lowers trust in that signal.
Bias Ratio
Validation
Calibration check comparing realized outcomes against the model's expected distribution or directional claim.
Why it matters: Large bias ratios indicate the model may be systematically overstating or understating an effect and should be treated conservatively.
95% VaR Coverage
Validation
Share of observations that fall inside the model's 95% value-at-risk or prediction interval.
Why it matters: Coverage far below 95% means risk is understated; coverage far above 95% may mean the model is too conservative to be useful.
R-Squared
Validation
Fraction of return variation explained by the factor model or validation regression.
Why it matters: Higher R-squared means the model explains more of the observed movement; low values imply more idiosyncratic or missing drivers.
Broad market
Factors
The stock model's common market component, with a base loading of 1 for each stock. Portfolio loadings also reflect position direction and any supported leverage adjustment. This is separate from stocks' historical beta.
Why it matters: Separates the common market component from differences in historical market sensitivity. A factor loading is not a percentage of your portfolio or a forecast return.
Size (Market Cap)
Factors
Company size measured by log market capitalization. Positive values indicate large-cap tilt, negative values indicate small-cap tilt.
Why it matters: Small caps historically outperform but with higher volatility. Size tilts drive meaningful return differences over time.
Size Non-Linear
Factors
Captures non-linear size effects that affect the very smallest and largest companies differently.
Why it matters: Extreme size exposures (micro-caps or mega-caps) behave differently than mid-caps. This factor captures those edge effects.
Book-to-Price
Factors
Value factor measuring book value relative to market price. Higher values indicate value stocks, lower values indicate growth stocks.
Why it matters: Value vs growth is one of the most persistent factor tilts. Value stocks often outperform long-term but can underperform for extended periods.
Earnings Yield
Factors
Earnings relative to price (inverse of P/E ratio). Higher values indicate cheaper stocks with more earnings per dollar invested.
Why it matters: High earnings yield stocks are often undervalued and may offer better risk-adjusted returns. Low yields signal growth expectations.
Dividend Yield
Factors
Annual dividends relative to share price. Captures income orientation and often correlates with value and quality factors.
Why it matters: Dividend stocks provide income and often exhibit lower volatility. High dividend tilts can indicate defensive positioning.
Growth
Factors
Historical and forecasted earnings growth rate. Captures exposure to companies with expanding earnings.
Why it matters: Growth stocks can deliver outsized returns but often trade at premium valuations, creating downside risk if growth disappoints.
Leverage
Factors
Financial leverage measured by debt-to-equity ratio. Higher values indicate companies with more debt financing.
Why it matters: Leverage amplifies both gains and losses. High leverage portfolios face greater risk during economic stress.
Historical Beta
Factors
The model's standardized historical-beta characteristic, based on how stock returns have moved with the market benchmark. This differs from the common market component.
Why it matters: Historical beta captures how a stock has moved with the market over time. It's a key input for estimating expected returns and portfolio risk.
Beta Non-Linear
Factors
Captures non-linear beta effects where very high or very low beta stocks behave differently than expected.
Why it matters: Extreme beta stocks (very defensive or very aggressive) have unique risk characteristics not captured by linear beta.
Residual Volatility
Factors
Idiosyncratic (stock-specific) volatility not explained by market or factor movements.
Why it matters: High residual volatility means more stock-specific risk. These positions need more attention and may require larger position limits.
Liquidity
Factors
Trading volume and ease of execution. Low liquidity stocks have wider spreads and higher market impact costs.
Why it matters: Illiquid positions are harder to exit during stress. High liquidity exposure typically indicates capacity for larger trades.
3-Week Momentum
Factors
Short-term price momentum measured over 3 weeks. Positive values indicate recent winners.
Why it matters: Short-term momentum often reverses (mean reversion), making high exposure risky. Useful for tactical timing.
11-Month Momentum
Factors
Intermediate-term price momentum, with recent weeks excluded from the measurement to reduce short-term reversal effects.
Why it matters: Momentum is one of the strongest and most persistent factors. Winners tend to keep winning over 6-12 month horizons.
5-Day Reversal
Factors
Recent price reversal, using the negative of a five-day price return before the model date. This factor is not your portfolio's five-day return.
Why it matters: Distinguishes exposure to recent price declines from exposure to recent winners. Reversal is a model characteristic, not a promise that a price move will reverse.
Short Interest
Factors
Percentage of shares sold short. High short interest indicates bearish sentiment from sophisticated investors.
Why it matters: Heavily shorted stocks can squeeze higher or validate bear cases. Understanding short exposure helps manage crowding risk.
Hedge Fund Ownership
Factors
Percentage of shares owned by hedge funds and sophisticated institutional investors.
Why it matters: High hedge fund ownership creates crowding risk - when hedge funds exit together, selling pressure can be severe.
Passive Ownership
Factors
Percentage of shares owned by passive and index funds that track benchmarks.
Why it matters: High passive ownership provides stable demand but can reduce price discovery. Index inclusion/exclusion events create significant flows.
Share Issuance
Factors
Share issuance momentum: 70% the 12-month log change in float shares plus 30% issuance relative to the change in market cap, z-scored across the universe. Split periods are masked and buybacks count as zero; higher means more dilution.
Why it matters: Issuance transfers value from existing holders, and net issuers have historically lagged companies that shrink their share count.
Important Disclaimer
The methodologies described here are for educational and research purposes. Factor exposures, alpha estimates, and signal scores are analytical tools, not trading recommendations. Past performance of any factor or signal does not guarantee future results. Always consult a qualified financial advisor before making investment decisions.