Beta Analysis

Risk beta regression
Bottom Line

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60-day rolling beta relative to SPY across the full universe, identifying systematic risk concentration and diversification opportunities.

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Avg Beta
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Highest Beta
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Lowest Beta
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Beta Dispersion
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Universe Size
Rolling Beta (60-Day) vs SPY benchmark
Beta Snapshot Current beta values, sorted
Beta vs Total Return Each dot = one ticker
Summary Table Click column headers to sort
Ticker Current Beta Avg Beta Trend Total Return

Overview

Beta measures a stock's sensitivity to market movements. A beta of 1.0 means the stock moves in lockstep with the benchmark (SPY). Values above 1.0 indicate higher systematic risk (amplified market moves), while values below 1.0 suggest defensive characteristics.

This tool computes 60-day rolling betas for 57 tickers against the SPY benchmark over 252 trading days. The rolling window captures regime changes in systematic risk exposure that static estimates miss.

Key Concepts

  • Rolling Beta — 60-day OLS regression slope of stock returns vs benchmark returns, updated daily
  • Beta Dispersion — Standard deviation of current betas across the universe; high dispersion signals divergent risk profiles
  • Beta Trend — Comparison of current beta to 20-day trailing average; rising beta means increasing systematic risk

Strengths

  • Captures time-varying systematic risk
  • Rolling window adapts to regime changes
  • Scatter plot reveals risk-return tradeoff
  • Multiple timeframe resampling support

Limitations

  • 60-day window may lag structural breaks
  • Single-factor model ignores sector/style effects
  • Assumes linear relationship between stock and market
  • Historical beta is not a forward-looking predictor

How to Read the Charts

  1. Rolling Beta Chart — Each line represents one ticker's 60-day rolling beta over time. The dashed horizontal line at 1.0 marks the market boundary. Lines above 1.0 are aggressive; below are defensive. Convergence suggests regime uniformity, divergence signals dispersion.
  2. Beta Snapshot Bar — A horizontal bar for every ticker, sorted by current beta value. Red bars indicate high-beta stocks (above 1.2), blue bars are low-beta (below 0.8), and gray bars are market-neutral (0.8 to 1.2). Use this to quickly identify risk outliers.
  3. Beta vs Return Scatter — X-axis is beta, Y-axis is total return over the period. The ideal position is bottom-left (low risk, positive return) or upper-left (low risk, high return). Stocks in the upper-right quadrant delivered returns but with amplified volatility.
  4. Summary Table — Sortable by any column. The trend column compares recent beta to its 20-day average. Rising trends are shown in red (increasing risk), falling in green (declining risk).

Mathematical Framework

Beta is the slope coefficient from an ordinary least squares (OLS) regression of asset returns on benchmark returns over a rolling window.

Single-Factor Model
$$r_{i,t} = \alpha_i + \beta_i \cdot r_{m,t} + \varepsilon_{i,t}$$

where $r_{i,t}$ is the stock return, $r_{m,t}$ is the benchmark return, $\beta_i$ is the sensitivity coefficient, and $\varepsilon_{i,t}$ is the idiosyncratic residual.

Rolling Beta Estimate
$$\hat{\beta}_{i,t} = \frac{\sum_{k=t-W+1}^{t}(r_{i,k} - \bar{r}_i)(r_{m,k} - \bar{r}_m)}{\sum_{k=t-W+1}^{t}(r_{m,k} - \bar{r}_m)^2}$$

where $W = 60$ is the rolling window width in trading days.

Beta Dispersion
$$\sigma_\beta = \sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(\hat{\beta}_{i,T} - \bar{\beta}_T)^2}$$

Beta dispersion measures how spread out systematic risk exposures are across the universe. High dispersion implies a mixed regime where some stocks are highly sensitive to the market while others are decoupled.