Beta
Beta Analysis
Key Observations
- High-beta / aggressive (β = 1.36): AMZN amplifies SPY — a 1% benchmark move maps to ~1.36%, cutting both ways.
- Benchmark explains 44% of AMZN's daily variance (R² = 0.44); a meaningful share is stock-specific, so treat beta as approximate.
- Forward-looking adjusted beta ≈ 1.24 (Blume: pulls the raw β one-third of the way toward the market's 1.0).
- α ≈ +1.0%/yr: after stripping out market exposure, AMZN outperformed SPY over the window (extrapolated from the daily intercept; not a forecast).
- Annualized volatility: AMZN 34.1% vs SPY 16.6% — 2.05× as volatile (more than the benchmark).
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
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Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
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Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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