Correlations Analysis
Correlation Analytics
Key Observations
- AFRM is most correlated with $GSPC (r = 0.59, β = 2.56), the closest co-mover in this set — it explains about 35% of AFRM daily moves (R² = 0.35).
- $STI is effectively uncorrelated (r = -0.07), the best diversifier against AFRM here.
- Across the set (all Equity Indices), average correlation to AFRM is 0.18.
- Overall linkage is moderate (avg |r| = 0.20 across 19 instruments).
- Versus $GSPC, Pearson r and Spearman ρ agree (0.59 vs 0.55), so the relationship is broadly linear and the β is dependable.
- AFRM and $GSPC move contemporaneously (correlation is strongest same-day) — no usable lead-lag timing within ±3 days.
- Correlation to $GSPC tightens under stress: r = 0.47 in calm markets (low VIX) vs r = 0.65 in high-volatility regimes — diversification weakens exactly when you need it.
| Symbol | Name | 1M | 3M | 6M | 1Y | Trend | β | R² |
|---|---|---|---|---|---|---|---|---|
| $GSPC | S&P 500 | 0.57 | 0.66 | 0.62 | 0.55 | ▲ | 2.56 | 0.35 |
| $NDX | Nasdaq 100 | 0.30 | 0.54 | 0.53 | 0.50 | ≈ | 1.80 | 0.31 |
| $DJI | Dow Jones Industrial Average | 0.61 | 0.64 | 0.57 | 0.49 | ▲ | 2.54 | 0.29 |
| Upgrade to PRO plan! to see all 19 instruments and every window. | ||||||||
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.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
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.
Or start with