Market sensitivity over time · β=1 moves with S&P · β>1 amplifies · β<0 inverts
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Risk-Reward Map
Volatility vs Return
1-year annualised · Asset vs S&P 500 benchmark
Annual Returns Calendar
Calendarised Performance
Year-by-year return · Full history
Monthly Returns Heatmap
Monthly Return Heatmap
Each cell = one calendar month · Colour intensity = magnitude
Rolling Returns
Rolling Return Windows
1M · 3M · 6M rolling windows
1-Month
3-Month
6-Month
Performance Attribution vs S&P 500
Alpha · Information Ratio · Capture Ratios
1-year daily returns · Benchmarked against S&P 500 · Skill vs beta decomposition
Monthly Return Seasonality
Average Return by Calendar Month
Historical mean ± 1σ · Green = historically positive · Red = historically negative
Positive avg month
Negative avg month
±1σ range
⚠ Seasonality patterns are based on limited samples (typically 5–15 years = 5–15 data points per month). Statistical significance is low. The ±1σ bands indicate variability — wide bands suggest unreliable averages. Do not rely on seasonality alone for timing decisions.
Advanced Analysis4 modules
Interactive Stress-Testing · What-If Module
Scenario Fragility Engine
Drag sliders to stress-test assumptions. CVaR and Monte Carlo metrics update in real-time to reveal mathematical fragility under adverse regimes.
Vol Spike+0%
Annualised volatility multiplier · 0% → +100%
Rate Hike (bps)+0
Additional basis points · discount rate impact on CAGR
Autocorrelation Regularisation — LW-style shrinkage on single-asset lag matrix
Noisy vs. Regularised Lag Matrix
Single-asset return autocorrelation at lags 1–10 — sample (noisy) vs. shrinkage-regularised. Note: this is not a multi-asset portfolio covariance.
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Sample Cond.
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Shrunk Cond.
Sample Lag Matrix Noisy · High Cond. No.
LowHigh
Regularised Lag Matrix Shrunk · Stable
LowHigh
In plain terms: Sample autocorrelations at short lags are noisy in finite samples — shrinkage blends the raw lag estimates toward a stable target, producing a regularised matrix with lower condition number that's safer to invert downstream.
Ledoit-Wolf-style shrinkage pulls the sample lag matrix towards a structured target (scaled identity), reducing estimation error in finite samples. Shrinkage intensity α chosen by analytical formula min‖S−T‖²/(n·‖S‖²). Lower condition number = more numerically stable inversion. Methodological honesty: the original Ledoit & Wolf (2004) estimator is defined on a multi-asset sample covariance matrix; applying the same shrinkage operator to a single-asset lag matrix is a regularisation borrowed from the same family, not a portfolio covariance. True multi-asset LW covariance is applied in the portfolio dashboard.
Current multiple · Sector median · Est. hist. avg · Relative verdict · Sector data: 2024-Q4 (estimated)
Metric
This Asset
Sector Median
Est. hist. avg
vs Sector
Load an asset to see valuation multiples
Relative Valuation Context
Position vs Sector & History
Visual positioning — where does this asset sit relative to peers?
Rolling P/E Ratio History
A.L. Capital · Valuation History
Trailing P/E Ratio — Historical Range
Quarterly TTM EPS · Mean ± 1σ · Self-relative valuation
Price Target & Fair Value
Analyst Price Target Model
Street consensus · Bear / Base / Bull scenarios
DCF Intrinsic Value Model
2-Stage Discounted Cash Flow
Adjust assumptions · Live recalculation
EPS (FWD)
Growth Yr 1-5
%
Terminal Growth
%
Ke / Discount Rate
%
⚠ DCF is highly sensitive to terminal assumptions. Bear = ½ stage-1 growth + 2% higher Ke. Bull = 1.5× growth + 1% lower Ke. Illustrative only — not investment advice.
DCF Advisory Note
Wealth Projection — Probability Fan Chart
50% Confidence
P25–P75 · Central outcome range · Gold
75% Confidence
P12.5–P87.5 · Amber probability cone
90% Confidence
P5–P95 · Outer tails · Red
Monte Carlo Wealth Projection
GBM simulation · 3,000 scenarios · Probability fan chart
⚠ GBM simulation. Drift options: Historical CAGR = past return extrapolated (may overstate for momentum stocks); Risk-Free Rate = conservative baseline; Zero = pure volatility scenario. GBM assumes constant volatility and log-normal returns — actual distributions exhibit fat tails and volatility clustering. Not a forecast.
Methodology: Each high-volume session (RVOL ≥ 1.5× 20-day ADV) is scored as
(Close − Low) ÷ (High − Low) — the closing-range bar score from Volume Spread Analysis. A value near 1.0 indicates the stock closed near its intraday high on elevated volume — consistent with passive institutional accumulation absorbing supply at the bid. A value near 0.0 indicates fade from intraday highs — consistent with distribution into late-session retail demand. Method: Wyckoff (1937, The Richard D. Wyckoff Method of Trading and Investing in Stocks) and Williams (1993, The Undeclared Secrets That Drive the Stock Market, Volume Spread Analysis). Threshold 1.5× RVOL isolates sessions with meaningful capital participation; 20-session lookback balances signal stability against responsiveness to regime change.
MACD (12, 26, 9)
Histogram · Signal · MACD line
Bollinger Bands (20, ±2σ)
Upper/Lower = mean ± 2 std devs
Relative Strength vs S&P 500
Price Ratio Chart
Asset ÷ S&P 500 · Both indexed to 100 · Rising line = outperformance
Asset
S&P 500
RS Ratio
Financial Overview
Total Revenue
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Annual · TTM
Net Income
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Annual · TTM
Free Cash Flow
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Annual · Operating – Capex
Earnings & EPS Growth
A.L. Capital · Financial momentum
Earnings power through time
Quarterly and annual EPS / earnings trajectory with a synchronized growth line.
Key Statistics
Valuation & Fundamentals
Source: Yahoo Finance · Most recent available data
52W LowCurrent52W High
Near LowMid-RangeNear High
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Factor Quality Scores · Piotroski-style (4 of 9 components) · Sector-relative value · Internal estimates
Income & Cash Flow
Financial Summary
Most recent annual data · Yahoo Finance
Business Summary
Private Institutional Advisory
You've identified the risk. Now architect the portfolio.
Single-asset analysis reveals the opportunity. The Strategic Session applies Black-Litterman portfolio optimisation to your complete holdings — quantifying true portfolio risk, correlations, and optimal allocation weights. The tools used by Tier-1 wealth management, applied to your capital.
Independent equity research tools for self-directed investors
Strategic Session
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