How It Works

A simple, transparent explanation of how this tool analyzes companies, scores financial health, and builds smarter portfolios.

1. Overview

This tool helps you understand any company in seconds — even if you're busy or new to investing.

It analyzes financial statements, compares companies to their sector peers, and uses proven quantitative models to forecast performance and build balanced portfolios.

Everything is explained in plain English so you always know why a company looks strong or risky.

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2. Financial Health Score (0–100)

Every company receives a 0–100 score that summarizes its overall financial strength.

The score is based on five major factors:

  • Profitability: How efficiently the company turns revenue into profit. Metrics used: ROE, Profit Margin.
  • Growth: Whether revenue and earnings are expanding or shrinking. Metrics used: Revenue Growth, EPS Growth.
  • Financial Strength: How much debt the company carries and whether it can handle it. Metrics used: Debt/Equity.
  • Liquidity: Whether the company can pay short-term bills. Metrics used: Current Ratio.
  • Risk Profile: How volatile the stock is compared to the market. Metrics used: Beta.

Each metric is compared to the company's sector, so tech stocks are judged against tech stocks, energy against energy, and so on. This makes the score fair and accurate.

Why it matters: You see the quality behind each stock, not just the price.
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3. Percentile Ranks

To make analysis easy, each metric is converted into a percentile rank:

  • 100th percentile → Best in sector
  • 75th percentile → Strong
  • 50th percentile → Average
  • 25th percentile → Weak
  • 2nd percentile → Very weak

This helps you instantly see where a company stands.

Example:

  • Profit Margin — 100th → "Top of the sector. Very efficient at turning revenue into profit."
  • EPS Growth — 2nd → "Very weak earnings growth compared to peers."
Why it matters: Percentiles make comparisons simple, intuitive, and fair across all sectors.
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4. Valuation, Cash Flow & Earnings Quality

These badges give quick signals about the company's fundamentals.

🟢 Valuation

Shows whether the stock is reasonably priced relative to its fundamentals.

  • 🟢 Good → Fairly priced
  • 🟡 Neutral → Somewhat expensive
  • 🔴 Poor → Overpriced vs peers

💵 Cash Flow

Indicates how strong and consistent the company's cash generation is.

  • 🟢 Strong → Reliable cash generation
  • 🔴 Weak → Inconsistent or negative cash flow

📘 Earnings Quality

Shows how reliable earnings are based on cash flow and accounting quality.

  • 🟢 Strong → Earnings backed by cash
  • 🟡 Moderate → Some caution needed
  • 🔴 Weak → Earnings may not be reliable
Why it matters: These badges simplify complex accounting concepts into something anyone can understand.
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5. Forecast Based on Historical Windows

You can choose different historical windows to analyze performance:

  • 1 year (tactical) — Recent performance
  • 3 years (balanced) — Medium-term trends
  • 5 years (default) — Long-term stability
  • 10 years (strategic) — Full market cycles

The tool analyzes how the stock performed in each window to help you understand:

  • Volatility
  • Drawdowns
  • Long-term trends
  • Risk vs reward
Why it matters: This gives you a realistic view of how the stock behaves in different market conditions.
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6. Portfolio Optimization

When you add multiple tickers, the tool builds a balanced portfolio using three proven models:

1. Modern Portfolio Theory (MPT)

Finds the portfolio with the best risk-adjusted return (Max-Sharpe).

Read the full MPT model deep dive →

2. Resampled Optimization

Runs the optimization many times with slightly varied data to avoid extreme weights. This produces a more stable, realistic portfolio.

See how resampling stabilizes portfolios →

3. Black-Litterman Model

Blends market equilibrium returns with historical data to avoid unrealistic allocations.

Why it matters: You don't need to understand the math — the tool explains everything in simple language.
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7. Explainability Layer

Every portfolio comes with a clear breakdown of why the optimizer chose certain weights:

  • Top return drivers
  • Risk reducers
  • Black-Litterman adjustments
  • Resampling effects
  • Factor-level impacts
  • Metric-level contributions
Why it matters: This removes the "black box" feeling and helps you make safer, more informed decisions.
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8. Data Quality

Some companies have missing or incomplete financial data.

We label them as:

  • Complete — All metrics available
  • Partial: missing X, Y — Some metrics unavailable
  • Sparse: many metrics missing — Significant data gaps
Why it matters: This helps you judge the reliability of the score.
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9. Disclaimer

This tool is for educational purposes only and does not provide investment advice.

Always consult with a qualified financial advisor before making investment decisions. Past performance does not guarantee future results. All investments carry risk, including potential loss of principal.

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