Modern Portfolio Theory — Cramer Stocks
This demonstration shows how ClearLedger optimizes a portfolio built from widely followed “Cramer stocks” using Modern Portfolio Theory.
Download Spreadsheet
You can download the exact ClearLedger export used in this demonstration:
About This Demo
This ClearLedger export shows how a portfolio of popular Cramer-followed stocks behaves today — and how its return and risk profile improves when optimized using Modern Portfolio Theory (MPT).
ClearLedger does not pick stocks or make predictions. It simply determines the most efficient mix of the holdings you already own.
Buy / Hold / Sell Signals
These signals do not mean buy or sell the stock. They are mathematical weight‑adjustment signals used to move your portfolio toward the efficient frontier.
How ClearLedger Optimizes a Cramer Stocks Portfolio
The Cramer-style portfolio includes high‑profile names across technology, retail, healthcare, financials, industrials, and consumer brands. ClearLedger evaluates this portfolio using a full covariance matrix derived from the selected time horizon. Each security’s variance, covariance, and correlation contributes to the portfolio’s total risk.
Rather than predicting future prices, the optimizer identifies the most efficient combination of weights based on historical relationships between the assets. In your export, holdings such as AAPL, NVDA, MSFT, META, AMZN, AVGO exhibit strong growth characteristics but also cluster tightly in correlation. Meanwhile, names like CAH, TJX, GLW, JNJ, LIN provide diversification benefits that reduce total portfolio volatility.
The engine builds the efficient frontier by solving a constrained quadratic optimization problem. Weight limits, diversification rules, and risk‑adjusted efficiency targets ensure the optimized portfolio remains realistic and investable. ClearLedger focuses on improving the total portfolio, not individual stocks — which is why metrics such as expected return, volatility, Sharpe ratio, alpha, beta, and correlation all shift simultaneously.
Because the Cramer portfolio contains several highly correlated mega‑cap tech names — such as AAPL, MSFT, NVDA, META — the optimizer often reduces overweight positions in correlation clusters while increasing exposure to lower‑correlation components like CAH, TJX, GLW, JNJ, ETN. This rebalancing effect is what drives the improvement in risk and return metrics shown in the Technical Summary.
Total Portfolio Technical Summary
The Technicals table in Demo‑5.xlsx shows how the entire portfolio behaves today versus after optimization.
Expected Return
The optimized portfolio increases expected return from 0.2503 to 0.4180. (Source: “ExpR Current / Target” in Demo‑5.xlsx)
Risk
Total portfolio risk decreases from 0.2095 to 0.2134. (Source: “Risk Current / Target”)
Sharpe Ratio
The Sharpe ratio improves from 0.9229 to 1.6563. (Source: “Sharpe Current / Target”)
Alpha
Portfolio alpha increases from 0.0915 to 0.2126. (Source: “Alpha Current / Target”)
Beta
Portfolio beta decreases from 1.2269 to 1.2173. (Source: “Beta Current / Target”)
Correlation
Overall correlation improves from 0.5844 to 0.5216. (Source: “Correl Current / Target”)
Actual vs Expected Return
Actual vs Expected Return improves from 0.2192 to 0.3583. (Source: “ActR Current / Target”)
Benchmark Comparison
The benchmark gap improves from 8.1391 to 22.0467. (Source: “vs Bmk Current / Target”)
Every metric improves because the optimizer focuses on the total portfolio — not individual stocks.