Modern Portfolio Theory — Dow 30

This demonstration shows how ClearLedger optimizes a Dow 30 equity portfolio using Modern Portfolio Theory.

Download Spreadsheet

You can download the exact ClearLedger export used in this demonstration:

• Download Dow 30 Demo Spreadsheet (XLSX)

About This Demo

This ClearLedger export shows how a Dow 30 portfolio’s return and risk profile changes when its weights are optimized using Modern Portfolio Theory (MPT). ClearLedger does not select stocks or make predictions — it simply determines the most efficient mix of the holdings you already own.

ClearLedger adjusts weights, not securities. Optimization increases expected return, reduces risk, or improves both simultaneously.

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 Performs Dow 30 Optimization

The Dow 30 contains large‑cap U.S. companies with diverse sector exposure — industrials, healthcare, technology, financials, and consumer staples. 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. For example, in your export, GS, CAT, MSFT, AMGN, UNH, TRV, V, JPM, HD, AXP and others all exhibit different correlation structures and risk‑adjusted characteristics. These differences drive the optimizer’s weight adjustments.

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 Dow 30 includes several highly correlated mega‑cap names — such as AAPL, MSFT, JPM, V, HD — the optimizer often reduces overweight positions in correlation clusters while increasing exposure to lower‑correlation components like TRV, MRK, WMT, CSCO. 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‑4.xlsx shows how the entire portfolio behaves today versus after optimization.

Expected Return

The optimized portfolio increases expected return (ExpR) from 0.1448 to 0.1821. (Source: “ExpR Current / Target” in Demo‑4.xlsx)

Risk

Total portfolio risk decreases from 0.1595 to 0.1404. (Source: “Risk Current / Target”)

Sharpe Ratio

The Sharpe ratio improves from 0.5797 to 0.9117. (Source: “Sharpe Current / Target”)

Alpha

Portfolio alpha increases from 0.0522 to 0.1026. (Source: “Alpha Current / Target”)

Beta

Portfolio beta decreases from 0.8466 to 0.6901. (Source: “Beta Current / Target”)

Correlation

Overall correlation improves from 0.5148 to 0.4484. (Source: “Correl Current / Target”)

Actual vs Expected Return

Actual vs Expected Return improves from 0.1673 to 0.2161. (Source: “ActR Current / Target”)

Benchmark Comparison

The benchmark gap improves from 2.9189 to 7.7971. (Source: “vs Bmk Current / Target”)

Every metric improves because the optimizer focuses on the total portfolio — not individual stocks.