Dashboards

FAMA-FRENCH 4 FACTOR MODEL

Understanding the Fama-French 4-Factor Model and How the Dashboard Works

What Is the Fama-French 4-Factor Model?

The Fama-French 4-Factor Model is a powerful tool used by investors and researchers to explain and predict the returns of stocks and portfolios. Unlike the traditional Capital Asset Pricing Model (CAPM), which only considers overall market risk, the Fama-French model adds three more factors that have been shown to drive stock returns:

1. Market Risk (MKT-Rf): The extra return stocks earn over the risk-free rate, reflecting sensitivity to the overall market.
2. Size (SMB – Small Minus Big): The tendency for smaller companies to outperform larger ones.
3. Value (HML – High Minus Low): The tendency for “value” stocks (with high book-to-market ratios) to outperform “growth” stocks (with low book-to-market ratios).
4. Profitability (RMW – Robust Minus Weak): The tendency for more profitable companies to outperform less profitable ones.

Each stock or portfolio has different exposures (betas) to these factors, and the model helps explain returns as a combination of these exposures plus any additional “alpha”

How the Dashboard Works

The interactive dashboard brings the Fama-French 4-Factor Model to life, letting you build and optimize a portfolio tailored to your desired exposures to these four key factors. Here’s how it works, step by step:

1. User Input: Set Your Target Factor Exposures

• Interactive Controls: Use sliders or input boxes to set your desired exposures to Market, Size, Value, and Profitability factors.
• Portfolio Constraints: Choose options like “no shorting,” weight limits, or requiring the portfolio to be fully invested.

2. Data Handling: Real Market Data

• Asset Universe: The dashboard uses a curated list of 30 or fewer highly liquid U.S. stocks and ETFs (detailed at the bottom¹).
• Price Data: It automatically pulls monthly price data from Yahoo Finance.
• Factor Data: It fetches the latest Fama-French factor data from the Kenneth French Data Library.
• Factor Exposure Calculation: For each security, the dashboard calculates how sensitive it is to each of the four factors (its “betas”) using historical data and regression analysis.

3. Portfolio Optimization: Matching Your Targets

• Optimization Engine: The dashboard uses advanced mathematical optimization to find the mix of stocks/ETFs that best matches your chosen factor exposures.
• Objective: It minimizes the difference between the portfolio’s factor exposures and your targets, while respecting your constraints (like no shorting or weight limits).
• Behind the Scenes: The optimizer solves for the weights of each asset so that, when combined, your portfolio’s exposure to each factor is as close as possible to your desired levels.

4. Outputs: Visualizing Your Portfolio

• Optimized Weights Table: See how much of your portfolio is allocated to each stock or ETF.
• Factor Exposure Chart: Instantly compare your portfolio’s actual factor exposures to your targets.
• Asset Allocation Pie Chart: Visualize your portfolio’s composition.
• Tracking Error: See how closely your portfolio matches your target exposures.
• Export: Download your optimized portfolio as an Excel or CSV file.

Example Use Case

Suppose you believe that small-cap and profitable stocks will outperform in the coming years. Using the dashboard, you can set higher target exposures to the Size (SMB) and Profitability (RMW) factors. The optimizer will then construct a portfolio of stocks and ETFs that maximizes your exposure to these factors, while keeping your portfolio diversified and within your chosen constraints.

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Summary Table

Step	What Happens
Set Targets	You choose desired factor exposures and constraints
Data Collection	Dashboard fetches price and factor data, calculates betas
Optimization	Finds the best mix of stocks/ETFs to match your targets
Results	Shows your portfolio weights, exposures, and allocation visuals
Export	Download your portfolio for your records or further action

Daniel Rivas

1. Full universe of securities used for the dashboard:
   
   Broad Market ETFs:
   – SPY (S&P 500 ETF):
   – QQQ (NASDAQ 100 ETF)
   – IWM (Russell 2000 ETF)
   – VTI (Vanguard Total Stock Market ETF)
   
   Sector ETFs:
   – XLF (Financials):
   – XLK (Technology)
   – XLE (Energy)
   – XLY (Consumer Discretionary)
   – XLP (Consumer Staples)
   – XLV (Health Care)
   – XLI (Industrials)
   – XLB (Materials)
   – XLRE (Real Estate)
   – XLC (Communication Services)
   – XLU (Utilities)
   
   Large-Cap Stocks:
   – AAPL (Apple)
   – MSFT (Microsoft)
   – GOOGL (Alphabet)
   – AMZN (Amazon)
   – META (Meta Platforms)
   – NVDA (NVIDIA)
   – JPM (JPMorgan Chase)
   – JNJ (Johnson & Johnson)
   – UNH (UnitedHealth Group)
   – PG (Procter & Gamble)
   – HD (Home Depot)
   – V (Visa)
   – MA (Mastercard)
   – DIS (Disney)
   – BAC (Bank of America)
   – PFE (Pfizer)
   – TSM (Taiwan Semiconductor)
   – COST (Costco)
   
   Small/Mid-Cap and Specialty:
   – PERI (Perion Network) (small-cap)
   – ROM (ProShares Ultra Technology) (leveraged/specialty ETF) ↩︎