Explaining Stock Returns with the Fama-French 5 Factor Model

<p>In 2023, the Fama-French 5-factor model could explain only 35% of NVIDIA's weekly return variation. In 2024, it explained 82%. That gap tells you something important: NVIDIA's 2023 surge was driven primarily by a narrative that no systematic factor had yet priced in - the sudden recognition that GPU scarcity would define AI infrastructure for years. By 2024, that insight had diffused into the market factor itself, and the stock began behaving like a large-cap bellwether rather than a thematic trade.</p> <p>The Fama-French 5-factor model is the most widely used framework for decomposing stock returns into systematic components. Instead of asking "did this stock go up?", it asks "how much of that return is explained by exposure to known risk premia, and how much is specific to the company?" The answer has implications for both portfolio construction and for understanding whether outperformance is repeatable.</p> <p>This article runs the model on NVIDIA, Apple, and Microsoft using the Finance Toolkit, interprets what each factor says about each company, and draws out the most useful comparisons across the three. <strong>For more information on the Finance Toolkit, have a look <a href="https://github.com/JerBouma/FinanceToolkit" rel="noopener noreferrer">here</a>. To explore the Finance Toolkit MCP, see <a href="https://www.jeroenbouma.com/projects/financetoolkit/mcp" rel="noopener noreferrer">here</a>.</strong></p> <h2> The Five Factors </h2> <p>Before running any code, it helps to have the five factors clearly defined. Each represents a systematic source of return that the model holds responsible for a portion of a stock's performance.</p> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Factor</th> <th>Full Name</th> <th>What it measures</th> <th>Negative slope means</th> </tr> </thead> <tbody> <tr> <td>Mkt-RF</td> <td>Market Risk Premium</td> <td>Excess return of the broad market over the risk-free rate. Slope ~= market beta.</td> <td>Less market-sensitive than average</td> </tr> <tr> <td>SMB</td> <td>Small Minus Big</td> <td>Historical premium of small-cap stocks over large-cap stocks.</td> <td>Large-cap orientation - underperforms when small-caps lead</td> </tr> <tr> <td>HML</td> <td>High Minus Low</td> <td>Historical premium of value stocks (high book-to-market) over growth stocks.</td> <td>Growth stock - underperforms when value leads</td> </tr> <tr> <td>RMW</td> <td>Robust Minus Weak</td> <td>Historical premium of high operating profitability over weak profitability.</td> <td>Returns not well explained by current realized earnings</td> </tr> <tr> <td>CMA</td> <td>Conservative Minus Aggressive</td> <td>Historical premium of low-investment firms over high-investment firms.</td> <td>Aggressive reinvestor - heavy capex or R&amp;D spend</td> </tr> </tbody> </table></div> <h2> Setting Things Up </h2> <p>Start by installing the Finance Toolkit:<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight shell"><code>pip <span class="nb">install </span>financetoolkit </code></pre> </div> <p>Then import the library and create a Toolkit instance for all three stocks:<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="kn">from</span> <span class="n">financetoolkit</span> <span class="kn">import</span> <span class="n">Toolkit</span> <span class="n">companies</span> <span class="o">=</span> <span class="nc">Toolkit</span><span class="p">(</span> <span class="n">tickers</span><span class="o">=</span><span class="p">[</span><span class="sh">"</span><span class="s">NVDA</span><span class="sh">"</span><span class="p">,</span> <span class="sh">"</span><span class="s">AAPL</span><span class="sh">"</span><span class="p">,</span> <span class="sh">"</span><span class="s">MSFT</span><span class="sh">"</span><span class="p">],</span> <span class="n">api_key</span><span class="o">=</span><span class="sh">"</span><span class="s">YOUR