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<h1>Financial Data Segmentation: Factor Analysis</h1>
<p><strong>Arina Bobrova, Dennis Maroulis, Tavleen Kaur, Ville Herlin</strong></p>
<h2>Business Decisions</h2>
<p>Analyze the correlation between different companies in the Vietnamise stock market.</p>
<h2>The Data</h2>
<p>We analyze 900 companies based on 17 different variables including market cap, EBITDA, PE, e.t.c. listed in the vietnamise stock market</p>
<p>This is how the first 50 data looks:
<br></p>
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<br> <br></p>
<h2>Our Approach</h2>
<h4>Factor Analysis in 6 steps</h4>
<ol>
<li><p>Confirm the data in metric </p></li>
<li><p>Decide whether to scale or standardize the data</p></li>
<li><p>Check the correlation matrix to see if Factor Analysis makes sense</p></li>
<li><p>Develop a scree plot and decide on the number of factors to be derived</p></li>
<li><p>Interpret the factors (consider factor rotations - technical but useful)</p></li>
<li><p>Save factor scores for subsequent analyses</p></li>
</ol>
<p>Let&#39;s follow these steps.</p>
<p>The data we use here have the following descriptive statistics: </p>
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<h4>Step 2: Decide whether to scale or standardize the data</h4>
<p>We scaled the data because of the different magnitude of the numbers in the data under consideration.</p>
<pre><code class="r">ProjectDatafactor_scaled=apply(ProjectDataFactor,2, function(r) {if (sd(r)!=0||is.na(r)==FALSE) res=(r-mean(r))/sd(r) else res=0*r; res})
</code></pre>
<p>Below is the summary statistics of the scaled dataset:</p>
<p><br></p>
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As expected all variables have mean 0 and standard deviation 1. </p>
<h4>Step 3: Check correlation matrix to see if Factor Analysis makes sense</h4>
<p>This is the correlation matrix of the 17 original variable we use for factor analysis. Some of the variables have strong correlations. The most obvious ones are between size meters such as revenue and value metrics such as market capitalization. Some interesting observations can be made from correlations alone. For instance dividend yield does not significantly affect valuation or depend on size. P/E does not depend on size. </p>
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<h4>Step 4. Develop a scree plot and decide on the number of factors to be derived</h4>
<p>Based on the cumulative explained variance we use six components that explain 67% of the total variance.</p>
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<h4>5. Interpret the factors</h4>
<p>For our data, the selected factors look as follows after the rotation: </p>
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<p><br> <br></p>
<p>Component 1 mainly consists of size metrics from recent performance and value metrics. Component 2 has some more longer time size metrics. It Component 3 consists of two debt capacity metrics. Component 4 combines historical and current p/e ratios. Component 5 has two variables that measure the valuation in relation to performance. Component 6 groups two metrics of debt to assets.</p>
<p>Components 1 and 2 have somewhat similar loadings because all the variables correlate strongly with each other. It might not make sense to use both of them.</p>
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<p><br> <br></p>
<h4>Step 6. Save factor scores for subsequent analyses</h4>
<p>For our data, using the rotated factors we selected, we can create a new dataset where our observations are as follows (for the first 50 companies):</p>
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<br> <br></p>
<h2>Further Ideas</h2>
<p>This framework can be extended to work as a tool for screening companies. It would make sense to add a lot more variables. Then components can be compared with other markets to test hypothesis along the lines “does debt capacity matter more or less for valuation in these markets?” The framework can also be extended for back testing investment strategies. </p>
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