tidy-finance · christophscheuch · Dec 16, 2024 · Dec 16, 2024 · Dec 16, 2024 · Dec 16, 2024
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diff --git a/_quarto.yml b/_quarto.yml
@@ -97,7 +97,11 @@ website:
           contents:
             - r/setting-up-your-environment.qmd
             - r/the-tidyfinance-r-package.qmd
-            - r/introduction-to-tidy-finance.qmd
+            - r/working-with-stock-returns.qmd
+            - r/modern-portfolio-theory.qmd
+            - r/capital-asset-pricing-model.qmd
+            - r/financial-ratios.qmd
+            - r/discounted-cash-flow-analysis.qmd
         - section: "Financial Data"
           contents:
             - r/accessing-and-managing-financial-data.qmd

diff --git a/assets/bib/packages-r.bib b/assets/bib/packages-r.bib
@@ -135,6 +135,13 @@ @book{ggplot2
 	note         = {R package version 3.3.6},
 	isbn         = {978-3-319-24277-4},
 }
+@manual{@ggrepel,
+  title = {ggrepel: Automatically Position Non-Overlapping Text Labels with 'ggplot2'},
+  author = {Kamil Slowikowski},
+  year = {2024},
+  url = {https://ggrepel.slowkow.com/},
+  note = {R package version 0.9.6},
+}
 @article{glmnet,
 	title        = {{Regularization paths for Cox's proportional hazards model via coordinate descent}},
 	author       = {Noah Simon and Jerome Friedman and Trevor Hastie and Rob Tibshirani},

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diff --git a/docs/introduction-to-tidy-finance.html b/docs/introduction-to-tidy-finance.html
@@ -2,7 +2,7 @@
 <head>
   <title>Redirect</title>
   <script type="text/javascript">
-    var redirects = {"":"r/introduction-to-tidy-finance.html"};
+    var redirects = {"":"r/working-with-stock-returns.html"};
     var hash = window.location.hash.startsWith('#') ? window.location.hash.slice(1) : window.location.hash;
     var redirect = redirects[hash] || redirects[""] || "/";
     window.document.title = 'Redirect to  ' +  redirect;

diff --git a/docs/python/factor-selection-via-machine-learning.html b/docs/python/factor-selection-via-machine-learning.html
@@ -704,7 +704,7 @@ <h2 class="anchored" data-anchor-id="data-preparation">Data Preparation</h2>
 <span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>data_plot.draw()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="6">
-<div id="fig-1401" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Title: Excess return distributions by industry in percent. The figure shows boxplots that visualize the industry's excess return distribution. All industry returns are centered around zero and exhibit substantial outliers in the magnitude of 20 percent on a monthly basis." data-fig-pos="htb">
+<div id="fig-1401" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-pos="htb" alt="Title: Excess return distributions by industry in percent. The figure shows boxplots that visualize the industry's excess return distribution. All industry returns are centered around zero and exhibit substantial outliers in the magnitude of 20 percent on a monthly basis.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-1401-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="factor-selection-via-machine-learning_files/figure-html/fig-1401-output-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="Figure&nbsp;1: The box plots show the monthly dispersion of returns for 10 different industries."><img src="factor-selection-via-machine-learning_files/figure-html/fig-1401-output-1.png" data-fig-pos="htb" alt="Title: Excess return distributions by industry in percent. The figure shows boxplots that visualize the industry's excess return distribution. All industry returns are centered around zero and exhibit substantial outliers in the magnitude of 20 percent on a monthly basis." width="647" height="407" class="figure-img"></a>
@@ -821,7 +821,7 @@ <h3 class="anchored" data-anchor-id="fit-a-model">Fit a model</h3>
 <span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a>predicted_values_plot.draw()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
-<div id="fig-1402" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Title: Monthly realized and fitted manufacturing industry risk premium. The figure shows the time series of realized and predicted manufacturing industry risk premiums. The figure seems to indicate that the predictions capture most of the return dynamics." data-fig-pos="htb">
+<div id="fig-1402" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-pos="htb" alt="Title: Monthly realized and fitted manufacturing industry risk premium. The figure shows the time series of realized and predicted manufacturing industry risk premiums. The figure seems to indicate that the predictions capture most of the return dynamics.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-1402-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="factor-selection-via-machine-learning_files/figure-html/fig-1402-output-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-2" title="Figure&nbsp;2: The figure shows monthly realized and fitted manufacturing industry risk premium. The grey area corresponds to the out of sample period."><img src="factor-selection-via-machine-learning_files/figure-html/fig-1402-output-1.png" data-fig-pos="htb" alt="Title: Monthly realized and fitted manufacturing industry risk premium. The figure shows the time series of realized and predicted manufacturing industry risk premiums. The figure seems to indicate that the predictions capture most of the return dynamics." width="647" height="407" class="figure-img"></a>
@@ -873,7 +873,7 @@ <h3 class="anchored" data-anchor-id="fit-a-model">Fit a model</h3>
 <span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>  theme(legend_position<span class="op">=</span><span class="st">"none"</span>))</span>
 <span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>coefficients_plot.draw()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
-<div id="fig-1403" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Title: Estimated coefficient paths for different penalty factors. The figure shows how estimated lasso and ridge coefficients tend to zero for a higher penalty parameter. Ridge trace is smooth, and Lasso exhibits non-linear behavior." data-fig-pos="htb">
+<div id="fig-1403" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-pos="htb" alt="Title: Estimated coefficient paths for different penalty factors. The figure shows how estimated lasso and ridge coefficients tend to zero for a higher penalty parameter. Ridge trace is smooth, and Lasso exhibits non-linear behavior.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-1403-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="factor-selection-via-machine-learning_files/figure-html/fig-1403-output-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-3" title="Figure&nbsp;3: The figure shows estimated coefficient paths for different penalty factors. The penalty parameters are chosen iteratively to resemble the path from no penalization to a model that excludes all variables."><img src="factor-selection-via-machine-learning_files/figure-html/fig-1403-output-1.png" data-fig-pos="htb" alt="Title: Estimated coefficient paths for different penalty factors. The figure shows how estimated lasso and ridge coefficients tend to zero for a higher penalty parameter. Ridge trace is smooth, and Lasso exhibits non-linear behavior." width="647" height="407" class="figure-img"></a>
@@ -958,7 +958,7 @@ <h3 class="anchored" data-anchor-id="tune-a-model">Tune a model</h3>
 <span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>validation_plot.draw()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
-<div id="fig-1404" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Title: Root MSPE for different penalty factors. The figure shows that more regularization does not affect the selected models in a meaningful fashion. At some point, the Elastic Net prediction error drops, which indicates the selected model. MSPE increases again for high penalization values." data-fig-pos="htb">
+<div id="fig-1404" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-pos="htb" alt="Title: Root MSPE for different penalty factors. The figure shows that more regularization does not affect the selected models in a meaningful fashion. At some point, the Elastic Net prediction error drops, which indicates the selected model. MSPE increases again for high penalization values.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-1404-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="factor-selection-via-machine-learning_files/figure-html/fig-1404-output-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-4" title="Figure&nbsp;4: The figure shows root MSPE for different penalty factors. Evaluation of manufacturing excess returns for different penalty factors (lambda) and proportions of Lasso penalty (rho). 1.0 indicates Lasso, 0.5 indicates Elastic Net, and 0.0 indicates Ridge."><img src="factor-selection-via-machine-learning_files/figure-html/fig-1404-output-1.png" data-fig-pos="htb" alt="Title: Root MSPE for different penalty factors. The figure shows that more regularization does not affect the selected models in a meaningful fashion. At some point, the Elastic Net prediction error drops, which indicates the selected model. MSPE increases again for high penalization values." width="647" height="407" class="figure-img"></a>
@@ -1036,7 +1036,7 @@ <h3 class="anchored" data-anchor-id="full-workflow">Full workflow</h3>
 <span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>selected_factors_plot.draw()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
-<div id="fig-1405" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Title: Selected variables for different industries. The figure shows which factors and macroeconomic predictors the Lasso model selected for the different industries. In general, there are not many selected variables. The market excess return is selected across all industries except for utilities." data-fig-pos="htb">
+<div id="fig-1405" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-pos="htb" alt="Title: Selected variables for different industries. The figure shows which factors and macroeconomic predictors the Lasso model selected for the different industries. In general, there are not many selected variables. The market excess return is selected across all industries except for utilities.">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-1405-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="factor-selection-via-machine-learning_files/figure-html/fig-1405-output-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-5" title="Figure&nbsp;5: The figure shows selected variables for different industries. Dark areas indicate that the estimated Lasso regression coefficient is not set to zero. White fields show which variables get assigned a value of exactly zero."><img src="factor-selection-via-machine-learning_files/figure-html/fig-1405-output-1.png" data-fig-pos="htb" alt="Title: Selected variables for different industries. The figure shows which factors and macroeconomic predictors the Lasso model selected for the different industries. In general, there are not many selected variables. The market excess return is selected across all industries except for utilities." width="647" height="647" class="figure-img"></a>