Add bankruptcy.parquet and README.md

README.md

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+---
+icon: seal-exclamation
+description: >-
+  Chapter 7 and Chapter 11 bankruptcy predictions made easy for over 13,000 US
+  publicly traded stocks.
+---
+
+# Bankruptcy Predictions
+
+
+Monthly corporate bankruptcy predictions arrive the **2nd of every month**_._
+
+
+`Tutorials` are the best documentation — [<mark style="color:blue;">`Corporate Bankruptcy Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Bankruptcy%20Prediction.ipynb)
+
+<table data-view="cards" data-full-width="false"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Input Datasets</strong></td><td>SEC Bankruptcies, Delistings, Market Data, Financial Statements</td></tr><tr><td><strong>Models Used</strong></td><td>CNN, LightGBM, RocketModel, AutoEncoder</td></tr><tr><td><strong>Model Outputs</strong></td><td>Calibrated Probabilities, Shapley Values</td></tr></tbody></table>
+
+## Description
+
+The model predicts the likelihood of bankruptcies in the next 6-months for US publicly listed companies using advanced machine learning models.
+
+With an accuracy of around 89% and ROC-AUC of 85%, these models represent a large improvement over traditional methods of bankruptcy prediction for equity selection.
+
+Advanced modeling techniques used in this dataset:
+
+* **The Boosting Model**: Utilizes LightGBM technology, integrating both fundamental and market data for accurate predictions.
+* **The Convolutional Model**: Employs a Convolutional Neural Network (CNN) for efficient pattern recognition in market trends.
+* **The Rocket Model**: Specializes in time series data, using random convolutional kernels for effective classification and forecasting.
+* **The Encoder Model**: Combines LightGBM with CNN autoencoders, enhancing feature engineering for more precise predictions.
+* **The Fundamental Model**: Focuses solely on fundamental data via LightGBM, without extra architectural layers, for straightforward financial analysis.
+
+## Data Access
+
+### **Monthly Probabilities**
+
+**Specific Tickers**
+
+```python
+import sovai as sov
+df_bankrupt = sov.data('bankruptcy', tickers=["MSFT","TSLA","META"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_1.png" alt=""><figcaption></figcaption></figure>
+
+**Specific Dates**
+
+```python
+import sovai as sov
+df_bankrupt = sov.data('bankruptcy', start_date="2017-01-03", tickers=["MSFT"])
+```
+
+**Latest Data**
+
+```python
+import sovai as sov
+df_bankrupt = sov.data('bankruptcy')
+```
+
+**All Data**
+
+```python
+import sovai as sov
+df_bankrupt = sov.data('bankruptcy', full_history=True)
+```
+
+### Daily Probabilities
+
+```python
+import sovai as sov
+df_bankrupt = sov.data('bankruptcy/daily', tickers=["MSFT","TSLA","META"])
+```
+
+The daily probabilities are experimental, and have a very short history of just a couple of months.
+
+### Feature Importance (Shapleys)
+
+```python
+import sovai as sov
+df_importance = sov.data('bankruptcy/shapleys', tickers=["MSFT","TSLA","META"])
+```
+
+Feature Importance (Shapley Values) calculates the contribution of each input variable (features) such as Debt, Assets, and Revenue to predict bankruptcy risk.
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_2.png" alt=""><figcaption></figcaption></figure>
+
+## Reports
+
+### Sorting and Filtering
+
+```python
+import sovai as sov
+sov.report("bankruptcy", report_type="ranking")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_3.png" alt=""><figcaption></figcaption></figure>
+
+Filter the outputs based on the top by **Sector**, **Marketcap**, and **Revenue** and bankruptcy risk. You can also change <mark style="color:blue;">`ranking`</mark> to <mark style="color:blue;">`change`</mark> to investigate the month on month change.
+
+```python
+sov.report("bankruptcy", report_type="sector-change")
+```
+
+## Plots
+
+### Bankruptcy Comparison
+
+```python
+import sovai as sov
+sov.plot('bankruptcy', chart_type='compare')
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_4.png" alt=""><figcaption></figcaption></figure>
+
+### Timed Feature Importance
+
+```python
+import sovai as sov
+df = sov.plot("bankruptcy", chart_type="shapley", tickers=["TSLA"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_5.png" alt=""><figcaption></figcaption></figure>
+
+### Total Feature Importance
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="stack", tickers=["DDD"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_6.png" alt=""><figcaption></figcaption></figure>
+
+### Bankruptcy and Returns
+
+```python
+import sovai as sov
+df= sov.plot("bankruptcy", chart_type="line", tickers=["DDD"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_7.png" alt=""><figcaption></figcaption></figure>
+
+### **PCA Statistical Similarity**
+
+```python
+import sovai as sov
+df= sov.plot("bankruptcy", chart_type="line", tickers=["DDD"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_8.png" alt=""><figcaption></figcaption></figure>
+
+### Correlation Similarity
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="similar", tickers=["DDD"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_9.png" alt=""><figcaption></figcaption></figure>
+
+### Trend Similarity
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="facet", tickers=["DDD"])
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_10.png" alt=""><figcaption></figcaption></figure>
+
+## Model Performance
+
+### **Confusion Matrix**
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="confusion_global")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_11.png" alt=""><figcaption></figcaption></figure>
+
+### **Threshold Plots**
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="classification_global")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_12.png" alt=""><figcaption></figcaption></figure>
+
+### **Lift Curve**
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="lift_global")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_13.png" alt=""><figcaption></figcaption></figure>
+
+### Global Explainability
+
+```python
+import sovai as sov
+sov.plot("bankruptcy", chart_type="time_global")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/bankruptcy_predictions_14.png" alt=""><figcaption></figcaption></figure>
+
+## Computations
+
+Leverage advanced computational tools for deeper analysis:
+
+*   **Distance Matrix:**
+
+    ```python
+    sov.compute('distance-matrix', on="attribute", df=dataframe)
+    ```
+
+    Assess the similarity between entities based on selected attributes.
+*   **Percentile Calculation:**
+
+    ```python
+    sov.compute('percentile', on="attribute", df=dataframe)
+    ```
+
+    Calculate the relative standing of values within a dataset.
+*   **Feature Mapping:**
+
+    ```python
+    sov.compute('map-accounting-features', df=dataframe)
+    ```
+
+    Map accounting features to standardized metrics.
+*   **PCA Calculation:**
+
+    ```python
+    sov.compute('pca', df=dataframe)
+    ```
+
+    Perform principal component analysis for dimensionality reduction.
+
+**For more advanced applications, see the tutotrial.**
+
+## Data Dictionary
+
+<table><thead><tr><th width="293">Name</th><th width="246">Description</th><th width="89">Type</th><th>Example</th></tr></thead><tbody><tr><td><code>ticker</code></td><td>Stock ticker symbol.</td><td>TEXT</td><td>"TSLA"</td></tr><tr><td><code>date</code></td><td>Record date.</td><td>DATE</td><td>2023-09-30</td></tr><tr><td><code>probability_light</code></td><td>LightGBM Boosting Model prediction.</td><td>FLOAT</td><td>1.46636</td></tr><tr><td><code>probability_convolution</code></td><td>CNN Model prediction for bankruptcies</td><td>FLOAT</td><td>0.135975</td></tr><tr><td><code>probability_rocket</code></td><td>Rocket Model prediction for time series classification</td><td>FLOAT</td><td>0.02514</td></tr><tr><td><code>probability_encoder</code></td><td>LightGBM and CNN autoencoders Model prediction.</td><td>FLOAT</td><td>0.587817</td></tr><tr><td><code>probability_fundamental</code></td><td>Prediction using accounting data only.</td><td>FLOAT</td><td>1.26148</td></tr><tr><td><code>probability</code></td><td>Average probability across models.</td><td>FLOAT</td><td>0.553823</td></tr><tr><td><code>sans_market</code></td><td>Fundamental prediction adjusted for market predictions.</td><td>FLOAT</td><td>-0.20488</td></tr><tr><td><code>volatility</code></td><td>Variability of model predictions.</td><td>FLOAT</td><td>0.62934</td></tr><tr><td><code>multiplier</code></td><td>Coefficient for model prediction calibration.</td><td>FLOAT</td><td>1.951868</td></tr><tr><td><code>version</code></td><td>Model/data record version.</td><td>INT</td><td>20240201</td></tr></tbody></table>
+
+
+When `sans_market` is <mark style="color:green;">positive</mark>, it means that the fundamentals show a larger predicted bankruptcy than what the market predicts **(stock might go down in medium term)** , when `sans_market` is <mark style="color:red;">negative</mark>, the market might have overreacted, and predict a larger probability of bankruptcy than what the fundamentals suggest **(stock might go up in medium term)**.
+
+
+## Use Cases
+
+1. **Bankruptcy Prediction Analysis**: Offer insights into predicted corporate bankruptcies and identify key factors, clarifying main drivers across different cycles.
+2. **Variable Impact Breakdown**: Analyze how each individual variable affects bankruptcy predictions, providing in-depth feature contribution insights.
+3. **Temporal Feature Distribution Analysis**: Reveal how variables contribute to predictions over time, emphasizing key features in forecasting models.
+4. **Correlation Discovery**: Identify stocks with similar bankruptcy probability trends, revealing correlated market behaviors.
+5. **Probability Shift Overview**: Showcase changes in bankruptcy probabilities among correlated stocks, providing a comprehensive market perspective.
+6. **Sentiment Inversion Analysis**: Convert bankruptcy predictions into positive sentiment indicators to gauge potential impacts on stock returns.
+7. **Behavioral Similarity Mapping**: Locate stocks with similar behaviors to a selected reference, based on bankruptcy trends and PCA feature analysis.
+

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