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    <title>Week 7: Classification and Decision Trees in Finance</title>
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        <h2>Week 7: Classification and Decision Trees in Finance</h2>

        <section>
            <h3>Learning Objectives:</h3>
            <ul>
                <li>Master classification fundamentals in financial contexts</li>
                <li>Understand decision tree algorithms and their applications</li>
                <li>Learn model evaluation metrics for classification</li>
                <li>Apply classification models to financial problems</li>
                <li>Interpret and validate classification results</li>
            </ul>
        </section>

        <section>
            <h3>Core Resources:</h3>

            <h4>1. Theoretical Foundations</h4>
            <ul>
                <li><a href="https://www.jstor.org/stable/2699986">Classification and Regression Trees - Breiman et al.</a></li>
                <li><a href="https://www.jstor.org/stable/2983268?seq=1">Credit Scoring Using Machine Learning - Hand & Henley</a></li>
                <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1294140">Bank Rating Prediction - Altman & Rijken</a></li>
                <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1362190">Regulatory Pressure and Fire Sales in the Corporate Bond Market- Ellul & Jotikasthira & Lundblad</a></li>
                <li><a href="https://www.cambridge.org/core/journals/journal-of-financial-and-quantitative-analysis/article/fire-sales-and-impediments-to-liquidity-provision-in-the-corporate-bond-market/0ABF516A8F5D01346AEAD15F094178A1">Fire Sales and Impediments to Liquidity Provision in the Corporate Bond Market- Wang & Zhang & Zhang</a></li>
            </ul>

            <h4>2. Technical Implementation</h4>
            <ul>
                <li><a href="https://scikit-learn.org/stable/modules/tree.html">Scikit-learn Decision Trees</a></li>
                <li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html">Classification Metrics</a></li>
                <li><a href="https://scikit-learn.org/stable/modules/cross_validation.html">Model Validation</a></li>
            </ul>

            <h4>3. Financial Applications</h4>
            <ul>
                <li><b>Data Sources:</b>
                    <ul>
                        <li><a href="https://wrds-www.wharton.upenn.edu/pages/get-data/compustat-capital-iq-standard-poors/capital-iq/sp-credit-ratings/">S&P Credit Ratings</a></li>
                        <li><a href="https://wrds-www.wharton.upenn.edu/pages/get-data/mergent-fixed-income-securities-database-fisd/bond-ratings/">Bond Ratings</a></li>
                        <li><a href="https://wrds-www.wharton.upenn.edu/pages/support/support-articles/mergent-fisd/difference-between-rating-and-rating-history-tables/">Ratings vs Rating History</a></li>
                        <li><a href="https://www.kaggle.com/datasets/wordsforthewise/lending-club">Lending Club Dataset</a></li>
                        <li><a href="https://fred.stlouisfed.org/">FRED Economic Data</a></li>
                    </ul>
                </li>
                <li><b>Code Samples:</b>
                    <ul>
                        <li><a href="https://www.kaggle.com/datasets/wordsforthewise/lending-club/code">Lending Club Code</a></li>
                    </ul>
                </li>
                <li><b>Applications:</b>
                    <ul>
                        <li>Credit Risk Assessment</li>
                        <li>Rating Changes Prediction</li>
                        <li>Fraud Detection</li>
                        <li>Trading Signal Generation</li>
                        <li>Default Prediction</li>
                    </ul>
                </li>
            </ul>
        </section>

        <section>
            <h2>Weekly Assignment</h2>
            <div class="alert alert-info">
                <strong>Due:</strong> End of Week 7
            </div>

            <h3>Option 1: Credit Risk Classification</h3>
            <ol>
                <li>Data Preparation
                    <ul>
                        <li>Use Lending Club or WRDS data</li>
                        <li>Prepare features: credit scores, debt ratios, employment history</li>
                        <li>Handle class imbalance</li>
                        <li>Split data respecting time order</li>
                    </ul>
                </li>
                <li>Model Implementation
                    <ul>
                        <li>Implement decision tree classifier</li>
                        <li>Use cross-validation for parameter tuning</li>
                        <li>Consider class weights or sampling techniques</li>
                        <li>Compare with logistic regression</li>
                    </ul>
                </li>
                <li>Analysis
                    <ul>
                        <li>Evaluate using precision, recall, F1-score</li>
                        <li>Analyze feature importance</li>
                        <li>Visualize decision tree structure</li>
                        <li>Consider economic implications of misclassification</li>
                    </ul>
                </li>
            </ol>

            <h3>Option 2: Research-Based Application</h3>
            <p>Design your own classification application in finance. Some suggestions:</p>
            <ul>
                <li>Rating Change Prediction
                    <ul>
                        <li>Predict credit rating upgrades/downgrades</li>
                        <li>Use financial ratios and market indicators</li>
                        <li>Compare with rating agency decisions</li>
                    </ul>
                </li>
                <li>Trading Signal Generation
                    <ul>
                        <li>Classify market conditions</li>
                        <li>Generate buy/sell signals</li>
                        <li>Evaluate trading performance</li>
                    </ul>
                </li>
            </ul>

            <div class="alert alert-warning">
                <strong>Submission Requirements:</strong>
                <ul>
                    <li>Code with clear documentation</li>
                    <li>Brief report including:
                        <ul>
                            <li>Problem motivation and relevance</li>
                            <li>Methodology and implementation details</li>
                            <li>Results and interpretation</li>
                            <li>Challenges encountered and solutions</li>
                        </ul>
                    </li>
                    <li>If choosing Option 2, include references to papers of similar applications</li>
                </ul>
            </div>
        </section>

        <section>
            <h3>Implementation Tips:</h3>
            <div class="alert alert-info">
                <strong>Key Considerations:</strong>
                <ul>
                    <li>Handle class imbalance appropriately</li>
                    <li>Use appropriate evaluation metrics</li>
                    <li>Consider interpretability vs performance</li>
                    <li>Document hyperparameter selection</li>
                    <li>Be mindful of look-ahead bias</li>
                    <li>Consider cost of misclassification</li>
                </ul>
            </div>
        </section>

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