DataAnalytics/week4.html at main · CinderZhang/DataAnalytics · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Week 4: Applied Financial Data Analysis with Python</title>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/css/bootstrap.min.css">
</head>

<body>
    <nav class="navbar navbar-default navbar-fixed-top">
        <div class="container-fluid">
            <div class="navbar-header">
                <a class="navbar-brand" href="index.html">Data Analytics I</a>
            </div>
            <ul class="nav navbar-nav">
                <li><a href="index.html">Home</a></li>
                <li><a href="about.html">About</a></li>
                <li><a href="contact.html">Contact</a></li>
            </ul>
        </div>
    </nav>

    <div style="height: 80px;"></div>

 <main class="container">
        <h1>Week 4: Applied Financial Data Analysis with Python</h1>

        <section>
            <h2>Topics Covered:</h2>
            <ul>
                <li>Advanced Data Analysis Techniques</li>
                <li>Python for Financial Research</li>
                <li>Post-Earnings Announcement Drift (PEAD)</li>
                <li>Data Quality and Validation</li>
            </ul>
        </section>

        <section>
            <h2>Core Resources:</h2>
            <ul>
                <li><b>Primary Implementation Guide:</b>
                    <a href="https://wrds-www.wharton.upenn.edu/pages/wrds-research/applications/python-replications/post-earnings-announcement-drift-python/">WRDS PEAD Python Implementation</a>
                </li>
                <li><b>Original Research:</b>
                    <a href="https://www.jstor.org/stable/2491062">PEAD Original Study</a>
                </li>
            </ul>
        </section>

        <section>
            <h2>Weekly Assignment :</h2>
            <div class="alert alert-info">
                <strong>Due Date:</strong> End of Week 4
            </div>

            <h3>Part 1: PEAD Replication </h3>
            <ol>
                <li>Data Preparation
                    <ul>
                        <li>Download and clean required data</li>
                        <li>Implement proper data quality checks</li>
                        <li>Document data sources and date ranges</li>
                    </ul>
                </li>
                <li>Core Analysis
                    <ul>
                        <li>Implement PEAD calculation following WRDS example</li>
                        <li>Calculate abnormal returns</li>
                        <li>Form portfolios based on earnings surprises</li>
                    </ul>
                </li>
                <li>Results & Validation
                    <ul>
                        <li>Compare your results with original findings</li>
                        <li>Document any discrepancies</li>
                        <li>Provide clear visualizations</li>
                    </ul>
                </li>
            </ol>

            <h3>Part 2: Extended Analysis </h3>
            <ol>
                <li>Updated Data Analysis
                    <ul>
                        <li>Update through 2024</li>
                        <li>Test in different market conditions</li>
                    </ul>
                </li>
                <li>Robustness Tests
                    <ul>
                        <li>Different portfolio formation methods</li>
                        <li>Various event windows</li>
                        <li>Market condition analysis</li>
                    </ul>
                </li>
            </ol>

            <h3>Bonus: AI Integration </h3>
            <div class="alert alert-success">
                <strong>Optional Enhancement:</strong> Use AI tools to enhance your analysis
            </div>
            <ol>
                <li>AI-Powered Analysis
                    <ul>
                        <li>Data validation using AI tools</li>
                        <li>Pattern recognition</li>
                        <li>Anomaly detection</li>
                    </ul>
                </li>
                <li>AI Insights
                    <ul>
                        <li>Generate additional insights</li>
                        <li>Compare AI findings with traditional analysis</li>
                        <li>Document AI tool effectiveness</li>
                    </ul>
                </li>
            </ol>
        </section>

        <section>
            <h2>Implementation Guide:</h2>

            <h3>1. Data Preparation</h3>
            <pre><code># Example data retrieval and preparation
import pandas as pd
import numpy as np
from pandasai import SmartDataframe

# Load and prepare data
earnings_data = pd.read_csv('earnings_data.csv')
market_data = pd.read_csv('market_data.csv')

# Basic quality checks
def basic_quality_checks(df):
    print("Missing values:", df.isnull().sum())
    print("Duplicates:", df.duplicated().sum())
    print("Date range:", df['date'].min(), "to", df['date'].max())</code></pre>

            <h3>2. PEAD Analysis</h3>
            <pre><code># Core PEAD calculation
def calculate_car(data, event_window=(-2, 2)):
    """Calculate Cumulative Abnormal Returns"""
    # Follow WRDS implementation
    pass

def form_portfolios(data, n_portfolios=10):
    """Form portfolios based on earnings surprise"""
    # Follow WRDS implementation
    pass</code></pre>

            <h3>3. AI Enhancement</h3>
            <pre><code># Optional AI analysis
from pandasai.llm import Ollama

# Initialize AI assistant
llm = Ollama(model="llama2")
df_smart = SmartDataframe(your_pead_results, config={'llm': llm})

# Example AI analysis
analysis = df_smart.chat("Analyze the PEAD patterns")</code></pre>
        </section>

        <section>
            <h2>Submission Requirements:</h2>
            <ol>
                <li><b>Code Files</b>
                    <ul>
                        <li>Well-documented Python scripts</li>
                        <li>Clear comments explaining each step</li>
                        <li>Requirements.txt file</li>
                    </ul>
                </li>
                <li><b>Analysis Report</b>
                    <ul>
                        <li>Data preparation steps</li>
                        <li>Implementation details</li>
                        <li>Results and comparisons</li>
                        <li>AI-enhanced insights (if used)</li>
                    </ul>
                </li>
                <li><b>Presentation</b>
                    <ul>
                        <li>Key findings visualization</li>
                        <li>Comparison with original study</li>
                        <li>Discussion of challenges</li>
                    </ul>
                </li>
            </ol>
        </section>

        <section>
            <h2>Best Practices:</h2>
            <ul>
                <li><b>Code Organization</b>
                    <ul>
                        <li>Follow WRDS code structure</li>
                        <li>Document any modifications</li>
                        <li>Use version control</li>
                    </ul>
                </li>
                <li><b>Data Management</b>
                    <ul>
                        <li>Keep raw data separate</li>
                        <li>Document transformations</li>
                        <li>Maintain data versions</li>
                    </ul>
                </li>
                <li><b>Result Validation</b>
                    <ul>
                        <li>Compare with benchmarks</li>
                        <li>Test across periods</li>
                        <li>Document discrepancies</li>
                    </ul>
                </li>
            </ul>
        </section>

        <div style="text-align: center; margin-top: 20px;">
            <a href="week3.html">← Previous Week</a>
            |
            <a href="week5.html">Next Week →</a>
        </div>

        <div class="back-to-home">
            <a href="index.html">← Back to Home</a>
        </div>
    </main>

    <footer class="container text-center">
        <p>&copy; 2025 Cinder Zhang. All rights reserved. Contact us at <a href="mailto:xzhang@walton.uark.edu">xzhang at walton uark</a></p>
    </footer>

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/js/bootstrap.min.js"></script>
</body>

</html>