WHUFT/WHU_FinTech_Workshop
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisJupyter NotebooksMatplotlibPandasPython ProgrammingQuantitative Finance
Stargazingwhuems09
PROFILE
Stargazingwhuems09
During November 2024, this developer created a Beta Coefficient Estimation Notebook for the WHUFT/WHU_FinTech_Workshop repository, focusing on quantitative finance analytics. They implemented rolling window regression techniques using Python and Jupyter Notebooks, leveraging libraries such as Pandas, Matplotlib, and Statsmodels to analyze Beta dynamics across industries and time periods. The notebook integrates CAPM theory, data preparation, estimation workflows, and comprehensive visualizations for both monthly and daily data. By structuring the content for clarity and reproducibility, the developer enhanced the accessibility of finance analytics training and enabled data-driven decision support, demonstrating depth in statistical modeling and practical application of financial concepts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,752
Activity Months1
Your Network
17 peopleWork History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for WHU_FinTech_Workshop highlighting delivery of a Beta Coefficient Estimation Notebook with rolling window regression and visualization. The notebook provides a structured explanation of CAPM concepts, estimation methods, data preparation, and practical rolling-window analyses for both monthly and daily data, with comprehensive visualization of Beta values across industries and time periods. This work enhances finance analytics training, reproducibility, and data-driven decision support.
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for WHU_FinTech_Workshop highlighting delivery of a Beta Coefficient Estimation Notebook with rolling window regression and visualization. The notebook provides a structured explanation of CAPM concepts, estimation methods, data preparation, and practical rolling-window analyses for both monthly and daily data, with comprehensive visualization of Beta values across industries and time periods. This work enhances finance analytics training, reproducibility, and data-driven decision support.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisJupyter NotebooksMatplotlibPandasPython ProgrammingQuantitative FinanceSeabornStatistical ModelingStatsmodels
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