Predicting Stock Returns Using CAPM and Fama-French Models

[sharayuanuse]

Is your feature request related to a problem? Please describe.

The aim of this project is to predict stock returns using the Capital Asset Pricing Model (CAPM) and the Fama-French Three-Factor Model based on historical data. However, the current implementation primarily produces basic numerical results and limited visualizations. This lack of comprehensive reporting makes it challenging for users to effectively interpret the predictive insights and performance trends of the stock returns. Users need a more detailed and visually appealing representation of the data to fully understand the implications of the predictions and communicate findings to stakeholders.

Describe the solution you'd like

• Advanced Visualizations:
  • Time Series Graphs: Display predicted vs. actual stock returns over time to visualize the accuracy of the models.
  • Scatter Plots: Illustrate relationships between stock returns and market returns with trend lines to show correlations.
  • Bar Charts: Show the coefficients and significance levels of the factors in the Fama-French model for easier interpretation of results.
• Automated PDF Reporting:
  • Generate a comprehensive PDF report that includes:
  • An executive summary of the CAPM and Fama-French model results.
  • Embedded visualizations to support the analysis.
  • A section that interprets the results, detailing insights for investors and stakeholders.
• User Interactivity:
  • Implement user-friendly widgets in the Colab notebook that allow users to select different stock tickers and date ranges, making the analysis more flexible and tailored to their interests.

Describe alternatives you've considered

• Manual Compilation of Results: Allowing users to manually compile results and visualizations into separate reports. This method is inefficient and can lead to inconsistent presentations of findings.
• Basic Visualizations Only: Limiting the project to basic static plots. This approach does not provide the depth or engagement needed for users to fully understand the predictive capabilities of the models.
• Separate External Reporting Tools: Using external reporting tools for visualization and reporting. While this can enhance presentation quality, it introduces additional complexity and dependencies.

Approach to be followed (optional)

• Selection of Visualization Libraries: Research and implement visualization libraries like Plotly or Matplotlib to create interactive and informative graphs.
• Designing the PDF Report Template: Create a template using libraries such as FPDF or ReportLab that includes sections for summaries, visualizations, and interpretations, ensuring a coherent and professional layout.
• Integrating Interactive Widgets: Use IPython widgets to enable users to select stock tickers and date ranges within the Colab notebook, enhancing user experience and engagement.
• User Testing and Feedback: Conduct user testing to gather feedback on the new features, ensuring they meet user needs and expectations.
• Documentation and Tutorials: Update the documentation and provide tutorials to guide users through the new features, promoting ease of use and effective understanding of the models.

Additional context

[github-actions[bot]]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

[github-actions[bot]]

Hello @sharayuanuse! Your issue #149 has been closed. Thank you for your contribution!