Data-Driven Investment Strategies for Peer-to-Peer Lending: A Case Study for Teaching Data Science

Area of Science:

• Quantitative Finance
• Data Science
• Machine Learning

Background:

• Peer-to-peer (P2P) lending platforms generate vast amounts of data.
• Traditional investment strategies may not fully leverage this data for optimal returns.
• There is a need for robust, data-driven approaches to navigate the complexities of P2P lending investments.

Purpose of the Study:

• To develop and evaluate data-driven investment strategies for P2P lending.
• To demonstrate the application of machine learning and data analytics in guiding P2P loan investments.
• To provide a comprehensive, modular framework for instructors teaching data science.

Main Methods:

• Data acquisition from a P2P lending platform.
• Development of investment strategies using various data science approaches.
• Evaluation of predictive performance and real-world strategy effectiveness.
• Utilizing Python for data cleaning, modeling, and portfolio optimization.

Main Results:

• Demonstrated the efficacy of machine learning and data analytics in P2P lending.
• Provided a practical methodology for applying and evaluating data science in finance.
• Assessed strategy performance beyond mere predictive accuracy using real data.

Conclusions:

• Data-driven strategies significantly enhance investment decisions in P2P lending.
• The developed framework offers a flexible and comprehensive resource for data science education.
• Practical evaluation of investment strategies is crucial for real-world success.