Introduction
Since passive long-term returns are achievable through investing in an index fund investing vehicle mimicking the S&P 500, investors utilize this as a benchmark to establish investment performance. Since a return of about 10% can be achieved in the long term with minimum effort or skill, active investment propositions need to accomplish significantly more than such a threshold to justify themselves (Greenwald et al., 2020).
Suggested Tools and Techniques
In designing a Python-based AI value investing algorithm, the following tools will be considered:
- Backtrader: Python framework for backtesting and strategy optimization.
- Yahoo Finance API / Alpha Vantage: Delivers 10-year historical S&P 500 data.
- pandas, NumPy: Tools for data manipulation.
Planned Project Timescales
The period scheduled for this part of the project will run from June 10, 2025, until June 23, 2025.
Expected Outcomes
It is expected that the AI value investing model will be able to surpass the performance of the S&P 500 between 2015 and 2024, lending further credibility to the use of AI as an investment aid in today's complex investment landscape.
References
Greenwald, B. C., Kahn, J., & Bellissimo, E. (2020). Value Investing: From Graham to Buffett and Beyond (2nd ed.). Hoboken, NJ: Wiley Finance.
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