Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for Systematic Equity Selection
arXiv:2606.24575v1 Announce Type: new Abstract: Modern finance relies heavily on complex machine learning models to find patterns in the stock market. However, as these AI models get more complicated, they often memorize short-term market noise instead of finding companies with real, lasting value. We designed this research to test if Benjamin Graham's classic value investing rules could act as a mathematical "low-pass filter" to keep these modern models in check. We built three different sets of features - pure Graham rules, modern market factors, and a mix of both - and tested them against h
The proliferation of complex AI models in finance necessitates new methods to prevent overfitting and ensure robust long-term performance, leading researchers to explore hybrid approaches.
This research suggests a more robust and interpretable future for quantitative investing by integrating timeless value principles with modern AI, potentially reducing the 'black box' risk of pure machine learning models.
Investment strategies may evolve to explicitly incorporate foundational economic principles alongside predictive AI, leading to more stable and less noise-sensitive asset allocation and security selection.
- · Quantitative fund managers utilizing hybrid models
- · Value investors seeking systematic application
- · Investors seeking more robust, less volatile returns
- · Purely black-box AI-driven funds susceptible to market noise
- · Investors relying solely on short-term factor momentum
Increased adoption of hybrid AI-classic finance models in institutional investment.
A potential re-emphasis on fundamental analysis in conjunction with advanced analytical tools.
Long-term capital allocation shifts towards companies demonstrating enduring value, guided by refined AI-driven screens.
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Read at arXiv cs.AI