Quantamental investment has emerged in recent years as an effective new investment approach that combines the strengths of traditional fundamental analysis and quantitative analysis. It leverages big data, AI and machine learning to extract signals and insights, while also incorporating human expertise and logic to validate the investment rationale. This hybrid approach aims to capitalize on the stock selection skills of fundamental investors and the rigor and scalability of quantitative techniques. The term ‘quantamental’ was coined to describe funds that blend quantitative and fundamental analyses. In this article, we will examine the characteristics, rationale, strategies and performance of quantamental investment.
Quantamental investment leverages both human insights and data/algorithm-driven signals
The key advantage of quantamental investing is that it utilizes both human expertise and judgement as well as quantitative models and algorithms. Fund managers can leverage their extensive industry and sector expertise to guide stock selection, while optimizing their process using big data, predictive models and backtesting. This allows them to process huge amounts of data that would not be possible manually. The quant models help remove subjective bias and enforce a disciplined, rules-based approach. Overall, the quantamental approach aims to benefit from the stock selection skills of fundamental investors as well as the consistency, objectivity and scalability of quants.
Quantamental strategies combine factors from both fundamental and quantitative analyses
Quantamental fund managers develop specialized factor models that incorporate fundamental factors like valuation ratios, earnings quality, profitability, growth prospects etc along with quantitative factors like price momentum, volatility, trading volume etc. The investment process typically involves fundamental analysts filtering a universe of stocks to arrive at an investible universe, which are then scored and ranked using a quantitative model. This blended approach allows investors to benefit from factors that drive stock returns in both schools of investing.
Quantamental funds have outperformed benchmark indexes over the long term
While the quantamental approach is still evolving, early evidence suggests combining fundamental and quantitative analysis can enhance returns. According to Greenwich Associates research, 23 out of 30 quantamental equity strategies they examined outperformed their benchmarks over the past five years. Another study by eVestment showed 68% of fundamental active managers on Wall Street use quantitative techniques to supplement their stock-picking – a sign of the growing acceptance of quants among old-school stock pickers. As quant models become more sophisticated at analyzing Sentiment, natural language etc, their insights will likely complement fundamental analysis even more.
Challenges remain in integrating fundamental and quantitative skillsets
Though promising, quantamental investing faces hurdles in seamlessly blending both schools of investing. Quants need to better understand how to break down mathematical models into fundamental logic and insights that analysts can understand and act upon. Fundamental investors have to get up to speed with quantitative techniques and machine learning approaches. Bridging the cultural gap between the two approaches remains an ongoing challenge. Investment firms also need personnel who can act as ‘translators’ between the two disciplines. But as quant technologies continue advancing, quantamental funds can exploit big data to generate alpha in innovative new ways.
Quantamental investing aims to capitalize on the strengths of both fundamental and quantitative investing by combining human judgement with data-driven insights. Though adoption is still in early stages, academic research and fund performance suggests quantamental strategies that blend factors from both approaches can generate excess returns and diversification benefits.