As finance/investment is an always-changing business, two prevailing methods of investing have taken place over the years, fundamental and quantitative investment. Historically these have been perceived as opposing approaches, with investors and fund managers often firmly situated in one camp or the other. Still, over the last few years, we have seen something quite intriguing – a vying and merging of these two dominant forms of investing. The piece delves into this convergence, what is triggering it, and the implications for investing in generations to come.
Understanding the Basics
Fundamental and Quantitative Investing — A brief recap Let us revisit briefly what Fundamental, as well as quantitative Investing, is before we end up in convergence.
Fundamental Investing
The fascination of Money Anyway, this is what fundamental investing has always been about – going through all the details that provide information on a company´s cash-generating capacity, competitive place, and growth trajectory. Fundamental investors analyze financial reports, industry developments, and the capabilities of management to find undervalued or high-growth companies. All of this seems to be closely related to legendary investors like Warren Buffett and Benjamin Graham.
Quantitative Investing
In contrast, quantitative investing leans heavily on mathematical models and computer algorithms to find opportunities. A quant long/short strategy leverages extensive data to detect associations or patterns that lead the way on future prices, and other related styles will identify overpriced (or underpriced) assets to initiate trades. This approach has become popular with the gaining computational power and big data analytics.
The Drivers of Convergence
There are several reasons for the convergence of these two investment philosophies:
Technological advances — the exponential increase in computing power and data storage, for example — have made it easier to apply quantitative methods within fundamental investment analysis.
Big Data and Alternative Data: More types of data (such as satellite imagery, and social media sentiment) are not previously available to alternatives in either fundamental or quantitative research.
Machine Learning and AI — Such technologies are allowing deeper analyses of structured or unstructured data, filling the divide between computational power and human insight.
Market Efficiency Gains: The more efficient a market the more you have to search for an edge which is pushing investment managers towards hybrid approaches.
Regulatory Pressures: Some of the increased scrutiny and regulations require high consistency behind investment processes but this imparts a need for more quantability, even at firms that have historically been fundamentally focused.
The Convergence in Practice
The commoditization of fundamental and quantitative strategies becoming closer to each other manifests itself in several ways:
Quantamental Investing
The most direct fusion of these two approaches in investing is what’s now called “quantamental” (a portmanteau of quantitative and fundamental). Quantamental refers to the combination of quantitative tools with traditional fundamental analysis that retail investors and institutions use when analyzing securities. They may, for instance, apply natural language processing to scan through earnings call transcripts or machine learning algorithms to unearth patterns in financial statements that a human analyst would otherwise overlook.
Systematic Fundamental Investing
The next, more scientific approach involves systematizing the most basic investment process. Rather than depend solely on human judgment, systematic fundamental investors employ rules-based methods to filter for stocks that satisfy certain criteria from a fundamentals prism. This prompts a more disciplined and scalable approach to fundamental investing.
Enhanced Quantitative Models
Quantitative investors have started to integrate more fundamental takeaways into their models. An example might be using machine learning algorithms to determine what if any of the fundamental factors are significant predictors of future returns, as opposed (or in addition) to just price and volume data.
AI-Powered Research Assistants
AI-powered tools are used by both fundamental and quantitative investors to enhance their research capabilities. These capabilities will allow AI to quickly analyze a large swath of data, obtain insights, and even draft preliminary research reports that can enable human analysts the opportunity to move up in terms of analysis & decision-making.
Implications for the Future of Investing
The intersection of fundamental and quantitative approaches has several important consequences:
Skill Set Evolution — Investment professionals will need to combine traditional financial analysis with data science and programming skills.
Investment decisions less influenced by biases: Investment strategies that link available and income-based quantitative technology to basic insights have the promise of delivering more logical, as well being less biased investment opportunities.
Sophistication for the masses: With hybrid approaches entering the mainstream, retail investors might increasingly be able to tap advanced investment strategies via ETFs and robo-advisors.
The larger your data set, the more broad-based you can go, and with that in mind there are a few overlapping trends: — People who use advanced software to make their algorithmic trades possible will have better fundamentals to choose from; Conversely, the people with no prior quantitative experience may simplify the returned metrics causing them at some point need access alt-data because currently they made these models relatively easy due wide net processes covering many stocks.
The emergence of New Market Inefficiencies: As an increasing number of actors adopt hybrid techniques, it could also potentially lead to new markets characterized by a lack of consensus — and as such novel data sources or analytical tools might need to be innovated upon (which would likely complement the positivist approach instead fix both views against each other).
Conclusion
The combination of fundamentals and quantitative investing is a huge step forward in the state-of-the-art investment landscape. Rather than thinking of these two approaches as in conflict, savvy investors have begun to appreciate that human insight and computational power can be a potent combination. As this will be a continuing trend further innovation in investment methodologies can come with more efficient markets for the benefit of investors.
The paths will intersect, but it’s worth noting that this convergence is not as straightforward. Concerns over data privacy, explainability of black-box models, and systemic risks due to propagation effects (i.e. implementation) compounded by the larger market convocations or cascading failures may also have to be taken into serious consideration
In the coming years, best-in-class investors will likely be those who blend fundamental and quantitative approaches most effectively — utilizing technology while still possessing a clear base of knowledge in economic and business fundamentals to drive long-term value creation.