A powerful new strategy, quantamental investing, is being employed by an increasing number of investment managers to enhance the quality of their decision-making and deliver more consistent returns.
The principle behind quantamental investing can best be illustrated by an example drawn from the chess world. In 1997, world chess champion Garry Kasparov took on IBM supercomputer Deep Blue for the second time.
The first time they played, Kasparov beat his automated adversary, but the second time the algorithm proved too powerful.
Kasparov was initially dejected, but then he had a powerful insight. The computer had ‘outwitted’ him, not through daring and imagination, but through the power of its algorithms. He realised that human players informed by computer programmes outperformed both humans and computers playing alone.
A similar dichotomy to that seen in chess is evident in many other spheres of human activity, including investing.
Traditionally, investment managers have maintained a clear distinction between machine intelligence and human insight, with quantitative methods on one side and fundamental analysis on the other.
Each approach has particular strengths and weaknesses.
The processing power of quantitative analysis is truly astonishing, orders of magnitude beyond anything even the most brilliant human mind can achieve. Big data analysis is unmatched at pattern detection.
But how do you tell which patterns are useful and which might be irrelevant, or even misleading? How do you distil the signal from the noise? Patterns that may have proved fruitful in the past may no longer apply as market conditions change.
Algorithms cannot predict regulatory change, emergent technologies or changes in the composition of firms.
For example, the algorithm cannot incorporate the financial impact of a material acquisition or disposal by a firm or anticipate the impact of a strong new competitor to a dominant firm.
Expert fundamental analysts can anticipate political and legislative changes, observe how businesses are adapting to new technologies and make educated inferences about business trends. With experience, they have a deep understanding of firms and the sectors they operate in and can relate their operations to broader political-economic outcomes.
Fundamental analysis remains indispensable to assessing value and anticipating change.
Humans are insightful, imaginative, adaptable and critical, but human cognition is inherently limited. Not only are there limits on how much data humans can process, but everyone has cognitive biases and finite knowledge.
Quantamental investing is a strategy that combines the best of both methods. It entails evaluating equities according to both fundamental and quantitative analysis. Each set of findings informs the other, and each stock pick has to be justified in terms of the integrated result.
This approach means that a richer data set informs each selection.
Essentially, the process creates a rational framework within which decisions are made. That framework constrains human biases and identifies relevant data amid the noise of algorithmic analysis.
The process has three components. Firstly, fundamental analysis is applied to stocks in each equity sector, assessing them according to quality-growth. Stocks are simultaneously scored according to quantitative analysis.
Next, the fundamental assessment and quantitative ranking of each stock is compared. Focused research is performed based on the comparative scores of each approach. The integrated findings inform portfolio construction and guide ongoing portfolio management.
At each stage of the process, analysts must account for their decisions in light of quantitative findings. The rigorous framework means there is no chance for stagnation or style drift.
Although quantamental investing requires considerable inputs, it is a transparent and consistent investment approach that has the potential to offer returns that are superior to, and more consistent than, traditional methods.