Financial market theory and practice has come a long way since the recently deceased Harry Markowitz minted modern portfolio theory (MPT) in 1952.
But after seven decades of theoretical refinements and data-led practical advances, the finance industry could now be too stuck in its ways to make real fundamental breakthroughs on a par with Markowitz, Research Affiliates (RAFI) founder, Rob Arnott, argues in a new paper.
“Both the academic and practitioner communities in our industry are perhaps too complacent, and too invested in maintaining the current equilibrium or paradigm. Too many people say, ‘Assuming this, then we can decide that.’ Too few are willing to question those basic assumptions,” Arnott says.
“As fiduciaries, we owe it to our clients to be less accepting of received wisdom (which is too often dogma) and more willing to explore the implications of errors in the root assumptions of finance theory. These basic assumptions often fail when they are tested. Flawed assumptions are not bad; they are our best source of learning. We can learn more and earn more by exploring the many gaps between theory, received wisdom, and reality.”
The analysis, published in the Journal of Portfolio Management, suggests quantitative finance participants have become broadly entrenched in two, antagonistic, schools of thought about how markets work: the theory-first proponents and the data-led quantitative “nerds”.
Arnott says the theory-first approach typically “disregards data and assumes that when the data does not support the theory, the data—not the theory—is simply wrong or otherwise driven by anomalous outliers”.
Data-diviners, on the other hand, have focused on uncovering deviations from theory in the historical record to identify potentially profitable ‘factors’.
“Even millions of tests can lead us off course,” the paper says. “Data-first means data mining. Relentless data mining is NOT scientific method. Using a backtest to improve the backtest gives us a great backtest, not a good product.”
However, Arnott advocates for a third-way that straddles the theory-data divide by employing Bayesian logic to produce “lasting insights”.
“A Bayesian will blend data and theory, giving neither preeminence,” he says. “Both depend upon the other. A theory is developed with care to identify validating empirical tests and then tested against the data. The data is not used to develop the theory.”
For example, Arnott says the behavioural finance movement, as pioneered by Nobel laureate Daniel Kahneman, has seriously challenged the neoclassical finance assumption of the ‘rational investor’.
“Isn’t it better to recognize elements of truth in seemingly incompatible theories?
“Economics is not physics. Neoclassical finance and behavioral finance both have important insights. By recognizing this possibility, we not only gain a richer understanding of the markets, but we may also help catalyze finance’s next paradigm shift and advance our little corner of the dismal science to the next stage in its evolution.”