By Rick Roche, CAIA, Managing Director of Little Harbor Advisors, LLC

Part 1 of a two-part series on Alternative Investment Data (Alt-Data) in the COVID Era.

In Part 1, the author makes the case for high-frequency, short-interval Alt-Data while discussing three primary drawbacks of interpreting official economic statistics amid a global pandemic.

The profound toll of the Global Coronavirus Crisis is being measured by lives lost and its accompanying economic fallout in terms of staggering unemployment, lost income, bankruptcy, and recession. The novel Coronavirus pandemic has already had an enormous impact on global GDP and the investment climate. It is an important reminder of the humbling and daunting task that an epidemic represents to investment professionals: we are essentially tasked with shaping favorable outcomes and solutions for all of the underlying constituents we serve.[1]

A core belief is that the Coronavirus and its disease, COVID-19 remain – above all else – a human tragedy. The virus knows no borders and reminds us of our existential vulnerability. Sadly, this pandemic also highlights society’s inequalities, as rates of infection and death are often worse for lower income-earners, people of color and older individuals.

The accompanying rapid regime changes in 1Q and 2Q 2020 demonstrate that many quantitative investment models–whether programmed by machines or humans–have run amuck. “If, statistically speaking, something is really an unknown, then all models–human models and quant models–will struggle,” stated Miguel Noguer Alonso, PhD, co-founder of the Artificial Intelligence Finance Institute.[2]

Using real-time, high frequency alternative data may enable asset managers to rapidly adjust exposures to cope with the consequences of events previously unencountered. Alternative data (Alt-Data) flows in ways that conventional economic indicators, which appear only periodically and lag on-the-ground in reality, do not.

Select alternative datasets are timelier and may be more reliable than “official” statistics. For example, data on air quality, auto congestion, public transit, and foot traffic were used to determine how quickly China’s workers returned after factory closures. It is important to establish “real-time base rates” of economic activity in other regions affected and to establish comparable periods of market dislocations and turbulence.[3]

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