The first hurricane forecast was issued in 1873 by a Jesuit cleric living in Cuba. Father Benito Viñes was said to be able to interpret cloud movements hundreds of miles ahead of a storm. Some attributed his occasional accuracy to supernatural powers.
Today, finance practitioners and lay people alike are observing the clouds in a similar fashion to discern signs of a coming storm. Each new report of a previous quarter’s activity represents debatable changes in temperature, wind speed and direction. And each interpretation yields varying forecasts of potential size, ferocity and impact. Often the speculation is based largely on old data – clouds that passed months ago – and filtered through memories and emotions. Our super-saturated media and social environments, which thrive on the hyperbolic and catastrophic, foment this practice. Doom and gloom scenarios of coming recessions and financial collapse yield clicks that sell advertising and leave little incentive for clear-headed interpretation of reliable data.
This is unfortunate because reliable data has never been more abundant. Satellites show us the precise location and size of a tropical storm, and hurricane chaser planes can tell us the exact speed of the winds from the very eye to the outermost bands. Similar data is available, in quantity, on large swaths of the U.S. and global economies, collected, tabulated, and disseminated by public sector agencies and private companies. And we are fortunate to have more refined and powerful tools to analyze this data more objectively to determine where we are in a business cycle.
Until recently, datasets covering large portions of the U.S. economy were limited to macro information – unemployment claims, building permits, household debt levels, to name a few – served up weekly or quarterly through Washington, D.C. Today, digitalization permits near real-time access to a treasure trove of micro-level data delivered straight from the real economy, based on real performance, not surveys of sentiment.
This new view, from inside the storm, is made possible by a new set of tools: those that can extract and instantly process this data and convert it to knowledge usable by decision makers. For example, HCx, our data warehouse, has access to over 700 million rows of data, which are growing by over three hundred thousand rows a day. We monitor on an intraday basis information on new loan origination, payment activity, repayments speeds, aging experience, and default incidence, among others, on a universe of approximately 100,000 consumer and small business credit assets at a given moment. Layering the HCx capability into macro data can help us spot wisps of billowy clouds well before the sky begins to darken.
While this deeper, more granular dive into real time data does not allow us to know the future, it does give us good guidance regarding the forward path of the economy and its range of outcomes, ultimately enabling a less emotional debate about what’s next. Inflection points, which generally tend to be marked by heightened volatility and concern, do not always lead to significant corrections or accelerations of underlying trends. Just as some storms simply slow down and die at sea, the emotions of the crowd can be calmed by an empirical understanding of current conditions.
Father Viñes’ 1873 forecast raised warning flags from the Gulf of Mexico to Newfoundland, where the Nova Scotia Cyclone eventually made landfall. In search of greater accuracy, Viñes founded the Meteorological Observatory at Havana’s Royal College of Belen, established a network of observation sites and developed the first data driven method to forecast tropical cyclone movement. His work continues today at the US National Hurricane Center and inspires us to always remain focused on the data at hand – the real time digital tracking of millions of data points tied to the real economy – and to not simply look to the sky and debate which way the winds might blow.