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In recent months, many investors and financial publications have been using “real-time” data to assess the economic fallout of—and recovery from—COVID-related lockdowns. In our view, these nontraditional data can provide timely and useful glimpses of how certain segments of the economy are faring. They aren’t a silver bullet, and they don’t predict economies’ or markets’ direction. But we think they can illustrate how reality is evolving relative to sentiment in the very near term.

Most economic data produced by private research outfits and government entities are published monthly, quarterly or annually—and released weeks after the reference period ends. The speed with which COVID-related business closures and shelter-at-home orders decimated commercial activity had many clamoring for faster looks. Enter lesser-known “high-frequency” economic data points. These include the number of travelers passing through TSA airport checkpoints, hotel occupancy, online restaurant reservations, rail freight volumes, credit card transactions, road traffic, retail foot traffic and mortgage applications, among many others. Collectively, they represent an attempt to shed light on consumer demand and business activity much sooner than traditional measures.

For example, daily TSA checkpoint totals and an estimate of online restaurant reservations in seven countries are available the next day. Map-making and navigation-technology company TomTom tracks traffic levels in major cities by the hour. For comparison, US consumer-spending data—which these high-frequency measures can hint at—typically arrive about four weeks after the month a given report covers. Other data points, like US electricity demand and worldwide pollution levels (both updated hourly), can give a sense of business activity—especially heavy industry. Meanwhile, the Fed releases monthly US industrial production statistics about two weeks into the following month. The Bureau of Economic Analysis (BEA) releases its initial estimate of GDP—the most comprehensive (though still imperfect) measure of a country’s economic output—a month after the relevant quarter. In many other countries, it is several weeks later.

Thanks to their relative timeliness, high-frequency data have several uses. First, they can indicate current conditions in a narrow economic segment. For example, if hotel occupancy and TSA checkpoints are down significantly from the same time a year ago, that is a reliable indication that tourism and business travel are down, which can have knock-on effects on dining and retail. Second, they shape investor expectations, potentially sapping surprise power from subsequent official releases—or heightening it, if the two diverge substantially. Third, they preview turning points in trends before traditional data confirm them. For example, states started reopening in May. Consumer spending results for that month won’t be available until June 26, and they will reflect a mix of lockdown and reopening, since states and cities are relaxing COVID restrictions at different speeds. This makes it difficult to get a clear, timely look at consumers’ shopping patterns. High-frequency data help fill the gap. Through June 8, TSA checkpoint traffic has nearly quintupled since the April 14 year-to-date low.[i] US online restaurant reservations—which were down between -95% and -100% y/y from March 19 to May 14—were down -75.1% y/y on June 8.[ii] While both remain far below pre-lockdown levels, these data points suggest reopenings are fostering more discretionary spending, well before traditional data confirm it.

By shedding light on changing economic conditions, high-frequency data can give investors a sense of whether sentiment is out of touch with reality. At extremes, this can even help identify market turning points. Presently, while the above real-time data points and several others offer glimpses of a nascent rebound, most financial commentators hype dismal, less-timely indicators while dismissing green shoots or presenting them as bad news because they sap political will for more alleged government “support.” In our view, while it will be clear only in hindsight, this supports the idea that the large stock market rebound since the global low on March 23 is the start of a new bull market, not a false rally.

While real-time data can reveal much, like all data, they have limitations. For example, they are often proxies for commercial activity and don’t always capture actual dollar amounts spent or goods and services produced. Retail foot traffic, for instance, indicates how many people are visiting stores. More traffic implies more spending. But to find out how much those visitors actually spent—both in-store and online—you have to wait for the final retail sales report. Meanwhile, credit- and debit-card transaction data capture actual purchases and cover much more than retail sales, but they exclude non-card transactions.

High-frequency data can also be subject to skew from factors unrelated to what they are trying to measure. For example, rising mortgage applications could reflect improving housing demand—or simply more homeowners trying to refinance. Air pollution—particularly in countries with more heavy industry—can suggest industrial production levels, but there are other, non-factory sources also. After one measure of air pollution in China surged in April, Chinese officials noted that spiking straw-burning and sandstorms in the month had contributed to the increase, not just higher industrial activity. In the US, meanwhile, plunging electricity usage may have exaggerated the extent to which shelter-in-place orders destroyed economic activity. Since the US economy is predominantly services, many white-collar employees were able to work from home when states and cities ordered offices closed—and houses are generally much easier to cool and heat than offices.

In our view, despite their limitations, real-time data are a good way to test the veracity of common economic narratives during this unique stretch. We doubt their utility persists much afterwards, though, as hour-by-hour blips in narrow data gauges aren’t likely to prove a helpful tool in the long run—particularly since so many market participants are looking at them now.

Investing in stock markets involves the risk of loss and there is no guarantee that all or any capital invested will be repaid. Past performance is no guarantee of future returns. International currency fluctuations may result in a higher or lower investment return. This document constitutes the general views of Fisher Investments and should not be regarded as personalized investment or tax advice or as a representation of its performance or that of its clients. No assurances are made that Fisher Investments will continue to hold these views, which may change at any time based on new information, analysis or reconsideration. In addition, no assurances are made regarding the accuracy of any forecast made herein. Not all past forecasts have been, nor future forecasts will be, as accurate as any contained herein.

[i] Source: Transportation Security Administration, as of 06/09/2020. [ii] Source: OpenTable, as of 06/09/2020.

The Reuters editorial and news staff had no role in the production of this content. It was created by Reuters Plus, part of the commercial advertising group. To work with Reuters Plus, contact us here.

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