Lies, Damn Lies, and Statistics

Lies, Damn Lies, and Statistics

Randall Coleman, CFA

I was born at St. Thomas’ Hospital in London, the same hospital where Florence Nightingale modernized nursing. Besides reforming hospital sanitation with what we view today as standard operating procedures (handwashing, sterilization of equipment, etc.), Nightingale was known for her use of statistics and data visualization to make her case for shifting public policy and hospital management.

The diagram above shows the increasing death toll among British troops in Crimea caused by infectious diseases month on month from April 1854 – the bigger the blue area, the more deaths. The diagram on the left shows the deaths from April 1855 onwards once basic cleaning measures were put into place. The dramatic decline in mortality shows there is no doubt Nightingale’s procedures worked. Her use of data collection and visualization were instrumental in implementing her controls.

Investors also rely on data and statistics to shape their models and projections. While data series come from a myriad of providers, one of the granddaddies in the business is the BLS, or Bureau of Labor Statistics. The BLS is a critical source of economic data which drives market forecasts and shapes investment policy and decisions.

DOGE layoffs may have compromised the accuracy of government data

Recently, however, there have been alarming reports that BLS data quality is declining. An article on CNN a few weeks ago claims that the BLS’s key inflation measure, the Consumer Price Index (CPI) has become less precise. The BLS posted a notice on its website, stating that it stopped collecting data in three cities and increased imputations (guesstimates, essentially) for certain items.

Investors should ask themselves two questions regarding this development: What are the implications of a supposed data quality degradation, and should we be concerned? Will this have lasting investment ramifications?

The implications can be numerous and varied. Perhaps the most obvious would be the risk of policy mistakes. Given that the Federal Reserve and federal government rely on BLS data to guide rate hikes, stimulus, and spending, an inaccurate inflation reading may delay rate changes, thus putting the Fed’s mandate for price stability in jeopardy. Once the inflation genie is out of the bottle, it is increasingly difficult to put it back in.

For ACM’s portfolio managers, the two main implications are on market volatility and alternative data sources. Investors rely on BLS data to gauge the health of the economy and anticipate Fed moves. If the data is questionable, uncertainty rises. Because of that uncertainty, markets may become more reactive to minor data releases or rumors, increasing short-term volatility.

No economist or portfolio manager relies on a single provider of data. The BLS series are one of many inputs into our internal models. If we suspect data degradation in any, not just BLS data, we can adjust for that by lowering its importance (or weight) in our models and raising alternative data measures. Perhaps even more important is paying attention to on-the-ground information. We listen to what companies themselves are reporting. It goes without saying, but they’re the ones closest to the heat of the fire. Monitoring conference calls, company presentations, earnings reports, interviews, etc., keeps us with an ear to the ground and to the closest suppliers of robust data. By shifting between various suppliers of data, our models remain robust, despite any potential issues with a single source.

As a cyclist, I record my long bike rides on an app called Strava. Unlike financial professionals building BLS information into their projections with sources of alternative data, the cyclist me relies on a single source of data collection. If it’s not on Strava, did it happen?

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