Seeing Through the Truth of U.S. Economic Data: Using "Time Lag" to Unmask False Prosperity

Recently, many people have asked me: "Can the economic data released by the US government be trusted? Could it be manipulated?"

My core idea is very simple: don't just look at single data; it should be compared within the entire time series.

The economic data of the United States is divided into three categories:
The first category is Leading Data.
For example, ISM Manufacturing PMI and money supply act like a "trailer" that informs you about the possible direction of the economy 3 to 9 months in advance.

The second type is synchronous data.
For example, the number of initial jobless claims and retail sales each week reflect "the here and now."

The third category is Lagging data.
Indicators like unemployment rate and non-farm payroll numbers are "historical reflections" and typically show the impact of economic changes several months later.

Under normal circumstances, an economic downturn unfolds in order: leading indicators turn first, followed by synchronous data weakening, and finally lagging data worsens.
So, if you see that the leading indicators in the U.S. have been declining for several months, but the lagging indicators (especially employment) are still performing well, then be cautious as this may indicate that the data is being manipulated, or the government wishes to delay the release of bad news.

Real case: In the second half of 2022, the US ISM Manufacturing PMI fell below the expansion line consecutively, and the growth rate of money supply also declined rapidly. These leading indicators were warning of an economic slowdown. However, the unemployment rate announced at the beginning of 2023 dropped to a 50-year low, and the market was impressed by this set of "beautiful data," leading to a short-term rebound in the US stock market. As a result, less than three months later, the unemployment rate began to rise again, and a wave of layoffs in the manufacturing sector erupted. Those who had looked at the leading indicators in advance had already reduced their positions to avoid risks at the rebound peak.

Of course, it is almost impossible to have long-term, comprehensive control over all the data in the United States, as the data is scattered across multiple agencies such as the Bureau of Labor Statistics, the Institute for Supply Management, the Federal Reserve, and the Census Bureau, which can cross-verify each other.

The real opportunity for investors lies in: when the market is misled by lagging data and ignores leading indicators, you can position yourself at the moment when asset pricing is incorrect and earn excess returns.
Remember to look at the three types of data: leading, coincident, and lagging together. This way, you can judge the authenticity of the data and make decisions ahead of others.