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What’s normal for financial data?

"Norming" indicators such as the St. Louis Fed Financial Stress Index

Financial data are useful for many reasons. One (perhaps subtle) reason is that they are never revised. Markets determine the prices and quantities of assets at the time of the transaction and that’s that. As such, once you observe the value of a particular financial variable at a particular point in time, you know it will remain at that value forever.

One might assume, then, that the St. Louis Fed Financial Stress Index, which includes 18 series of financial data to measure stress in the markets, would also remain the same forever. Well, the graph shows us something different: It plots 10 distinct vintages of the index starting with the first, from March 2010, and then one from every year since then. Despite the fact that this index is composed entirely of unrevised financial data, the lines of past data are not exact replicas of each other, so the index clearly changes over time. But the reason for this variation lies in the construction of the index and not in the underlying data.

The index is calculated as a weighted average of the underlying financial series. So, what changes are the weights used in this calculation when new data are released. These weights are functions of the means, variances, and correlations of those 18 financial data series used in the index; and these means, variances, and correlations are recalculated every time another observation is added. In March 2010, the first index was constructed based on data ranging from 1993 through early 2010. In the most recent vintage, from April 2019, the index was constructed based on data ranging from 1993 through early 2019. Since the means, variances, and correlations differ across the two samples, the weights differ as well, and therefore we have different paths for the index.

Why should we care about this? The entire premise of the index is that it is useful for identifying periods of financial stress that are higher or lower than normal. But what’s normal? Take a look at the “Notes” section of the series page for this index: Stress is considered normal when the index takes the value zero. So an index value greater than zero indicates above-normal levels of financial stress and a value less than zero indicates below-normal levels.

Unfortunately, the index’s position above or below zero changes across vintages. For example, consider the index’s value in 1996: The first (March 2010) vintage of the index indicated that financial stress was less than normal in 1996. But this isn’t the case in more-recent vintages, which indicate that financial stress was higher than normal in 1996.

The bottom line is that “normal” is relative to the total sample available. It’s not an absolute statement about financial markets at any given time and shouldn’t be interpreted as such.

How this graph was created: From ALFRED, search for “St. Louis Fed Financial Stress Index.” Check the appropriate series in the search results and click “Add to Graph.” By default, ALFRED creates a bar chart; to change to a line graph, use the “Graph type” menu under the “Format” tab. To include the earliest vintage, click on the “Edit Graph” button, go to the “Edit Line 1” tab, and select vintage 2010-03-05. To add the next vintage, go to the “Add Line” tab, search for and select the St. Louis Fed Financial Stress Index, click “Add data series,” go to the “Edit Line 2” tab, and select vintage 2011-03-03. Repeat this process until all desired vintages have been added. Finally, adjust the range of the plot so the end date is 2010-02-28.

Suggested by Michael McCracken and Joseph McGillicuddy.

View on FRED, series used in this post: STLFSI

The booms, blips, and dips of dot-com, telecom, and cultural transmissions

Employment in the information sector

Some call the past few decades a new industrial revolution, given the dynamic emergence of the information economy. The graph above shows employment in information services, and, indeed, there’s strong growth in the sector, especially up to the dot-com crash in 2000. But since then, the sector doesn’t seem to have expanded its payrolls much. In fact, once you take out the boom, current data seem to follow the previous trend.

Now, the employment classification for information services includes more than just jobs related to the internet. NAICS code 51 encompasses anything related to the diffusion of information. So, it’s also phone companies, movie makers, broadcasting, newspapers, and software. Clearly, some of these sub-sectors have suffered from the rise of the internet economy. Thus, the long trend hides a considerable amount of churn within the sector itself.

Also, notice that there are some downticks. For employment data, this is usually due to strikes. The big downtick in August 1983 is due to a 22-day strike of close to 700,000 workers across the phone industry.

How this graph was created: Search for and select “employment information services.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: USINFO

Where in the world are banks profitable?

World Bank data on ROA for 173 economies

There are many indicators to help us evaluate the U.S. economy, but international data are a little more limited. Which is why FRED is fortunate to have World Bank data to compare economic conditions across countries. Today, we look at how well banks are doing—according to their return on assets—all over the world. Measuring banks’ ROA is relatively simple: Aggregate the net income of all commercial banks in a country and divide this sum by their total assets.

The graph above shows three countries with contrasting fortunes. The most dramatic is Greece, whose banks have been struggling with bad debt. Then there’s Kenya, whose banks are doing surprisingly well, at least under this dimension. Finally, the United States is in the middle, with a noticeable dip during the financial crisis and lower returns since that crisis.

FRED has regional data that you can map using GeoFRED. And that’s exactly what we have in the map below: The world’s most profitable banks (darker colors) tend to be in the Global South, with Moldova, Belarus, and Iceland also doing very well.

How these graphs were created:The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DDEI05GRA156NWDB, DDEI05KEA156NWDB, DDEI05USA156NWDB

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