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Have you heard the news? News can affect markets

The effects of economic news on expectations of future financial performance

Posted on January 27, 2020

FRED’s all about data, which economists often use to conduct or test their research. So let’s look at some of that research…

In a recent St. Louis Fed working paper, economists Maximiliano Dvorkin, Juan M. Sanchez, Horacio Sapriza, and Emircan Yurdagul study how the arrival of news affects emerging markets. They use a logic from a 2006 paper by Beaudry and Portier to identify news events—aka “shocks.” The idea is to compare a financial index that captures the expected future performance of the economy with a measure of current performance. They identify “good news” when the expected performance variable improves without any proportional improvement in the current performance variable. On the flip side, they identify “bad news” when the expected performance variable declines without any proportional decline in the current performance variable.

Because their research focuses on emerging markets, they use the JPMorgan Emerging Market Bond Index (EMBI) spread, which captures the risk of sovereign default, as their measure of future performance. They show a connection between the arrival of bad news and an increase in the EMBI spread that can’t be accounted for by current data. They also find that these shocks are important in accounting for fluctuations in these emerging economies and that these economies can’t shield themselves from news shocks by extending the maturity of their debt.

Now, back to FRED: Data can be used to test and illustrate the logic behind this research. The graph shows (in blue) the St. Louis Fed Economic News Index that’s used to predict the value of current real GDP before the BEA releases the official data. Assuming this index is good at capturing current news, we should see a strong correlation between this index and a financial index affected by the future performance of the economy. The index we chose (in red) is the S&P 500: This measure of the value of the stock market, as a measure of the expected performance of U.S. companies, serves as our measure of future performance.

The graph shows that the S&P 500 and Economic News Index move closely together, which suggests the logic is correct and that additional research could identify how news affects the U.S. economy.

How this graph was created: Search for and select “St. Louis Fed Economic News Index: Real GDP Nowcast.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select the S&P 500 series; then click “Add data series.” From there (the “Edit Lines” tab), adjust the units to “Percent Change from Year Ago” for comparability with the news index. Now, both lines will be on the same graph, but their scales are quite different. To better compare the two, you can select “Format” and change the y-axis position to “Right” for the S&P 500 line.

Suggested by Ryan Mather and Juan Sánchez.

View on FRED, series used in this post: SP500, STLENI

Nowcasting current activity

How's the economy doing...now?

Posted on January 11, 2018

Forecasting, as we all know, tries to predict the future. For FRED’s purposes, that prediction is how a statistic will evolve. Nowcasting, a variant of forecasting, looks at the current state of a statistic that hasn’t yet been released because the period of coverage is not yet over. Nowcasting is one way to examine current economic activity; another was discussed in a previous post.

GDP is a popular target for nowcasting, and FRED covers the nowcasts of several Federal Reserve Banks—with the Federal Reserve Banks of Atlanta (GDPNow) and St. Louis nowcasts shown here along with the final GDP numbers released by the Bureau of Economic Analysis. To gaze into the future, focus on the very last data point for each nowcast (Q4 2017, shown here), as this is what nowcasting is all about.

The earlier data points for the nowcasts are the last estimates before the first (early) GDP release by the BEA, which is typically revised over time to create the green line. Like the BEA’s GDP numbers, the nowcasts are revised several times per month.

We see that there are disparities between the nowcasts. While they are in principle all based on the same information, estimates can differ because of different statistical methodologies and how they are revised over time. And what about the differences between the nowcasts and the final data? The BEA obviously has the advantage of access to more raw data and more time to refine the numbers.

How this graph was created: Search for “nowcast” and all the series you want should appear. Select the relevant series and click “Add to Graph.” From the “Edit Graph” menu, use the “Add Line” option to search for and select “real GDP” (use the growth rate series). Finally, start the graph in October 2011, the first data point of GDPNow.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, STLENI

Diverging forecasts

Different stories from hard and soft data

Posted on April 27, 2017

Leading up to the 2017:Q1 GDP release, the two GDP tracking indicators in FRED have told starkly different stories of expected growth. These indicators, also referred to as Nowcast indicators, combine higher-frequency (e.g., monthly) economic data released before the GDP data to estimate growth in the current quarter. As shown in the graph above, in the beginning of April the GDPNow indicator from the Atlanta Fed forecasted a significant slow-down in growth, predicting 0.638 percent annualized growth in the first quarter. In contrast, the St. Louis Fed Economic News Index forecasted annualized GDP growth to be higher, at 2.89 percent in Q1.

What is driving the difference? An analysis into the data underlying the GDP trackers identifies stark differences between “hard” data and “soft” data in the first months of 2017. The Nowcasts rely on both soft data such as consumer and business surveys and hard data such as retail sales and industrial production. The GDPNow indicator uses more hard data, taking an accounting approach to building a forecast; the St. Louis Fed’s News Index is based more on soft data, which is surveyed from news reports. (For more insight on this topic, see this recent Economic Synopses essay.)

The graph below illustrates the contrast between a soft data series and a few hard data series over the beginning of 2017. The blue line is the University of Michigan Consumer Confidence Index, and the red and green lines are industrial production and retail sales, respectively. Each series is indexed to 100 at October 2016 to show the progression over the end of 2016 and beginning of 2017. Since October 2016, consumer confidence has risen dramatically while retail sales and industrial production have risen steadily but slowly. Soft data in 2017 have so far told a much more positive story of economic growth than hard data. One reasons analysts identify as a possible cause for the divergence between survey and hard economic data is consumer and business optimism after the election of Donald Trump that is not yet reflected in the hard data.

How these graphs were created: Top graph: Search for “GDPNow” in the FRED search box and graph the first series that is returned. Click the “Edit Graph” button and select the middle “Add Line” menu. First, search for “St. Louis Fed Nowcast” and add the St. Louis Economic News Index as a new line. Repeat this process for real GDP, selecting the series with units in percent change from preceding period at an annualized rate. Adjust the date range to 2015:Q3 to 2017:Q1. Bottom graph: Search for “Consumer Sentiment” in the FRED search box and graph the first series that is returned. Repeat the process above to add industrial production and retail and food services as additional lines on the graph. Once all three lines are added, select “Edit Line 1” on the “Edit Graph” menu and change the units to “Index.” Set the date to October 2016 and click “Copy to all.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, INDPRO, RSAFS, STLENI, UMCSENT