Board of Governors of the Federal Reserve System

October 14, 2020

Macroeconomic news and stock prices over the FOMC cycle

Jack McCoy, Michele Modugno, Dino Palazzo, and Steve Sharpe

1 Introduction

We develop novel macroeconomic surprise indexes to identify the impact of macroeconomic releases on aggregate stock market returns over the FOMC cycle. We find that aggregate stock prices are positively correlated with our real economic activity news index and negatively correlated with our price news index. We show that our aggregate indexes are able to explain about 20% of the variation in FOMC-period stock market movements, a good deal more than the stock market returns explained by the Citigroup US Economic Surprise Index (CESI), a commonly used index of macroeconomic surprises.

2 Methodology

Our macroeconomic surprise indexes are a weighted average of macroeconomic surprises, where surprise is defined as the difference between the realized value of the macroeconomic release and the median (a.k.a. consensus) of the forecasts made by a panel of market participants and collected by Bloomberg Economic Calendar (ECO). Those forecasts can be submitted one to two weeks before each release and can be updated at any time until the official release, making them a very plausible representation of market participants beliefs in real-time. In order to express all of data surprises in comparable units, each data surprise series is normalized by its historical standard deviation.

Our index is constructed using up to 94 surprise series on macroeconomic variables separated into two groups: 69 economic activity variables (production, consumption, sales, labor, activity surveys, etc.), and 25 price variables. Over time, the set of available consensus forecasts on Bloomberg expanded, as exhibited in Figure 1. Table A.1 and Table A.2 report the components of the activity and price series, the date the consensus forecast was first available in Bloomberg, the number of observations in our sample, the data series frequency, and its most recent relevance index. The relevance index is the percentage of Bloomberg users that set up an automatic alert to be notified when the figure for a given macroeconomic variable has been released. This index provides a gauge of the relative importance of a release to market participants, which we use to weight the news components within an FOMC period before aggregating them.

Figure 1. Evolution of sources over time

Our activity news index $$ani$$ at daily frequency is thus calculated as:

$$$$ {ani}_t = \sum_{i=1}^{N_{a,t}} s_{i,t} w_{i,t} = \sum_{i=1}^{N_{a,t}} \frac{ (A_{i,t} - M_{i,t}) }{ {std}(A_{i,t} - M_{i,t}) } \frac{ W_{i,t} }{ \sum_{i=1}^{N_{a,t}} W_{i,t} } $$$$

Where $$N_{a,t}$$ is the number of activity variables available at time $$t$$, $$s_{i,t}$$ is the surprise in day $$t$$ for activity variable $$i$$, and $$w_{i,t}$$ is the relative importance for activity variable $$i$$. The relative importance of variable $$i$$ is equal to its relevance at time $$t$$ ($$W_{i,t}$$) divided by the sum of the relevance values of all the activity data releases at time $$t$$. The price news index $$pni$$ is calculated in a similar fashion. Using the relevance index to weight the importance of each series allows us to have an expanding set of series underlying the indexes, overcoming the need of working with a balanced panel of data as in Altavilla et al. (2017), whose weights are obtained within a 2-step regression framework.¹

Figure 2 reports an 8-FOMC [8-meeting] moving average of the two news indexes, alongside the moving-average of CESI, again, one of the more widely-used time-series indicators of economic news.² Our activity and price news indexes each identify periods of persistent weaker-than-expected and stronger-than-expected news. In particular, activity in the U.S. was persistently weaker than expected around the beginning of the financial crisis and during the 2014-2015 economic slowdown. Periods of persistent stronger-than-expected activity are evident during the recovery from the financial crisis and during the reacceleration of activity over 2016-2018. Turning to prices, we have periods with persistently higher-than-expected prices during the commodity price boom leading up to the financial crisis, as well as during 2011. Each of those episodes is then followed by a period of persistently lower-than-expected price inflation.

Figure 2. News indexes over time

3 Results

We use the economic activity news index $$ani$$ and the price news index $$pni$$ to explore the influence of macroeconomic news on the aggregate stock market, measured by the average daily S&P500 return over the inter-FOMC meeting period. The latter quantity is the return from the close of the second day of previous FOMC meeting and announcement to the close of first day of the current FOMC meeting, divided by the number of trading days. We aggregate the news indexes $$ani$$ and $$pni$$ as the sum of daily surprises from the day after the previous FOMC through the first day of the current FOMC, divided by the number of days where index data is available. We focus on this time window in order to abstract from the impact of regular monetary policy announcements on stock prices, and focus only on the surprise components of the macro releases.³ To understand the impact of macroeconomic news on stock returns, we fit the following linear model:

$$$$ {ret}_{m_1} = a + b^\prime x_{m_1} + c\ {ret}_{(m-1)_1} + u_{m_1} $$$$

Where $$x_{m_1}$$ may contain either or both of our news indexes. Note that we also control for the market return of the previous inter-FOMC meeting period. Figure 3 reports the scatter plot of the market return against the different measures of macroeconomic surprises used in the empirical analysis.

Figure 3. Inter-FOMC S&P 500 returns vs. news indexes

Table 3 reports the results for a sample of 137 inter-FOMC meeting periods, spanning March 2003 – April 2020. The specification in Column 1, which includes the activity news index, documents that a one standard deviation (unexpected) increase leads to a significant increase in the average daily S&P 500 return of 0.06%. Given the average length of an inter-FOMC meeting period of 33 trading days, this would imply a total S&P 500 increase over the FOMC cycle of about 2%. Moreover, the activity index alone explains about 17% of the stock market variability. In Column 2, we only include the price news index and find a statistically significant relation with aggregate stock market movements that survives when we also control for the activity news index (Column 3). In the latter case, a one standard deviation increase in the price news index leads to a significant decrease in the average daily S&P 500 return of 0.03%, which translates into a cumulative decrease of about 1% over the inter-FOMC meeting period. All told, these results indicate that not only is news about real activity much more important than news about prices in explaining aggregate stock price movements, but also that price news has the opposite sign of activity news: the adjusted R-squared goes from 17% when we only consider the real activity news index, to 20% when we also consider the price news index.

Finally, Table 3 also reports the performances of the CESI, the commonly used index for tracking the trajectory of macroeconomic news surprises and their presumed influence on financial markets. Column 4 shows that, by itself, the FOMC-period change in the CESI has a positive effect on aggregate stock returns, but with only about a third of the explanatory power of our news indexes. Moreover, column 5 shows that CESI has no marginal explanatory power in a regression that also includes our news indexes.

Table 3: Regressions of S&P 500 returns on news indexes

4 Conclusion

This note introduces a novel set macroeconomic surprise indexes based on forecasts made by a panel of market participants and collected by Bloombergs Economic Calendar (ECO). These indexes have substantial explanatory power for aggregate stock market movements over the FOMC cycle.

Table A.1: Activity News Index Components

Table A.2: Price News Index Components