RDP 2020-08: Start Spreading the News: News Sentiment and Economic Activity in Australia 6. News Sentiment and Monetary Policy

Next, we study the link between news sentiment and changes in monetary policy. This research extension is motivated by a couple of factors. First, it is important for a central bank to understand how its policy actions are reported in the news media and how this might affect beliefs amongst consumers about the outlook for the economy. For example, if the RBA wants to cut interest rates and lift confidence and economic activity then it may be important to understand how the policy action is typically communicated through the media. For this, we construct a measure of news sentiment that focuses specifically on news articles about monetary policy. Second, as suggested previously, news sentiment may be a high-frequency indicator of broader sentiment in the economy. If changes in monetary policy only affect sentiment within a few days of an interest rate change, then this effect may be captured by the NSI, but not the lower-frequency survey-based measures. So it allows us to study the causal effect of interest rates on sentiment more generally.

6.1 Monetary Policy News Sentiment Index

We first develop a monetary policy NSI for articles that mention terms specifically related to monetary policy, such as ‘monetary policy’, ‘cash rate’, and ‘RBA’. These articles account for about 15 per cent of total articles about the economy at any given time. The resulting index is quite volatile from month to month (Figure 10). Despite this, the index is significantly correlated with the stance of monetary policy as measured by ‘monetary policy shocks’ (for example, Romer and Romer (2004) for the United States, Bishop and Tulip (2017) and Beckers (2020) for Australia). These ‘shocks’ capture changes in the cash rate that are not systematically related to changes in the RBA's forecasts for economic activity and inflation.[7]

Figure 10: Monetary Policy News Sentiment Index versus Monetary Policy Shock
Three-month moving average
Figure 10: Monetary Policy News Sentiment Index versus Monetary Policy Shock

Note: (a) Measured by Beckers (2020)

Sources: Authors' calculations; Beckers (2020); Dow Jones Factiva

This correlation suggests that in setting monetary policy the RBA takes into account additional qualitative information about the future direction of the economy (over and above what is captured in the RBA's forecasts or financial markets), that is also evident in news articles; some of this information might be taken into account in the RBA's assessment of risks around the central forecasts, but not in the central forecasts themselves. Research for the United States similarly suggests that the tone of the narrative around the Federal Reserve's forecasts predicts their forecast errors for GDP growth and unemployment (and explains monetary policy decisions over and above the forecasts) (Sharpe et al 2018).

6.2 The Causal Effect of Monetary Policy on News Sentiment

We next look to establish direct evidence that unexpected changes in monetary policy cause changes in sentiment as expressed in the news media. Prior to the COVID-19 pandemic, there was extensive media coverage suggesting that lowering interest rates caused weaker sentiment amongst business and consumers (as discussed by Kirchner (2020a)). This positive co-movement of interest rates and sentiment contradicts the predictions of standard macroeconomic models. So it is important to weigh the evidence for these two competing predictions.

There is systematic variation in the language used by the news media in articles about monetary policy on the day just after a monetary policy announcement (Figure 11). For instance, the news media commonly uses the term ‘strong’ when the RBA raises the cash rate and the term ‘recession’ when the RBA lowers the cash rate. The terms ‘strong’, ‘unemployment’ and ‘volatility’ appear with similar frequency when the RBA leaves the cash rate unchanged. At face value, this suggests that the news media is discussing the balance of underlying risks to the economic outlook when reporting on monetary policy decisions. However, the term ‘crisis’ appears in the news when either the cash rate is increasing or decreasing, so the differences in language are not totally clear-cut for changes in monetary policy.[8]

Figure 11: Sentiment Word Clouds
Monetary policy news in the day after the monetary policy announcement
Figure 11: Sentiment Word Clouds

Sources: Authors' calculations; Dow Jones Factiva

We test the direction of the relationship between interest rates and sentiment using an event study approach, estimating the change in news sentiment around the announcement of monetary policy decisions. Here, decisions include both announced changes to monetary policy and announcements of decisions to not change policy, so long as the market knew that a policy announcement would take place.

We exploit the fact we have a daily measure of news sentiment and examine how this sentiment measure changes in the days around monetary policy announcements. The identification strategy rests on the assumption that we can minimise the potential effect of any omitted variables by focusing on a narrow time window around monetary policy decisions. For instance, it is plausible that any change in news sentiment that occurs on the day after the monetary policy announcement will be due to changes in monetary policy and not to its actual effect on the economy. A similar identification strategy is developed by Lewis, Makridis and Mertens (2019) to identify the effect of monetary policy on consumer sentiment in the United States.

We estimate the response of daily changes in news sentiment to monetary policy shocks in a narrow window of time around RBA board meetings through local projections. Specifically, we estimate the following equation:

(2) NS I t+h NS I t1 = c h + β h s t + t+h

where NSIt+h denotes the news sentiment index on day t, NSIt−1 is the value of the index on the day prior to the announcement and st is the monetary policy shock.[9] We estimate the equation by ordinary least squares for each daily horizon h in a range of 3 days before and 10 days after the monetary policy shock. The resulting series of coefficient estimates gives us the conditional average NSI relative to the average value for the day before the monetary policy announcement.

Given the daily volatility in the NSI we also consider smooth local projections:

(3) NS I t+h NS I t1 =c+I( h=0 ) β 0 s t +I( h>0 )( γ 1 + γ 2 h+ γ 3 h 2 ) s t + t+h

where all variables are as before, except that an indicator function is included for whether the projection horizon is equal to zero (I(h = 0)) or greater than zero (I(h > 0)).[10]

In gauging the effect on sentiment, we consider several alternative monetary policy shock estimates. First, we look at Romer and Romer-style estimates for Australia that are sourced (and updated) from Bishop and Tulip (2017) and Beckers (2020). Second, we consider monetary policy shocks that are identified in high-frequency event studies by isolating the surprise component of a change in monetary policy using changes in market interest rates (e.g. Kearns and Manners 2006). The interest rate surprise is calculated as the change in the 1-month overnight indexed swap rate (OIS) from the close of the day prior to the close of the day of the monetary surprise.

The local projection estimates indicate that an unexpected tightening of monetary policy of 100 basis points is associated with news sentiment declining by about one-half of a standard deviation in the days just after the policy announcement, irrespective of the policy shock measure used (Figure 12). The effect is not particularly strong given that a typical monetary policy shock will be much smaller at around 10 to 20 basis points. The relatively small economic effects of monetary policy on economic activity indicators, such as sentiment, is common when using high-frequency shocks (Nakamura and Steinsson 2018b). The effect is consistently negative for at least two weeks after the announcement for both the Bishop and Tulip (2017) and the Beckers (2020) shock (left-hand and middle panels of Figure 12). In contrast, in the case of the 1-month OIS series, the effect of the shock on sentiment is not significantly different from zero (right-hand panel of Figure 12). At the very least, we find little evidence that falling interest rates cause lower sentiment as measured in the news media.[11]

Figure 12: Effect of Monetary Policy Shocks on News Sentiment
Figure 12: Effect of Monetary Policy Shocks on News Sentiment

Notes: Effect of a 100 basis point shock to monetary policy; solid lines show smooth local projections, dashed lines show non-smoothed local projections; shaded areas are 90 per cent confidence intervals; standard errors are robust

Sources: Authors' calculations; Beckers (2020); Bishop and Tulip (2017); Dow Jones Factiva


The Beckers (2020) shock captures non-systematic changes in the cash rate accounting for the RBA's forecasts and credit spreads. [7]

After a cash rate increase, the term ‘crisis’ often appears as a reference to the post-crisis economic recovery. Following a cash rate decrease, the term ‘crisis’ often appears in articles discussing the crises related to the Australian bushfires and COVID-19 pandemic in 2020. [8]

Many news articles on the day of the monetary policy announcement are actually written the night before. Unfortunately, we are not able to separately identify these articles. [9]

Unlike the fully non-parametric projections, the smooth local projections estimate the responses at all horizons simultaneously. [10]

The relationship between unexpected changes in monetary policy and news sentiment is not affected by the presence of widely-reported economic releases in the week of the monetary policy decision. We find very similar effects in the weeks in which the national accounts, inflation and labour force statistics are released.[11]