In times of crisis, real-time indicators of economic activity are a critical input to timely and well-targeted policy responses. The COVID-19 pandemic is the most recent example of a crisis where events with little historical precedent played out rapidly and unpredictably.
To address this need for real-time indicators researchers from the Reserve Bank of Australia develop a new indicator of news sentiment based on a combination of text analysis, machine learning and newspaper articles. The news sentiment index complements other timely economic indicators and has the advantage of potentially being updated on a daily basis.
It captures key macroeconomic events, such as economic downturns, and typically moves ahead of survey-based measures of sentiment. Changes in sentiment expressed in monetary policy-related news can also partly explain unexpected changes in monetary policy. This suggests that news captures important, but unobserved, information about the risks to the RBA’s forecasts that the RBA responds to when setting interest rates.
An event study in the days around monetary policy decisions suggests that an unexpected tightening in monetary policy is associated with weaker news sentiment, though the effects on sentiment are temporary and not particularly strong.
Conclusions and future research directions
Researchers construct a news sentiment index and find that it is useful both to understand the current state of the economy and to help explain economic conditions in the near term. Some related indicators, such as the news uncertainty index, are also useful real-time indicators of the economy.
They also present novel estimates of monetary policy-related news sentiment, which can predict changes in the cash rate even after accounting for the RBA’s forecasts for the economy. This suggests that the news contains useful information about the risks to the economic outlook to which the RBA is responding when setting interest rates.
An event study conducted in the days around monetary policy announcements finds evidence that increases (decreases) in interest rates are associated with weaker (stronger) news sentiment. These results are consistent with monetary policy having its intended effects and reseachers find no evidence of any counterproductive effects of monetary policy on news sentiment.
This paper focuses on the ability of the news sentiment indicators to explain high-frequency indicators of economic activity. Researchers do not find any evidence that fluctuations in news sentiment explain economic activity at a longer frequency, such as quarterly measures of GDP growth and consumer price inflation.
It may be worth looking at this more closely in future given the relative importance that policymakers place on these specific measures of activity. Relatedly, researchers have applied a very simple text analysis method in constructing preferred news sentiment indicators. It may be worth looking at more sophisticated machine learning measures in future research.
Finally, researchers are unable to clearly disentangle the effects of shocks to news (fundamentals) from those of sentiment or “animal spirits” (non-fundamentals) on the economy. Future research may be able to separate these two shocks through different types of news articles. For example, the language used in editorials and opinion articles is plausibly more associated with changes in sentiment and the language used in standard news reports may be more associated with changes in fundamentals.