The Power of Narratives in Economic Forecasts

Abstract

We apply textual analysis tools to the narratives that accompany Federal Reserve Board economic forecasts to measure the degree of optimism versus pessimism expressed in those narratives. Text sentiment is strongly correlated with the accompanying economic point forecasts, positively for GDP forecasts and negatively for unemployment and inflation forecasts. Moreover, our sentiment measure predicts errors in FRB and private forecasts for GDP growth and unemployment up to four quarters out. Furthermore, stronger sentiment predicts tighter than expected monetary policy and higher future stock returns. Quantile regressions indicate that most of sentiment's forecasting power arises from signaling downside risks to the economy and stock prices.

German forecasters’ narratives: How informative are German business cycle forecast reports?

“…German forecasters' narratives can enhance the accuracy of German business cycle forecasts. Overall, the results are in line with the findings of Jones et al (2020), Sharpe et al (2020) and Clements and Reade (2020). The four-quarter forecast horizon is comparable with the results of Sharpe et al (2020) for the Fed's Greenbook, whereas findings for the UK show shorter forecast horizons (Jones et al 2020;Clements and Reade 2020).…”

“…Following Clements and Reade (2020) and Sharpe et al (2020), the dictionary-based method is applied to develop sentiment indices. In fact, three well-established linguistic dictionaries are used to generate five different indices.…”

“…The dictionary is binary coded for polarity in positive and negative terms. -Second, there is a forecast-specific German dictionary-based on Sharpe et al (2020). According to Di Fatta et al (2015), words have different connotations and meanings in different contexts, and sentiment indices have to be adapted to the content to which they have been applied.…”

“…According to Di Fatta et al (2015), words have different connotations and meanings in different contexts, and sentiment indices have to be adapted to the content to which they have been applied. To this end, Sharpe et al (2020) developed a forecast-specific word list which excludes words that have special meanings in an economic forecasting context. The word list contains 205 positive and 103 negative words (see Tables 8, 9) and is binary coded like the previous one.…”

“…A second strand of the literature uses computational text analysis methods to generate text-based sentiment indices. Clements and Reade (2020) and Sharpe et al (2020) are two seminal related studies. The latter study applies computational text analysis to quantify the 'tonality' (the degree of optimism versus pessimism) of the Federal Reserve Board's Greenbooks and examines whether this measure has predictive power for the economic development over the period 1972-2009. The investigation shows some predictive power of the Greenbook tonality on Greenbook numerical GDP growth and unemployment forecasts, as well as on private GDP forecasts.…”