The word cloud on the plot on left side shows fifty most positive words used in the Greenbook during the period Jan 1994 and Dec 1998. The word cloud on the right side shows fifty most positive words during the period Jan 2005 and Dec 2009. The size of individual word in a word cloud is proportional to its contribution in the calculation of Tonality during the plotted time-window.
Figure 1 shows two side-by-side word clouds for the 50 most prominent positive words in Greenbooks during two periods, 1994-1998 and 2005-2009. The word size is proportional to its contribution to Tonality, that is, its contribution to the sum of tf-idf weights during the five-year window. The word cloud for positive words during 1994-1998 is slightly bigger than that for 2005-2009. Word choices between these two periods that are roughly ten years apart are similar, suggesting there is not a lot of language drift, whereby many words simply fall out of favor and are replaced by new ones. The most important positive word in both periods is “upward”, followed closely by “positive.” However, the word “favorable” is a more prominent word during 1994-1998, as is the word “moderation.”
The word cloud on the plot on left side shows fifty most negative words used in the Greenbook during the period Jan 1994 and Dec 1998. The word cloud on the right side shows fifty most negative words during the period Jan 2005 and Dec 2009. The size of individual word in a word cloud is proportional to its contribution in the calculation of Tonality during the plotted time-window.
Figure 2 shows two side-by-side word clouds for the 50 most prominent negative words in Greenbooks during the same two periods. The word cloud for negative words during 1994-1998 is smaller than that in 2005-2009. The most prominent negative word in both samples is “negative”, followed by “sluggish.” However, negative words are simply more prominent in the latter sample as indicated by the larger word sizes in the right-hand-side word cloud. For example, the word “adverse” is somewhat more prominent in 2005-2009 period. Similarly the word “recession” is much smaller in the 1994-1998 period than it is in the 2005-2009 period, perhaps not surprising since the later period includes the “Great Recession.”
Shaded regions represent NBER-dated recessions. Tonality is standardized to have a zero mean and a standard deviation equal to one. Prior to 1980, Greenbooks were produced almost every month, thereafter the frequency was reduced to eight times a year.
Figure 3 shows the Tonality index plotted over the full sample period, with positive levels indicated in green and negative levels indicated in red. As one might expect, Tonality appears to be procyclical, with the large majority of observations during recessions in negative territory, and a mixture of positive and negative observations during expansionary periods. Among the most deeply negative readings of Tonality are observations in the year leading up to and during the Great recession, and also during the 1974-75 recession. The most noticeable run of highly positive reading was during the mid-1990s. Despite these cyclical tendencies, Tonality also appears to be quite volatile, exhibiting a lot of high-frequency movement that is often quickly reversed. To some extent, these fluctuations might reflect noise in our proxy for sentiment.
Shaded regions represent NBER-dated recessions. Tonality is standardized to have a zero mean and a standard deviation equal to one. Prior to 1980, Greenbooks were produced almost every month, thereafter the frequency was reduced to eight times a year.
In order to examine whether the high frequency movements reflect more noise than signal, we produce a measure of what we call “Trend” Tonality, by constructing an exponentially weighted moving-average of Tonality. For that calculation, the weighting parameter—the rate of decay of weight placed on lagged observations—is chosen to maximize the fit (minimize the mean squared distance) between the smoothed series and our raw Tonality measure. This yields the same measure of trend as that from estimating a Kalman Filter under the assumption that “Trend” Tonality followed a random walk. Figure 4 shows the resulting times series plot for “Trend Tonality”, shown by the black line, along with (total) Tonality. Not surprisingly, the cyclical pattern in this smoothed measure of sentiment stands out more clearly.
The plot shows the decrease in Bayesian information criteria (BIC) and residual sum of square as we increase the number of breakpoints in the relationship between Tonality and current Greenbook point forecasts. The first breakpoint corresponds to September 1990 and the second breakpoint corresponds to December 2000). There is a U shaped relationship between BIC (shown on the left Y axis) and number of breakpoints, the optimum number of breakpoints is two. However, the second breakpoint does not lead to as much improvement in BIC (lower is better) as the first breakpoint.