RDP 2021-11: Smells Like Animal Spirits: The Effect of Corporate Sentiment on Investment 4. The Effect of Corporate Sentiment Shocks

4.1 The distribution of sentiment and investment

Before turning to the statistical analysis, it is helpful to look at the data underpinning the regression modelling. The key dependent variable is the net investment rate (or change in the net capital stock). The company-level data indicates a wide dispersion in investment outcomes across companies each year as shown by the difference between the 25th and 75th percentiles of the distribution (top panel of Figure 5). Across the company distribution there is an apparent shift down in the rate of investment around the time of the GFC, with a recovery from around 2015.

For the corporate sentiment indicator, there is also a wide distribution of outcomes at any point in time (middle panel of Figure 5). The sentiment indicator shows a clear cyclical pattern, with sentiment declining during the GFC. There is little evidence that corporate sentiment fell during 2019/20, perhaps because the COVID-19 pandemic only affected economic activity in the last quarter of that year. The Q ratio also displays clear cyclical fluctuations (bottom panel of Figure 5).

Figure 5: Distribution of Corporate Indicators
Company level
Figure 5: Distribution of Corporate Indicators

Notes: Median shown by the line while the shaded area shows the 25th to 75th percentiles
(a) Measured as annual change in net capital stock
(b) Measured as the net balance of positive and negative words per 100,000 words
(c) Measured as (market value of equity plus book value of liabilities less inventories) divided by book value of assets

Sources: Author's calculations; Connect 4; Morningstar; Refinitiv Eikon

We can also look at how corporate investment is distributed across measures of sentiment, uncertainty and fundamentals. To see this, binned scatterplots are constructed and the average investment rate is calculated for deciles of the distribution of the corporate sentiment indicator, the Q ratio and the corporate uncertainty indicator (Figure 6). Companies that report higher levels of sentiment also invest more on average (top panel). Companies with particularly low sentiment levels scale back investment significantly, as shown by the bottom decile. There is a similar positive correlation in the cross-section between the corporate investment rate and the Tobin's Q ratio (middle panel). Further, there is a strong negative correlation in the cross-section between the investment rate and uncertainty (bottom panel). Taken at face value, this is consistent with investment being a function of sentiment, fundamentals and uncertainty.

Figure 6: Corporate Investment Rate
By decile of relevant indicator
Figure 6: Corporate Investment Rate

Sources: Author's calculations; Connect 4; Morningstar; Refinitiv Eikon

4.2 The effect of sentiment on investment

To explore the determinants of investment a standard Tobin's Q model is augmented with the indicators of corporate sentiment and uncertainty:

Δ K it K it1 =β S it +γ Q it +π U it +δCONTROL S it + θ i + λ t + ε it

The dependent variable is the change in the net capital stock ( Δ K it K it1 ) , or the net investment rate, for company i in year t. The key explanatory variable is corporate sentiment (Sit), measured as the ratio of net positive words to total words in each company's annual report, alongside the Tobin's Q ratio (Qit). If sentiment matters to business investment, we would expect a positive correlation between corporate sentiment and the rate of investment. The model also includes controls for other factors that may be associated with investment, such as profitability, growth and size. Also considered is a regression in which firm-level uncertainty (Uit) is controlled for.

To identify the causal effect of sentiment on investment, there are at least two potential identification challenges. First, the level of manager sentiment may be correlated with adjustment cost shocks that are observed by the firm. For example, a factory may have to unexpectedly shut down for a period of time to replace the capital. This might have an adverse effect on the sentiment of the management team. In this case, we would have a positive correlation between sentiment and the error term and the estimated coefficient on sentiment will be biased upwards:

β ^ =β+ cov( S it , ε it ) var( S it ) >β

To partly address this, I consider alternative model specifications that test the sensitivity of investment to sentiment based on prior company reports (which should contain no knowledge of any adjustment cost shock).

The OLS estimates point to a significant positive correlation between sentiment and investment (Table 2). A one standard deviation (sd) increase in the share of net positive words (which is about 3.5 in 10,000 words) is associated with an increase in the rate of investment of about 6 percentage points (so the investment rate rises from about 10 per cent to 16 per cent at the sample mean).[5] The correlation between sentiment and investment is unaffected by either the inclusion of Tobin's Q (comparing columns 1 and 2) or the inclusion of other proxies for fundamentals, such as profits and sales growth (comparing columns 1 and 3). Moreover, the result is driven mostly by the time series (within-company) variation in sentiment and investment – companies typically invest more when they use more net positive words, all other things being equal. This is shown by the stronger effect of sentiment when company fixed effects are included (comparing columns 2 and 3).

The Q ratio is also positively associated with investment. A 1 per cent increase in investment opportunities, as measured by Q, is associated with the rate of investment rising by 2 percentage points on average (a one standard deviation shock is associated with the investment rate being about 5 percentage points higher, all else equal). As expected, investment is also positively correlated with other indicators of fundamentals, such as sales growth and profitability.

Furthermore, adding a measure of uncertainty to the set of control variables (column 4) we find that uncertainty is negatively associated with investment, and the effect appears to be of a similar magnitude to that of sentiment. But controlling for firm-level uncertainty has a limited effect on the sensitivity of investment to sentiment. This suggests that they both have roles to play in explaining corporate investment behaviour.

Table 2: The Effect of Corporate Sentiment on Investment
Sample period: 2003 to 2020
  OLS with no controls OLS with controls Fixed effects with controls Fixed effects with controls and uncertainty indicator
Sentiment 0.02***
Tobin's Q 0.02***
Uncertainty −0.04**
Return on assets 0.11***
Sales growth 0.16***
Lagged sales level 0.16***
Lagged capital stock level −0.25***
Company fixed effects N N Y Y
Year fixed effects N N Y Y
R squared 0.5% 1.6% 32.9% 33.1%
Companies 2,050 1,210 999 999
Observations 11,733 7,440 7,215 7,215

Notes: *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels, respectively, with t-statistics in parentheses; standard errors are two-way clustered by firm and year; coefficient estimates for constant, firm dummies and year dummies are omitted

Sources: Author's calculations; Connect 4; Morningstar; Refinitiv Eikon

4.3 The dynamics of corporate sentiment, uncertainty and investment

The dynamics of investment in response to shocks to sentiment and other variables in the model may tell us more about the mechanism through which changes in sentiment are associated with changes in investment. For instance, if sentiment is a proxy for news about future company performance then we may expect the effect of a sentiment shock on investment to be relatively persistent (Barsky and Sims 2012). If sentiment is instead capturing noise or animal spirits, then the effect may be expected to be temporary, or at least more temporary than a similar shock to a fundamental proxy such as Tobin's Q.

To examine the dynamics of the relationship between sentiment and investment I estimate a series of impulse response functions using the local projections (LP) method of Jordà (2005). Specifically, for each forecast horizon (h) I run a regression of the annual investment rate on standardised ‘sentiment shocks’ as well as a series of lagged controls:

( I K ) i,t+h ( I K ) i,t = α i,h + β h s i,t + γ h Q i,t + π h U i,t + μ h CONTROL S i,t1 + ε i,t+h

Here, a sentiment shock is measured by a one standard deviation change in the sentiment indicator conditional on a set of controls that includes Tobin's Q, uncertainty, sales growth, the lagged level of the net capital stock and the lagged level of sales. Similarly, a Q shock is measured by a one standard deviation change in the Tobin's Q ratio conditional on sentiment and the same set of controls. Further, an uncertainty shock is measured by a one standard deviation change in the uncertainty indicator conditional on sentiment, Q ratio and the controls. The sentiment, Q ratio and uncertainty measures are standardised to z-scores by subtracting the firm-level mean and dividing by the firm-level standard deviation.

The horizon-zero version of the LP model is almost identical to the baseline regression presented in Section 4.2 (with the full set of control variables). The only difference is that the local projections include the one-year lag of each of the standardised indicators of sentiment, uncertainty and Tobin's Q. For example, the sentiment indicator is serially correlated, so including lags is important to be able to interpret the coefficients as the response to sentiment ‘shocks’.

The impulse response of the investment rate to the sentiment shock is traced out by the estimates of the β s. The impulse response of the investment rate to the Q and uncertainty shocks are similarly traced out by the estimates of the γ s and the π s.

The estimated impulse response suggests that a standardised sentiment shock has a persistent positive correlation with investment, with the rate of investment being 10 percentage points higher after about three years before gradually declining (Figure 7). This would imply the average investment rate rises from 10 per cent to 20 per cent at the peak. The size of the investment response to a one standard deviation Tobin's Q shock is of a similar magnitude but more persistent than a sentiment shock. The (negative) effect on investment of a standardised uncertainty shock is smaller in size to the sentiment shock and more temporary. Overall, all three shocks appear to matter to investment at the company level.

The fact that the sentiment shock has an effect that lasts beyond a couple of years suggests that the sentiment indicator is at least partly capturing news about a company's future productivity. Alternatively, it may indicate that an animal spirits shock has a self-fulfilling nature in which fundamentals adjust to the initial demand shock to render the change in sentiment rational ex post. Moreover, the effect of a shock to sentiment appears to be less persistent than that of a similar shock to Tobin's Q. Overall, this empirical exercise provides mixed evidence on whether the sentiment indicator is capturing news or noise.

Figure 7: Response of Corporate Investment Rate to Various Shocks
Shock: one standard deviation increase in each indicator
Figure 7: Response of Corporate Investment Rate to Various Shocks

Notes: Shaded areas show 95 per cent confidence interval; standard errors are two-way clustered by company and year

Sources: Author's calculations; Connect 4; Morningstar; Refinitiv Eikon


This effect of sentiment on investment is estimated as β ^ × sd in sentiment × 100 = 0.018 × 3.49 × 100 = 6.3%. [5]