RDP 2021-11: Smells Like Animal Spirits: The Effect of Corporate Sentiment on Investment 1. Corporate Sentiment, Animal Spirits and Investment

‘We should not conclude from this that everything depends on waves of irrational psychology … We are merely reminding ourselves that human decisions affecting the future, whether personal or political or economic, cannot depend on strict mathematical expectation, since the basis for making such calculations does not exist; and that it is our innate urge to activity which makes the wheels go round …’ (Keynes 1936, pp 162–163)

Economists have long been interested in the role of ‘animal spirits’ (or sentiment) in the business cycle. Since Keynes (1936) originally coined the term ‘animal spirits’ there have been many books written on the subject and an extensive literature in economics, finance and psychology has grown to study how changes in sentiment amongst individual decision-makers affect their behaviour and, in turn, the overall economy.

But identifying a role for sentiment in the business cycle is challenging partly because it is hard to define. Economists have long recognised the importance of expectations for aggregate economic behaviour, but they disagree on why expectations matter and how expectations are formed. Such disagreements make it difficult to define sentiment. For example, Keynes (1936) argued that changes in expectations were not caused by rational probabilistic calculations but by animal spirits, and Pigou (1927) similarly believed that business cycles were largely driven by expectation errors of optimism and pessimism.^[1] Expectations are important in modern general equilibrium models, but they are typically modelled according to the rational expectations hypothesis. Some rational expectations models allow for animal spirits by assuming equilibrium indeterminancy – expectation errors are a function of structural disturbances and exogenous ‘sunspot’ variables (Benhabib and Farmer 1999). Similarly, in this paper, I define changes in sentiment as shifts in expectations that are exogenous to changes in economic fundamentals (Milani 2017).

Identifying a role for sentiment is also challenging because it is not observed. Traditionally, economists have relied on surveys of consumers and businesses to measure sentiment. These surveys ask respondents about their beliefs for current and future economic conditions. But advances in machine learning and big data have allowed sentiment to be measured not just through surveys, but through alternative sources of information, such as text, audio and visual data (Algaba et al 2020). These techniques present new opportunities to measure sentiment and therefore answer long-standing questions about the role of sentiment in the business cycle.

In practice, it is also difficult to disentangle the effects of sentiment and uncertainty. Assuming that business managers have different expectations about future growth outcomes, sentiment can be thought of as the average outcome (the first moment of the distribution) while uncertainty can be thought of as the variance of possible outcomes (the second moment).^[2] A negative sentiment shock would cause firms to expect average growth to fall. A negative uncertainty shock would lead firms to believe that a broader range of growth outcomes is possible. For example, recessions are typically associated with both a decline in average sales growth and a higher variance (Figure 1).

Motivated by these insights, I construct a new indicator of company-level sentiment using text analysis and examine its link to the business cycle by identifying how changes in company-level sentiment are associated with changes in investment. The measure of sentiment is very simple and is constructed as the net balance of positive and negative words used in the annual reports of publicly listed companies in Australia. A company that uses more positive words (or fewer negative words) in its report compared to the previous report is higher in sentiment and therefore more likely to invest, all other things being equal.

This paper aims to extend insights from text-based analysis of sentiment in behavioural finance to the domain of macroeconomics. While text analysis is relatively new in macroeconomics, it is much more common in corporate finance (Kearney and Liu 2014). In standard corporate finance models there is no role for sentiment in explaining corporate behaviour. But the behavioural finance literature has consistently found that text-based measures of sentiment for investors (Zhou 2018) and managers (Jiang at al 2019) have strong ability to predict equity returns. I explore whether such measures also have predictive ability for corporate investment – a key variable of interest to macroeconomists.

The focus on business investment is motivated by the observation that much of the variation in the business cycle is due to fluctuations in investment. If there is a role for sentiment in explaining economic activity, it is likely to be found in its link to business investment. The literature has long recognised the importance of business expectations in models of investment under uncertainty (e.g. Hayashi 1982; Abel and Blanchard 1986; Chirinko 1993; Dixit and Pindyck 1994). But open questions remain, such as why expectations matter to investment, and whether there is a role for feelings or opinions (and hence sentiment) in influencing such decision-making. This paper explicitly examines such roles for sentiment and uncertainty.

An added motivation of this paper is to explore whether sentiment and uncertainty have some role to play in explaining the broad-based weakness in business investment observed since the global financial crisis (GFC). It is common to hear financial market participants, business managers and the media argue that this weakness in investment is at least partly due to ‘a lack of confidence’ or ‘heightened uncertainty’. The weakness in investment is apparent in both Australia and the United States. It is also apparent for both aggregate investment (in the national accounts) and for the investment of the average small business (in firm-level data) (Figure 2).^[3] The fact that it is so prevalent points to common causes across countries and industries. Even so, there remains a long list of potential explanations for the weakness in investment (Gutiérrez and Philippon 2017).

This paper fits into a growing literature that examines the role of both news (fundamentals) and sentiment (non-fundamentals) in driving the business cycle. This literature is divided about the role of sentiment in explaining changes in macroeconomic conditions (Nowzohour and Stracca 2020). There is the ‘fundamental’ view that believes that measures of sentiment capture news about the economy (e.g. Roberts and Simon 2001; Barsky and Sims 2012; Blanchard, L'Huillier and Lorenzoni 2013; Beaudry and Portier 2014). Under this view, agents receive an imperfect signal about future economic fundamentals, such as productivity, and the economy is subject to recurrent booms (if the signal is correct) and occasional busts (if the signal is false). There is also the ‘animal spirits’ view that measures of sentiment capture non-fundamental factors (e.g. Akerlof and Shiller 2009; Farmer 2012; Benhabib, Wang and Wen 2015). This line of research argues that psychological waves of optimism and pessimism cause macroeconomic fluctuations, implying that expansions eventually lead to busts as fundamentals are unaffected.

This paper is also related to a vast body of empirical research that studies the effects of uncertainty on the economy (e.g. Bloom 2009). Most of the existing literature focuses on identifying the effect of either sentiment or uncertainty and it is surprisingly rare to see them considered side by side. A key contribution of this paper is to develop an empirical framework in which to measure both sentiment and uncertainty on a consistent basis using text analysis. This allows me to test for differential effects of sentiment and uncertainty on investment, which may be important to understanding the drivers of the business cycle.

I motivate my empirical approach to identifying the effects of sentiment by considering a standard theoretical model of investment that is extended to allow for sentiment shocks. In this set-up, corporate investment is a function of both current and expected future profits, where the relevant expectations are those of corporate managers, which can differ from market (or investor) expectations. Expected profit growth among corporate managers is based on their beliefs about fundamentals and non-fundamental ‘noise’ or sentiment shocks.

I find strong evidence that changes in corporate sentiment are positively associated with investment. An increase in one standard deviation of the sentiment indicator is associated with the investment rate increasing from a sample mean of 10 per cent to 16 per cent, all other things being equal. Moreover, the relationship holds even when controlling for a broad range of proxies for corporate fundamentals, including the Tobin's Q ratio, current and expected profitability and sales growth. This suggests that the relationship between sentiment and investment is at least partly capturing the effect of noise shocks, or animal spirits, among managers.

The claim that the effect of sentiment on investment is due to animal spirits rests on the assumption that the sentiment indicator is a pure measure of animal spirits among company managers. But managers may know more about the company's fundamentals than outside investors. If managers have private knowledge about the company's prospects, the relationship between investment and sentiment may be due to fundamental factors, rather than animal spirits.

To test this, I examine whether investment is sensitive to changes in sentiment, even when investors potentially know more about the future of the company than the managers. In particular, I measure Tobin's Q based on the share price at the end of the relevant financial year and I measure sentiment using financial reports from the prior financial year. In this case, investors should have incorporated any relevant information revealed about the company's future prospects from the relevant corporate reports. But, even with this timing advantage, I find that investment is still more sensitive to lagged sentiment than it is to Tobin's Q.

I also explore heterogeneity in the sensitivity of investment to sentiment across companies. If the sentiment indicator is mainly capturing private knowledge among managers, investment should be more sensitive to sentiment in companies that are more opaque to outside investors. This might include companies that are small or new or that have shares that are rarely traded. However, I find that the sentiment effect is not stronger for small, young or rarely traded companies, which argues against the private knowledge interpretation.

To further explore the mechanism underpinning the sensitivity of investment to sentiment, I also explore the dynamics of investment in response to shocks to sentiment, uncertainty and Tobin's Q through a series of local projections. This identification strategy assumes that sentiment will have a temporary effect on investment if it mainly reflects animal spirits, whereas it should have a more permanent effect if it reflects fundamentals (Barsky and Sims 2012). Here, I find mixed evidence. A sentiment shock has a fairly persistent effect on investment, though the effect is more temporary than that of an equivalent shock to Tobin's Q. I also find that uncertainty shocks have a temporary negative effect on investment of a slightly smaller magnitude to that of a sentiment shock. Overall, I conclude that the sensitivity of investment to sentiment reflects both animal spirits and fundamentals.

The main contributions of this paper are to:

• develop new firm-level indicators of corporate sentiment and uncertainty within the same empirical framework using text analysis;
• demonstrate that firm-level investment is very sensitive to changes in sentiment, even controlling for corporate fundamentals;
• show that both corporate sentiment and uncertainty have independent roles to play in explaining business investment, suggesting that both first and second moment shocks matter;
• show that the effect of sentiment is relatively persistent, albeit more temporary than that of a shock to Tobin's Q, which suggests that the sentiment measure is at least partly capturing animal spirits or noise shocks.

These findings are important from a policy perspective. If sentiment shocks can affect corporate behaviour independently of fundamentals, this suggests that there may be a role for policy to manage business cycle fluctuations by influencing the expectations of corporate managers. So having a variety of policy communication channels that target business decision-makers could matter, including speeches, business liaison programs and surveys dedicated to measuring the beliefs and uncertainties of respondents.

More generally, the ability of simple text-based indicators to consistently predict investment – a macro variable that is inherently difficult to forecast – is consistent with similar findings for equity returns in the corporate finance literature. Based on this, macro policymakers should consider investing more resources in machine learning techniques and extracting information from non-traditional sources of data, such as text, audio and visual media.

Footnotes

The Macquarie Dictionary defines sentiment as ‘a thought influenced by or proceeding from feeling or emotion’. This suggests that sentiment should be defined in terms of the strength of feeling that agents hold in predicting future outcomes. [1]

In the paper I assume that ‘sentiment’ and ‘confidence’ are the same concept, even though it may be possible to distinguish between ‘sentiment’ as referring to beliefs about current economic conditions and ‘confidence’ referring to beliefs about future economic conditions. [2]

The firm-level investment data shown in Figure 2 are based on estimates for the entire population of Australian businesses and are drawn from the ABS BLADE data environment. [3]