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Does business confidence matter for investment?

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Abstract

Business confidence is a well-known leading indicator of future output. Whether it has information about future investment is, however, unclear. We determine how informative business confidence is for investment growth independently of other variables using US business confidence survey data for 1955Q1–2016Q4. Our main findings are: (i) business confidence has predictive ability for investment growth; (ii) remarkably, business confidence has superior forecasting power, relative to conventional predictors, for investment downturns over 1–3-quarter forecast horizons and for the sign of investment growth over a 2-quarter forecast horizon; and (iii) exogenous shifts in business confidence reflect short-lived non-fundamental factors, consistent with the ‘animal spirits’ view of investment. Our findings have implications for improving investment forecasts, developing new business cycle models, and studying the role of social and psychological factors determining investment growth.

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Notes

  1. Historically, the view that behavioural factors may influence investment decisions has been around at least since Keynes (1936) who famously invoked ‘animal spirits’ as an inducement to invest and noted: “But individual initiative will only be adequate when reasonable calculation is supplemented and supported by animal spirits.”(Chap 12, page 163).

  2. Appendix provides details on how the business confidence index is constructed.

  3. Rossi (2013) points out that it is not necessary for the in-sample results to be similar to OOS results.

  4. This definition is similar to that in Taylor and McNabb (2007) for output downturns.

  5. Many previous studies have focused on sign of stock market returns [see Christoffersen and Diebold (2006), Christoffersen et al. (2007) and Nyberg (2011)]. Christoffersen and Diebold (2006) find a link between asset return volatility and asset return sign predictability.

  6. Online Appendix available at https://carleton.ca/economics/wp-content/uploads/cep17-13.pdf provides the details of data construction and sources.

  7. To save space, we have put all the associated Tables and Figures from this section in Appendix.

  8. Chirinko and Schaller (2001) also use neoclassical model where dependent variable is investment rate and the regressors are the level and lag of change in output, the level and lag of change in the cost of capital and liquidity, where liquidity is retained earnings plus depreciation.

  9. Allowing for different lags for different sets of variables in (2) does not affect our empirical findings (the results are available upon request).

  10. The general setup for obtaining OOS data is similar to Estrella and Mishkin (1998).

  11. The results for the rolling-window estimation are available upon request. Notably, the recursive estimation scheme performs better for structures investment, and with statistical significance, relative to the rolling-window estimation.

  12. Goyal and Welch (2003, 2008) use this approach to show the CDSFE of historical average versus predictive variable’s regression.

  13. This definition is similar to that used for output downturns in Taylor and McNabb (2007).

  14. Previously, the probit model has been used by Estrella and Mishkin (1998), Kauppi and Saikkonen (2008), Nyberg (2010), Christiansen et al. (2014), Chen et al. (2016), among others, to forecast recessions. The main difference relative to these papers and other previous research is that our focus is on investment downturns, not output recessions.

  15. We consider dynamic probit model in the robustness section.

  16. The results based on the alternative approach of rolling-window estimation are available upon request.

  17. In probit models, the ps.\(R^2\) of Estrella (1998) is used by Estrella and Mishkin (1998), Kauppi and Saikkonen (2008), Nyberg (2010), Christiansen et al. (2014) and Chen et al. (2016), among others, in order to evaluate model fit.

  18. Berge and Jordà (2011) and Liu and Moench (2016) provide a detailed discussion of the ROC curves, where they use them to assess the classification abilities of various leading indicators into recessions and expansions.

  19. Kauppi and Saikkonen (2008), Nyberg (2010), Pönkä (2017a) and among others show dynamic probit model increases the forecasting performances.

  20. Nyberg (2010) uses nine months lags of the dependent variable (recession indicator) due to the NBER announcement delay.

  21. http://www.ism.ws/ISMReport/MfgROB.cfm?navItemNumber=12942.

  22. https://finance.yahoo.com/q/hp?s=%5EGSPC+Historical+Prices.

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Authors and Affiliations

  1. Department of Economics, B843 Loeb, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada

    Hashmat Khan

  2. Department of Economics, D844 Loeb, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada

    Santosh Upadhayaya

  3. Ottawa-Carleton GSE, Ottawa, ON, Canada

    Hashmat Khan & Santosh Upadhayaya

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  1. Hashmat Khan
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  2. Santosh Upadhayaya
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Correspondence to Hashmat Khan.

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We thank three anonymous referees, Patrick Coe, Lilia Karnizova, Lynda Khalaf, Konstantinos Metaxoglou and participants at the Canadian Economic Association Conference, 2017 at Antigonish, Nova Scotia for comments.

Appendix

Appendix

1.1 Data construction and source

Business confidence index We obtain the business confidence index from the OECD’s leading indicator database. The OECD collects business confidence data, based on business tendency survey of manufacturing activity, from the Institute for Supply Management (ISM).Footnote 21 The business confidence series refers to PMI (previously, PMI referred to the Purchasing Managers’ Index), which is based on Manufacturing ROB. The PMI is an equally weighted (20% each) composite index of five seasonally adjusted diffusion indices, namely new orders, production, employment, supplier deliveries and inventories. An index value of over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month and a value of under 50 indicates contraction. The OECD converts the PMI diffusion index into a net balance (in %) for cross-country consistency.

Real business investment and its components The real business investment corresponds to the private non-residential fixed investment and its components are non-residential structure, equipment and intellectual property products. We obtain the data from NIPA Table 1.1.3 of BEA. Real gross domestic product We obtain the data for the real gross domestic product from NIPA Table 1.1.3 of BEA.

Price index of gross domestic product We obtain the data for the price index of real gross domestic product from NIPA Table 1.1.4 of BEA.

Price index of business investment We obtain the data for the price index of real business investment from NIPA Table 1.1.4 of BEA.

Real lending rate It is the prime business rate of commercial bank. We obtain the data from Economic Research Division, Federal Reserve Bank of St. Louis. Source: Board of Governors of the Federal Reserve System.

We calculate the real lending rate as an ex post measure as follows:

$$\begin{aligned} R_t = (i/100) - \log (\text {Price index of}\, \text {GDP}_{t+1}/\text {Price index of}\, \text {GDP}_{t}) \end{aligned}$$
(17)

where R is the real lending rate.

User cost of capital We measure the user cost of capital following Chirinko and Schaller (2001) and Ang (2010), which is similar to the Hall and Jorgenson (1969). The user cost of capital is as follows:

$$\begin{aligned} \text {CC}_t = (R_t + \text {DEP}_t)(\text {Price index of}\, \text {TBI}_t/\text {Price index of}\, \text {GDP}_t) \end{aligned}$$
(18)

where DEP is the depreciation. We fix the DEP as 5%.

Real cash flow It is the net cash flow with Inventory Valuation Adjustment (IVA) divided by the price index of gross domestic product. We obtain from Economic Research Division, Federal Reserve Bank of St. Louis. Source: BEA.

Stock market price It is the monthly S&P 500 index divided by the price index of gross domestic product. We collect from Yahoo!Finance.Footnote 22

Term spread It is the monthly rate of 10-year government bond minus the monthly rate of 3-month treasury bill. We obtain from Economic Research Division, Federal Reserve Bank of St. Louis. Source: Board of Governors of the Federal Reserve System.

Credit spread It is the Moody’s Baa corporate bond yield minus the Moody’s Aaa corporate bond yield. We obtain from Economic Research Division, Federal Reserve Bank of St. Louis. Source: Board of Governors of the Federal Reserve System.

1.2 SR calculation

The calculation of SR is as:

$$\begin{aligned} SR = \frac{{\hat{g}}^{uu}+{\hat{g}}^{dd}}{{\hat{g}}^{uu}+{\hat{g}}^{du}+{\hat{g}}^{ud}+{\hat{g}}^{dd}}, \end{aligned}$$
(19)

where \(\hat{g_t}\), u and d are the forecast of \(g_t\), upward signal and downward signal, respectively.

$$\begin{aligned} {\hat{g}}^{uu}= & {} \sum _{t=1}^{T}\mathbf{1 }[\hat{g_t} =1, g_t =1],\\ {\hat{g}}^{ud}= & {} \sum _{t=1}^{T}\mathbf{1 }[\hat{g_t} =1, g_t =0],\\ {\hat{g}}^{du}= & {} \sum _{t=1}^{T}\mathbf{1 }[\hat{g_t} =0, g_t =1],\\ {\hat{g}}^{dd}= & {} \sum _{t=1}^{T}\mathbf{1 }[\hat{g_t} =0, g_t =0] \end{aligned}$$
Table 13 OOS results: predictive ability of BCI for investment downturns, using control variables
Full size table

1.3 Additional models for downturns and direction of investment

Table 13 contains the results to assess the robustness whether BCI has independent forecasting power for business investment downturns, after controlling for other relevant predictors. Panel (a) shows that BCI-nested model performs better than BCI non-nested model for 1–4-quarter horizons. The result suggests that BCI has additional information to forecast investment downturns, after controlling for conventional predictors, TS and \(\Delta \hbox {SP}\) of recessions. Panel (b) shows that BCI-nested model is superior than BCI non-nested model for all forecast horizons, where we control for CS and \(\Delta \hbox {SP}\). We next consider CS, \(\Delta \hbox {SP}\) and \(\Delta \hbox {GDP}\) as control variables and show the results in panel (c). This result is also consistent with previous result and implies that BCI has independent information to forecast the investment downturns for 1–4-quarter horizons.

Table 14 OOS results: predictive ability of BCI for direction of investment growth, using control variables
Full size table
Table 15 Baseline forecast of business investment growth
Full size table

Finally, we use different control variables to evaluate whether BCI forecast for direction of investment growth independently. Table 14 shows the results. Panel (a) shows that BCI-nested model is better than BCI non-nested model for 1- and 3-quarter forecast horizons, suggesting that BCI has independent information to explain the direction of investment, controlling for conventional predictors, TS and \(\Delta \hbox {SP}\). We then control for CS and \(\Delta \hbox {SP}\) and show the results in panel (b). The results show that BCI-nested model has better performance than BCI non-nested model for 1- and 4-quarter horizons. Panel (c) also shows the results after controlling for three predictors, CS, \(\Delta \hbox {SP}\) and \(\Delta \hbox {GDP}\) and suggests that BCI has additional information to explain the direction of investment for 2-quarter horizons (Table 15).

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Khan, H., Upadhayaya, S. Does business confidence matter for investment?. Empir Econ 59, 1633–1665 (2020). https://doi.org/10.1007/s00181-019-01694-5

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