The first hypothesis is about the relationship between investment volatility and a company's level of debt and liquidity. In terms of the relationship between investment volatility and debt levels, our evidence indicates that high fixed investment and acquisition volatility lead to higher debt levels. Overall, our evidence does not support DeAngelo et al. 2011) model prediction that firms with high investment volatility keep debt levels low and cash levels high.
Interestingly, in contrast to the volatility of capital expenditures and acquisitions, this finding supports the claims of DeAngelo et al.'s 2011) model prediction that firms with high investment volatility keep debt levels low and cash levels high. However, the result that the volatility of research and development investments leads to high cash levels is no longer statistically significant. Although we cannot observe marginal productivity shocks in the study of DeAngelo et al. 2011) we can observe the volatility of investments at the firm level.
Control Variables
To control for variables affecting a firm's cash level, we follow Opler et al. xi) DP f irms is a dummy variable set to one if the firm pays a dividend in year t, and zero otherwise. xiii). Finally, we control for the investment variables used to construct our measures of investment volatility. xiii). Capx is the natural logarithm of the ratio of capital expenditure to property, plant and equipment (ppegt).
Because we already control for R&D using the Frank and Goyal (2009) measure, we do not add an additional control for R&D.
Univariate Statistics
Acquisitions are the natural logarithm of the ratio of procurement expenditure to tangible fixed assets (ppegt). To mitigate possible omitted variable bias, we use all the control variables in all tests. The correlation coefficients between M arketT oBook and Inv, Capx and Acq are small and negative, while the correlation between Xrd and M arketT oBookratio is small and positive.
This suggests that most of the change in the composite measure of investment is driven by purchases.
4 Testing
- Estimation approach
- Hypotheses 1a and 1b - Effect of investment volatility on a firm’s debt and cash levels
- Testing Hypotheses 2a and 2b - Effect of investment spikes on a firm’s debt and cash levels
- Hypotheses 3a and 3b - The intertemporal effect of investment spikes on firm debt and cash levels
The coefficient associated with acquisition volatilityAcqV olt−5,t is positive and statistically significant at less than the 1% level, which is opposite to the predicted relationship. The coefficient associated with research and development volatility XrdV olt−5,t is negative and statistically significant at less than the 1% level, which is consistent with the predicted relationship. The coefficient associated with research and development volatility XrdV olt−5,t is positive and statistically significant at less than the 1% level, which is consistent with the predicted relationship.
The coefficient associated with the increase in capital expenditures is positive and statistically significant at the less than 1% level, which is consistent with the predicted relationship. The coefficient associated with the acquisition increase is positive and statistically significant at the less than 1% level, which is consistent with the predicted relationship. The coefficient associated with the lead of capital expenditure spikeCapxSpiket+1 is negative and statistically significant at the less than 1% level, which is consistent with the predicted relationship.
The coefficient associated with acquisition spike AcqSpiket+1 is negative and statistically significant at the less than 1% level, which is consistent with the predicted relationship. The coefficient associated with research and development spike XrdSpiket+1 is negative and statistically significant at the less than 1% level, which is consistent with the predicted relationship. The coefficient associated with the lead of capital expenditure spikeCapxSpiket+1 is positive and statistically significant at the less than 1% level, which is consistent with the predicted relationship.
The coefficient associated with the acquisition peak AcqSpiket+1 is positive and statistically significant at less than the 1% level, which is consistent with the predicted relationship. The coefficient associated with research and development spikeXrdSpiket+1 is positive and statistically significant at less than the 1% level, which is consistent with the predicted relationship.
5 Discussion and Robustness
Economic Importance
Although not explicit in the model of DeAngelo et al. 2011), our evidence supports the plausible implication of their model that firms rebuild their cash stocks after depleting their cash stocks to finance large investments. The table also shows that the economic importance is slightly lower when using the panel data model. For example, the table reports a 12.02% decline in a company's cash level due to a one standard deviation increase from the mean of InvV olt−6,t−1.
Robustness to alternative measures
- Robustness using market debt ratios
- Robustness using an alternative cash ratio
- Robustness using multiple investment spikes
Retesting our three cash hypotheses using Cash_na, our main results remain qualitatively unchanged for Hypotheses 2b and 3b and remain qualitatively unchanged for Hypothesis 1a using capital expenditure and acquisition volatility but not R&D volatility. In addition to our primary investment spike variable representing firms with large investments, we construct a two consecutive investment spike variable representing firms with two consecutive large investments. The use of two investment spikes increases the magnitude of the coefficients associated with all the spike variables in hypothesis 2a and the coefficient associated with CapxSpike in hypothesis 2b.
Robustness – A re-examination of results related to Hypothesis 1
DeAngelo and Roll (2015) show that a firm's capital structure exhibits both high and low debt levels over extended periods of time. DeAngelo and Roll (2015) attribute these high debts to the need to finance investments. Consistent with the need to finance high levels of investment over time, investment volatility is also persistent over time.
Correlated investment shocks require firms to finance investments, likely leading to persistently high debt and low cash. To test whether our results are partly due to a correlated investment shock, we reestimate where the interest rate variable is future investment volatility and the dependent variable is the book debt ratio of cash holdings. We find that firms with high future investment volatility hold more debt and less cash. Firms with future uncertain investments should maintain their financing capacity by maintaining relatively low levels of debt and high levels of cash.
6 Conclusion
The first hypothesis investigates whether firms with higher investment volatility have lower levels of debt and higher levels of cash. Our results are economically relevant - we predict the percentage change in debt-to-cash ratios with a one standard deviation increase from the mean of all four investment volatility variables. the decrease in cash levels as a result of a one standard deviation increase from the mean of capital expenditures plus acquisition volatility. Overall, our evidence does not support the model of DeAngelo et al. 2011) that firms with high investment volatility have low levels of debt and high levels of cash.
The second hypothesis tests the effect of large investments on companies' debt and cash positions. The intuition behind this hypothesis is that companies issue debt and use their cash positions to finance large investments. Our results are of economic interest: we predict the percentage change in debt and cash ratios from the change in the dummy variable for the investment peak from zero to one.
Our evidence supports the predictions of DeAngelo et al. 2014) that companies finance their large investments by issuing debt and using their cash resources. Our results are economically relevant - we predict the percentage change in debt-to-cash ratios by changing the investment jump dummy variable from zero to one. Our findings suggest that high R&D investment volatility is accompanied by lower debt and higher cash levels.
Plausibly, volatility in research and development investment may be a reasonable proxy for shocks to the marginal productivity of capital, the latent variable in DeAngelo et al. In addition, our evidence suggests that an R&D increase is not an important determinant of a firm's level of debt and liquidity.
7 Appendix
1986), "Evidence on the Impact of Agency Costs of Debt on Corporate Debt Policy", The Journal of Financial and Quantitative Analysis Back to the beginning: Persistence and the cross-section. 1984), 'Corporate financing and investment decisions when firms have information. 1999), 'Workshops and the Implications of Corporate Cash. 2007), “What is the market value of a dollar of entrepreneurial money?”, Journal of Applied. 2011), “Two common problems in capital structure research: the financial debt-to-asset ratio and issuance. This table shows summary statistics on the study's variables for US non-financial and non-utility firms from 1974 to 2015.
All variables are winsorized at the 0.1% level in both tails of the distribution before calculating summary statistics. Table 3: Testing Hypothesis 1a This table shows the estimation results of equation (6) using GLM with a logit link function and equation (7) using panel GLM, where short- and long-term book debt (BDR) is the dependent variable. All RHS variables are in information set and used in the labeled form. )to(8)displaytheestimationresultsusingpanelGLM.Thevariablesofinterestarethemeasureofinvestmentvolatilitywithdifferentinvestmentdefinitionsincludingthesumofcapitalexpendituresandacquisitions(Capx+Acq),capitalexpenditures(Capx),acquisitions(Acq),andresearchanddevelopmentexpenditures(R&D). Section 3.2 defines the variables. Clustered standard errors per company are shown in brackets with a significance level of 1%, 5% and 10% indicated by ***,**and* respectively. Table 4: Test Hypothesis 1b This table shows the estimation results of equation (6) using GLM with a logit link function and equation (7) using panel GLM, where the ratio of cash total assets (cash) is the dependent variable . All RHS variables are in the information set and used in the labeled form. (8) showed estimation results using panel GLM. The variables of interest are the measurement of investment volatility with different investment definitions including the sum of capital expenditures and acquisitions (Capx+Acq), capital expenditures (Capx), acquisitions (Acq) and research and development expenditures (R&D). Section 3.2 defines the variables. Clustered standard errors of firms are shown in brackets at a significance level of 1%, 5%, and 10%, indicated by ***,**, and*, respectively.
Table 5: Testing Hypothesis 2a This table shows the results of estimating equation (6) using GLM with the link function and equation (7) using panel GLM, where short-term and long-term book debt parity (BDR) is the variable depended. show estimation results using GLMand columns(5) to (8) show estimation results using GLM panel. Variables for investment stakeholders measured using several definitions of investment, including the sum of capital expenditures and acquisitions (Capx+Acq), capital expenditures (Capx), acquisitions (Acq), and research and development (R&D) expenditures. Section 3.2 defines variables. Table 6: Testing Hypothesis 2b This table shows the results of estimating equation (6) using GLM with the logical link function and equation (7) using panel GLM, where the cash ratio over total (cash) are the dependent variables . as the estimation result using GLMand columns(5) to (8) shows the estimation results in the GLM panel. (Capx), purchases (Acq) and research and development (R&D) expenses. Section 3.2 defines the variables. Standard errors grouped by country are shown in parentheses with 1%, 5%, and 10% significance levels denoted by ***, **, and *, respectively. Table 7: Testing Hypothesis 3a This table shows the results of estimating equation (6) using GLM with the link function and equation (7) using panel GLM, where short-term and long-term book debt parity (BDR) is a dependent variable. display the estimation results using the GLMand columns (5) to (8) display the estimation results using the GLM panel. Vested interest and investment peak variables measured using several investment definitions, including the sum of capital expenditures and purchases (Caqx), capital expenditures (Capx), purchases (Acq), and research and development (R&D) expenditures. Section 3.2 defines variables.
Table 8: Test Hypothesis 3b This table shows estimation results of equation (6) using GLM with a log-link function and equation (7) using panel GLM, where cash to total set (Cash) is the dependent variable. to (8)show the estimation results using panelGLM. The interest variables are lag-and-lead preinvestment targets using several investment definitions, including the sum of capital expenditures and acquisitions (Capx+Acq), capital expenditures (Capx), acquisitions (Acq), and research and development expenditures (R&D). Section 3.2 defines the variables. Clustered standard errors by states are shown in parentheses with 1%, 5%, and 10% significance levels indicated by ***, **, and *, respectively. For each company, the variables are averaged over the entire sample period, so that there is one observation for each company. This table shows the pairwise correlation coefficients between 5-year lag and 5-year lead investment volatility.
Reference numbers in columns and rows refer to variables associated with pairwise correlation coefficients.
