Does Economic Policy Uncertainty Affect M&amp;A Operations? Evidence from the Brazilian Market

Batista, Alexandre Teixeira Norberto; Lamounier, Wagner Moura; Mário, Poueri do Carmo

doi:10.15728/bbr.2023.20.2.2.en

Alexandre Teixeira Norberto Batista

conceptualization

methodology

formal analysis

data curation

investigation

writing – original draft

writing – review & editing

Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

https://orcid.org/0000-0003-1490-1662

Wagner Moura Lamounier

conceptualization

validation

supervision

writing – review & editing

Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

https://orcid.org/0000-0001-7154-6877

Poueri do Carmo Mário

conceptualization

validation

supervision

writing – review & editing

Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

https://orcid.org/0000-0003-4307-6040

alexandretnb@yahoo.com.br

wagner@face.ufmg.br

poueri@gmail.com

AUTHOR’S CONTRIBUTION

ATNB: Conceptualization, Methodology, Formal Analysis, Data Processing, Research, Writing - original draft, writing - proofreading and editing. WML: Conceptualization, Validation, Supervision, Writing - proofreading and editing. PCM: Conceptualization, Validation, Supervision, Writing - proofreading and editing.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

Explanatory Variables	Description	Source
LNEPU	Natural logarithm of the weighted average between the months of each year of the EPU Brazil index.	Nguyen and Phan (2017Nguyen, N. H., & Phan, H. V. (2017). Policy Uncertainty and Mergers and Acquisitions. Journal of Financial and Quantitative Analysis, 52(2), 613-644. https://doi.org/10.1017/S0022109017000175 https://doi.org/10.1017/S002210901700017... ); Bonaime et al. (2018Bonaime, A., Gulen, H., & Ion, M. (2018). Does policy uncertainty affect mergers and acquisitions? Journal of Financial Economics, 129(3), 531-558. https://doi.org/10.1016/j.jfineco.2018.05.007 https://doi.org/10.1016/j.jfineco.2018.0... ); Borthwick et al. (2020Borthwick, J., Ali, S., & Pan, X. (2020). Does policy uncertainty influence mergers and acquisitions activities in China? A replication study. Pacific-Basin Finance Journal, 62, 101381. https://doi.org/10.1016/j.pacfin.2020.101381 https://doi.org/10.1016/j.pacfin.2020.10... ); Sha et al. (2020Sha, Y., Kang, C., & Wang, Z. (2020). Economic policy uncertainty and mergers and acquisitions: Evidence from China. Economic Modelling, 89, 590-600. https://doi.org/10.1016/j.econmod.2020.03.029 https://doi.org/10.1016/j.econmod.2020.0... ).
LNIIE	Natural logarithm of the weighted average between the months of each year of the IIE Brazil index.
Firm level
LNSIZE	Natural logarithm of total assets.
ROA	Ratio of earnings before interest and taxes to total assets.
VSALES	Change in net revenue in relation to t-1.
LEV	Ratio between total debt and total assets.
CX	Ratio between cash and equivalents and total assets.
MTB	Market-to-Book Index. Ratio between market value and book value of equity.
RET	Buy-and-hold stock return during period t trading days.
VOL	Annualized standard deviation of daily stock returns during period t.
Industry level
IMTB	Median of the Market-to-Book index for each industry in period t
IRET	Median of returns for each industry in period t
IVOL	Median of annualized standard deviations of daily returns for each sector in period t
HHI	Herfindahl-Hirschman Index: sum of the square of the market shares of companies in the industry.
Macroeconomic level
INVOP	Investment opportunities: first principal component extracted from the linear combination between four indexes: Consumer Confidence Index - ICC; Economic Activity Index - IBC; Future Expectations Index - IEX; and yearly change in GDP .
SELIC	Yearly variation of the Selic rate.

Note: the calculation of industry level variables was not restricted to the companies in the sample. In this case, all listed companies with available data in the industry in period t were considered to calculate the medians and the HHI. In calculating the macroeconomic level variable INVOP, the year average was considered for the ICC, IBC and IEX indexes, that are released monthly by the Central Bank of Brazil (https://www3.bcb.gov.br/sgspub/). Details of the PCA results are provided in ^{Appendix A} APPENDIX A: Results of Principal Components Analysis In Table A1 below, the PCA results are presented. The results were obtained with the variables ICC, IBC, IEX and real variation of GDP per year. We consider the average between the months of each year for the ICC, IBC, IEX indexes, which have a monthly frequency, to coincide with the periodicity of the series used in the regression analysis, which are annual. To obtain the correlation matrix and the extracted components, we used the entire history of available data for these variables, starting in 2003, since it is the year in which the publication of the series of the analyzed indexes begins, until the date of the analysis (2020). Table A1: Eigenvalues and Proportion of Total Variance Explained Component Eigenvalue Difference Proportion Accumulated 1 2.43362 1.33377 0.6084 0.6084 2 1.09985 0.711264 0.2750 0.8834 3 0.388591 0.310659 0.0971 0.9805 4 0.0779318 0 0.0195 1.0000 Source: Authors’ own elaboration. Through linear combinations between the original variables, 4 components were extracted, however, only the first component was considered for the variable INVOP, as it explains most of the total variance of the variables included, with a proportion of 60.84%. According to Hair et al. (2009, p. 112), principal component analysis is most appropriate when “data reduction is a priority concern, focusing on the minimum number of factors necessary to explain the maximum proportion of the total variance represented in the original set of variables”. Therefore, it is a less restrictive and simpler component extraction method than common factor analysis. In Table A2, the eigenvectors are shown, which denote the importance of each variable for the extracted component and the sign indicates the direction in which they are related. Table A2: Eigenvectors and Principal Components Variable Comp. 1 Comp. 2 Comp. 3 Comp. 4 IBC -0.0417 0.9303 0.3282 0.1582 ICC 0.6129 0.1856 -0.0801 -0.7639 IEX 0.5836 0.1328 -0.5723 0.5606 GDP 0.5311 -0.2870 0.7472 0.2780 Source: Authors’ own elaboration. Bartlett’s sphericity test tests the null hypothesis that the correlation matrix is an identity matrix. The results for this test showed a p-value of less than 0.05, which indicates the existence of significant correlations between the variables and allows the analysis to proceed (Hair et al., 2009). Reference Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th ed). Prentice Hall. . Due to limitations in the available data, some variables used to calculate INVOP differ from those used by Bonaime et al. (2018Bonaime, A., Gulen, H., & Ion, M. (2018). Does policy uncertainty affect mergers and acquisitions? Journal of Financial Economics, 129(3), 531-558. https://doi.org/10.1016/j.jfineco.2018.05.007
https://doi.org/10.1016/j.jfineco.2018.0... ).
Source: Authors’ own elaboration.

Variable	N	Mean	Median	Std.Dev	CV	Minimum	Maximum
MA (t+1)	980	0.164	0	0.371	2.257	0.000	1.000
(Ln) EPU	980	5.144	4.987	0.441	0.086	4.347	5.726
(Ln) IIE-Br	980	4.652	4.638	0.093	0.020	4.497	4.766
(Ln) SIZE	980	15.712	15.656	1.594	0.101	10.973	19.874
ROA	980	0.045	0.045	0.067	1.500	-0.221	0.231
VSALES	958	0.056	0.034	0.273	4.880	-0.631	1.569
LEV	977	0.292	0.296	0.165	0.565	0.000	0.686
CX	977	0.088	0.667	0.079	0.900	0.000	0.409
MTB	977	2.238	1.425	2.229	0.996	0.170	11.896
RET	968	0.141	0.059	0.482	3,417	-0.721	1.889
VOL	980	0.300	0.251	0.164	0.545	0.136	1.131
IMTB	980	1.399	1.313	0.601	0.430	0.208	4.905
IRET	980	0.059	0.012	0.256	4.352	-0.345	0.750
IVOL	980	0.376	0.361	0.077	0.203	0.241	0.699
HHI	980	0.123	0.062	0.125	1.014	0.041	0.683
INVOP	980	-0.191	-0.738	1.929	-10.096	-3.322	2.658
SELIC	980	0.103	0.099	0.023	0.228	0.064	0.140

Note: N: number of observations. Std.Dev: Standard Deviation. CV: Coefficient of Variation. Obs.: The correlation matrix between the variables used in the regressions and their analysis can be found in ^{Appendix B} APPENDIX B: Correlation Matrix The preliminary relationship between the variables analyzed in this study can be seen in the following table: Table B1: Correlation Matrix between the variables used in the regressions MA t+1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2 (Ln) EPU -0.07 3 (Ln) IIE -0.03 0.75 4 (Ln) SIZE 0.13 0.00 0.00 5 ROA 0.03 -0.18 -0.11 -0.01 6 VSALES 0.07 -0.23 -0.13 0.05 0.17 7 LEV 0.04 0.02 0.00 0.30 -0.33 0.01 8 CX 0.03 0.02 0.01 -0.03 0.08 0.00 0.10 9 MTB 0.00 -0.13 -0.09 -0.10 0.32 0.08 0.07 0.08 10 RET 0.05 0.11 0.12 -0.04 0.25 0.18 -0.10 0.05 0.22 11 VOL -0.02 0.17 0.17 -0.34 -0.29 -0.07 0.00 -0.11 -0.05 0.05 12 IMTB 0.04 -0.27 -0.21 -0.10 0.15 0.11 -0.06 0.03 0.24 0.08 -0.15 13 IRET 0.01 0.25 0.26 -0.03 0.02 0.01 -0.05 0.00 0.08 0.53 0.05 0.20 14 IVOL -0.05 0.46 0.50 -0.01 -0.17 -0.14 -0.01 0.06 -0.15 0.02 0.25 -0.56 0.03 15 HHI 0.05 0.01 -0.01 0.27 -0.05 0.03 -0.07 0.10 -0.03 -0.07 -0.06 -0.02 -0.14 0.29 16 INVOP 0.05 -0.85 -0.66 -0.01 0.18 0.21 -0.04 -0.02 0.15 0.04 -0.16 0.35 -0.01 -0.48 -0.01 17 SELIC -0.03 0.46 0.37 -0.02 -0.09 -0.17 0.03 0.03 -0.08 -0.04 0.15 -0.26 0.00 0.38 0.03 -0.48 Note: In bold significant correlations at 10%. Source: Authors’ own elaboration. Moderate and strong correlations were found in some cases between the independent variables of the model, however the mean VIF was 1.96 for the specification with EPU and 1.63 for the specification with IIE-Br. The correlations that take into account the variable MA(t+1) were significant in four cases: EPU, SIZE, VSALES and RET, with a negative sign in the uncertainty variable. It is worth highlighting some correlations that consider the uncertainty variables. It was noticed that ROA, revenue variation, Market-to-Book at firm and industry levels react negatively to uncertainty fluctuations, with greater intensity for political uncertainty. Stock volatility tends to positively accompany measures of uncertainty, with emphasis on the median volatility of the industry, which has moderate intensity. Such evidence shows that this measure may be able to capture the effects of uncertainty in the environment, reflected in greater dispersion of returns at the industry level and is a fact that may justify its use as a proxy for uncertainty. The INVOP variable has a strong negative correlation with uncertainty (considering the EPU, IIE-Br and median industry volatility). This shows that moments of high uncertainty are strongly associated with poor economic conditions, reflected in expectations of agents about economic activity. On the other hand, the interest rate has a positive association with uncertainty. The causal effects of uncertainty on financial and market variables are still unclear. In this sense, with the analysis of the preliminary relationship between these variables, we suggest that their causality be tested in new studies. of this document.
Source: Authors’ own elaboration with research data.

	Dependent Variable MA (t+1)
	(1)			(2)
	(Ln) EPU	Standard Error	\|z\|	(Ln) IIE-Br	Standard Error	\|z\|
Uncertainty	-0.754	0.386	-1.96^*	-0.263	1.153	-0.23
(Ln) SIZE	0.338	0.091	3.73^***	0.337	0.088	3.82^***
ROA	1.513	1.921	0.79	1.670	1.936	0.86
VSALES	0.262	0.459	0.57	0.338	0.438	0.77
LEV	-0.054	0.355	-0.15	-0.033	0.350	-0.09
CX	2.145	0.907	2.37^**	2.160	0.884	2.44^**
MTB	-0.053	0.042	-1.27	-0.052	0.042	-1.24
RET	0.453	0.269	1.69^*	0.422	0.259	1.63
VOL	1.286	0.778	1.65^*	1.260	0.766	1.65
IMTB	0.203	0.310	0.66	0.183	0.293	0.62
IRET	-0.181	0.678	-0.27	-0.411	0.704	-0.58
IVOL	-0.052	1.959	-0.03	0.086	2.645	0.03
HHI	-6.539	3.378	-1.94^*	-6.330	3.253	-1.95^*
INVOP	-0.092	0.058	-1.59	0.047	0.082	0.57
SELIC	0.088	2.138	0.04	-0.603	3.567	-0.17
Constant	-3.206	1.911	-1.68^*	-5.769	5.808	-0.99
Obs.		943			943
Pseudo-R2		0.0704			0.0671

	Dependent Variable MA (t+1)
	(3)			(4)
	(Ln) EPU	Standard Error	\|z\|	(Ln) IIE-Br	Standard Error	\|z\|
Uncertainty	-0.745	0.298	-2.5^**	-0.352	0.800	-0.44
(Ln) SIZE	0.283	0.072	3.93^***	0.283	0.071	3.97^***
ROA	0.714	1.589	0.45	0.983	1.509	0.65
VSALES	0.300	0.428	0.7	0.383	0.398	0.96
LEV	-0.065	0.343	-0.19	-0.037	0.334	-0.11
CX	1.905	0.765	2.49^**	1.901	0.760	2.5^**
MTB	-0.049	0.047	-1.06	-0.049	0.046	-1.06
RET	0.457	0.183	2.5^**	0.365	0.156	2.34^**
HHI	-6.429	3.118	-2.06^**	-6.087	3.168	-1.92^*
INVOP	-0.090	0.046	-1.95^**	0.045	0.072	0.63
Constant	-1.719	1.432	-1.2	-3.911	4.153	-0.94
Obs.		943			943
Pseudo-R2		0.0656			0.0619

	Dependent Variable MA (t+1)
	(5)			(6)
	(Ln) EPU	Standard Error	\|z\|	(Ln) IIE-Br	Standard Error	\|z\|
Uncertainty	-0.638	0.300	-2.13^**	-0.045	0.729	-0.06
(Ln) SIZE	0.252	0.042	6.03^***	0.251	0.040	6.25^***
ROA	0.756	1.586	0.48	0.975	1.497	0.65
VSALES	0.403	0.423	0.95	0.474	0.400	1.19
LEV	0.322	0.235	1.37	0.340	0.233	1.46
CX	1.778	0.844	2.11^**	1.785	0.850	2.1^**
MTB	-0.023	0.044	-0.52	-0;021	0.044	-0.49
RET	0.412	0.159	2.59^***	0;320	0.129	2.49^**
HHI	0.126	0.737	0.17	0;112	0.737	0.15
INVOP	-0.069	0.046	-1.48	0;054	0.069	0.79
Constant	-2.747	1.500	-1.83^*	-5;780	3.475	-1.66^*
Obs.		943			943
Pseudo-R2		0.0384			0.035

Models	Bonaime et al. (2018Bonaime, A., Gulen, H., & Ion, M. (2018). Does policy uncertainty affect mergers and acquisitions? Journal of Financial Economics, 129(3), 531-558. https://doi.org/10.1016/j.jfineco.2018.05.007 https://doi.org/10.1016/j.jfineco.2018.0... )	Borthwick et al. (2020Borthwick, J., Ali, S., & Pan, X. (2020). Does policy uncertainty influence mergers and acquisitions activities in China? A replication study. Pacific-Basin Finance Journal, 62, 101381. https://doi.org/10.1016/j.pacfin.2020.101381 https://doi.org/10.1016/j.pacfin.2020.10... )	Nguyen and Phan (2017Nguyen, N. H., & Phan, H. V. (2017). Policy Uncertainty and Mergers and Acquisitions. Journal of Financial and Quantitative Analysis, 52(2), 613-644. https://doi.org/10.1017/S0022109017000175 https://doi.org/10.1017/S002210901700017... )	Sha et al. (2020Sha, Y., Kang, C., & Wang, Z. (2020). Economic policy uncertainty and mergers and acquisitions: Evidence from China. Economic Modelling, 89, 590-600. https://doi.org/10.1016/j.econmod.2020.03.029 https://doi.org/10.1016/j.econmod.2020.0... )	Model 1	Model 3
Country	USA	China	USA	China	Brazil	Brazil
Obs.	115,796	20,966	88,768	29,588	943	943
Pseudo-R2	-	0.02	0.07	0.01	0.07	0.07
Variables
Economic Policy Uncertainty	-	-	-	+	-	-
Size	+	+	+	+	+	+
Return on Assets	+	+	n/a	.	.	.
Sales Variation	+	+	+	n/a	.	.
Leverage	-	+	-	.	.	.
Cash and Equivalents	+	+	n/a	+	+	+
Operating Working Capital	n/a	n/a	+	n/a	n/a	n/a
Market-to-book	+	-	+	.	.	.
Past Returns	+	+	+	+	+	+
Returns Volatility	-	+	n/a	n/a	+	n/a
Shiller’s CAPE	+	.	n/a	n/a	n/a	n/a
Market-to-book (industry median)	.	+	n/a	n/a	.	n/a
Past returns (industry median)	+	.	n/a	n/a	.	n/a
Volatility (industry median)	-	-	n/a	n/a	.	n/a
Industry Economic Shocks	+	-	n/a	n/a	n/a	n/a
Herfindahl-Hirschman	n/a	n/a	n/a	n/a	-	-
Investment Opportunities	-	-	n/a	n/a	.	-
Interest Rate	+	+	n/a	n/a	.	n/a
Macroeconomic Uncertainty	-	+	n/a	n/a	n/a	n/a
Firm Age	n/a	n/a	+	n/a	n/a	n/a
Constant	.	-	-	-	-	.

\frac{\partial p (x)}{\partial x_{j}} = G^{'} (β_{0} + x β) β_{j} = \frac{e^{Z}}{1 + e^{Z}} \frac{1}{1 + e^{Z}} β_{j} \frac{e^{Z}}{{(1 + e^{Z})}^{2}} β_{j}

Component	Eigenvalue	Difference	Proportion	Accumulated
1	2.43362	1.33377	0.6084	0.6084
2	1.09985	0.711264	0.2750	0.8834
3	0.388591	0.310659	0.0971	0.9805
4	0.0779318	0	0.0195	1.0000

Variable	Comp. 1	Comp. 2	Comp. 3	Comp. 4
IBC	-0.0417	0.9303	0.3282	0.1582
ICC	0.6129	0.1856	-0.0801	-0.7639
IEX	0.5836	0.1328	-0.5723	0.5606
GDP	0.5311	-0.2870	0.7472	0.2780

		MA t+1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
2	(Ln) EPU	-0.07
3	(Ln) IIE	-0.03	0.75
4	(Ln) SIZE	0.13	0.00	0.00
5	ROA	0.03	-0.18	-0.11	-0.01
6	VSALES	0.07	-0.23	-0.13	0.05	0.17
7	LEV	0.04	0.02	0.00	0.30	-0.33	0.01
8	CX	0.03	0.02	0.01	-0.03	0.08	0.00	0.10
9	MTB	0.00	-0.13	-0.09	-0.10	0.32	0.08	0.07	0.08
10	RET	0.05	0.11	0.12	-0.04	0.25	0.18	-0.10	0.05	0.22
11	VOL	-0.02	0.17	0.17	-0.34	-0.29	-0.07	0.00	-0.11	-0.05	0.05
12	IMTB	0.04	-0.27	-0.21	-0.10	0.15	0.11	-0.06	0.03	0.24	0.08	-0.15
13	IRET	0.01	0.25	0.26	-0.03	0.02	0.01	-0.05	0.00	0.08	0.53	0.05	0.20
14	IVOL	-0.05	0.46	0.50	-0.01	-0.17	-0.14	-0.01	0.06	-0.15	0.02	0.25	-0.56	0.03
15	HHI	0.05	0.01	-0.01	0.27	-0.05	0.03	-0.07	0.10	-0.03	-0.07	-0.06	-0.02	-0.14	0.29
16	INVOP	0.05	-0.85	-0.66	-0.01	0.18	0.21	-0.04	-0.02	0.15	0.04	-0.16	0.35	-0.01	-0.48	-0.01
17	SELIC	-0.03	0.46	0.37	-0.02	-0.09	-0.17	0.03	0.03	-0.08	-0.04	0.15	-0.26	0.00	0.38	0.03	-0.48

[1] alexandretnb@yahoo.com.br

Brasil

Brasil

Does Economic Policy Uncertainty Affect M&A Operations? Evidence from the Brazilian Market

Abstract