Cost behaviour and reporting frequency during the COVID-19 outbreak

Figures & data

Table 1. Descriptive statistics.

Panel A: Summary statistics by country and reporting frequency
Country	Obs.	Mean						Coverage
		ΔlnXOPRi	ΔlnSALEi	DECi	MVi	AINTi	ΔGDPc	% of Obs.	% of MCap.
Austria	30	−0.100	−0.106	0.700	20.461	0.181	−0.077	46.9%	53.2%
QRTi = 1	22	−0.089	−0.092	0.636	20.975	0.304
QRTi = 0	8	−0.129	−0.144	0.875	19.046	−0.155
Belgium	66	−0.059	−0.092	0.636	19.954	0.506	−0.074	54.1%	65.9%
QRTi = 1	23	−0.059	−0.004	0.652	21.006	0.573
QRTi = 0	43	−0.059	−0.138	0.628	19.391	0.470
Croatia	52	−0.284	−0.569	0.712	17.625	0.866	−0.089	85.2%	69.3%
QRTi = 1	52	−0.284	−0.569	0.712	17.625	0.866
QRTi = 0	-	-	-	-	-	-
Denmark	71	−0.016	−0.097	0.577	19.373	0.635	−0.013	50.0%	80.0%
QRTi = 1	57	0.031	0.083	0.561	20.062	0.444
QRTi = 0	14	−0.205	−0.833	0.643	16.566	1.412
Finland	116	−0.031	−0.010	0.448	19.130	0.024	−0.016	77.3%	75.6%
QRTi = 1	96	−0.022	0.002	0.427	19.594	0.057
QRTi = 0	20	−0.072	−0.063	0.550	16.904	−0.133
France	379	−0.083	−0.104	0.697	19.038	0.563	−0.092	63.4%	79.6%
QRTi = 1	216	−0.106	−0.144	0.727	20.277	0.431
QRTi = 0	163	−0.051	−0.052	0.656	17.396	0.738
Germany	335	−0.044	−0.072	0.630	19.466	0.321	−0.043	56.3%	61.4%
QRTi = 1	225	−0.045	−0.103	0.613	20.340	0.259
QRTi = 0	110	−0.041	−0.008	0.664	17.678	0.449
Greece	117	−0.162	−0.224	0.726	17.142	0.590	−0.103	78.5%	61.4%
QRTi = 1	22	−0.119	−0.181	0.727	19.798	0.340
QRTi = 0	95	−0.172	−0.234	0.726	16.527	0.648
Italy	213	−0.140	−0.207	0.704	18.909	0.499	−0.109	69.8%	59.5%
QRTi = 1	120	−0.116	−0.169	0.717	19.875	0.442
QRTi = 0	93	−0.170	−0.256	0.688	17.662	0.572
Netherlands	84	−0.009	−0.034	0.583	20.587	0.269	−0.027	66.1%	75.4%
QRTi = 1	50	0.007	−0.037	0.540	21.435	0.394
QRTi = 0	34	−0.032	−0.031	0.647	19.341	0.085
Norway	123	−0.043	−0.055	0.463	19.218	0.850	−0.142	61.5%	75.2%
QRTi = 1	111	−0.051	−0.053	0.459	19.347	0.727
QRTi = 0	12	0.031	−0.077	0.500	18.025	1.988
Poland	449	−0.119	−0.155	0.579	16.150	0.237	−0.022	67.7%	53.9%
QRTi = 1	288	−0.129	−0.135	0.576	17.100	0.270
QRTi = 0	161	−0.103	−0.190	0.584	14.451	0.179
Romania	57	−0.160	−0.249	0.632	16.955	0.565	−0.035	74.0%	57.4%
QRTi = 1	54	−0.131	−0.228	0.630	17.030	0.548
QRTi = 0	3	−0.678	−0.639	0.667	15.607	0.862
Spain	88	−0.041	−0.079	0.705	20.301	0.608	−0.119	52.4%	52.7%
QRTi = 1	78	−0.064	−0.098	0.718	20.603	0.601
QRTi = 0	10	0.136	0.074	0.600	17.945	0.663
Sweden	508	0.012	0.035	0.488	17.887	0.651	−0.033	71.8%	67.2%
QRTi = 1	496	0.014	0.043	0.492	17.945	0.627
QRTi = 0	12	−0.048	−0.283	0.333	15.485	1.637
Switzerland	133	−0.139	−0.150	0.759	20.618	0.374	0.011	56.6%	75.9%
QRTi = 1	35	−0.138	−0.106	0.829	22.096	0.445
QRTi = 0	98	−0.139	−0.166	0.735	20.090	0.349
UK	376	−0.051	−0.122	0.686	19.098	0.613	−0.064	27.4%	45.2%
QRTi = 1	32	−0.082	−0.229	0.750	21.319	0.762
QRTi = 0	344	−0.049	−0.112	0.680	18.891	0.599
Panel B: Full sample descriptive statistics
Variable	Mean	Std.Dev.	Min	P25	Median	P75	Max
ΔlnXOPRi	−0.070	0.319	−1.255	−0.195	−0.061	0.069	1.072
ΔlnSALEi	−0.103	0.484	−1.962	−0.262	−0.067	0.080	1.683
MVi	18.554	2.604	12.294	16.669	18.354	20.347	24.999
AINTi	0.487	1.025	−1.254	−0.144	0.251	0.847	4.777
ΔGDPc	−0.056	0.038	−0.142	−0.092	−0.043	−0.027	0.011
QRTi	0.618	0.485	0.000	0.000	1.000	1.000	1.000
DECi	0.617	0.486	0.000	0.000	1.000	1.000	1.000

Note: The table reports summary statistics by country and reporting frequency in Panel A and for the full sample in Panel B. QRT_i takes the value one if a firm i reports quarterly or semi-annually with business reviews, and zero otherwise. ΔlnXOPR_i (ΔlnSALE_i) is the log-change in operating cost (sales revenue) between H1 2020 and H1 2019. DEC_i takes the value one if the sales revenue in H1 2020 is smaller than in H1 2019, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2019. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2020 and H1 2019 for country c. For the sample coverage relative to the Compustat Global database, we display coverage in terms of observations (% of Obs.) and market capitalisation (% of MCap.).

Table 2. Reporting frequency and cost elasticity.

	(1)	(2)	(3)
	ΔlnXOPRi	ΔlnXOPRi	ΔlnXOPRi
QRTi	0.0143	−0.0026	−0.0008
	(1.06)	(−0.18)	(−0.05)
MVi		0.0053**	0.0061***
		(2.32)	(2.74)
AINTi		−0.0052	−0.0124
		(−0.58)	(−1.65)
ΔGDPc		0.1353	0.1685
		(0.87)	(1.11)
ΔlnSALEi	0.3553***	0.7883***	0.8014***
	(7.11)	(3.23)	(3.03)
ΔlnSALEi × QRTi	0.1583***	0.1436**	0.1362**
	(4.56)	(2.64)	(2.51)
ΔlnSALEi × MVi		−0.0168	−0.0174
		(−1.06)	(−1.02)
ΔlnSALEi × AINTi		−0.0479**	−0.0474**
		(−2.25)	(−2.26)
ΔlnSALEi×ΔGDPc		0.8140	0.8687
		(1.13)	(1.14)
Fixed effects	No	No	Industry
Adj. R-sq.	0.499	0.546	0.554
Obs.	3197	3197	3197

Note: The table reports the results for regressions analyses that examine the difference in cost elasticity between quarterly and semi-annual reporters. The dependent variable is the log-change in operating cost between H1 2020 and H1 2019 (ΔlnXOPR_i). The coefficient on the two-way interaction between ΔlnSALE_i and QRT_i is used to test the difference in cost elasticity. ΔlnSALE_i is the log-change in sales revenue between H1 2020 and H1 2019. QRT_i takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2019. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2020 and H1 2019. Column (1) reports the OLS estimation of Eq. (2) without control variables, Column (2) reports the OLS estimation of Eq. (2), and Column (3) reports the OLS estimation of Eq. (2) with industry fixed effects. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 3. Reporting frequency and cost asymmetry.

	(1)	(2)	(3)
	ΔlnXOPRi	ΔlnXOPRi	ΔlnXOPRi
QRTi	0.0416***	0.0346**	0.0358**
	(3.30)	(2.30)	(2.40)
MVi		0.0007	0.0014
		(0.32)	(0.58)
AINTi		0.0368***	0.0309**
		(3.07)	(2.59)
ΔGDPc		0.3248	0.3111
		(1.34)	(1.18)
ΔlnSALEi	0.3508***	0.4293	0.4491
	(3.00)	(0.98)	(1.05)
ΔlnSALEi × QRTi	0.0281	−0.0655	−0.0642
	(0.69)	(−1.16)	(−1.15)
ΔlnSALEi × MVi		0.0232	0.0214
		(0.84)	(0.78)
ΔlnSALEi × AINTi		−0.1906***	−0.1837***
		(−9.94)	(−10.07)
ΔlnSALEi×ΔGDPc		0.6518	0.7799
		(0.73)	(0.92)
ΔlnSALEi × DECi	0.0073	0.4127	0.4091
	(0.06)	(0.89)	(0.92)
ΔlnSALEi × DECi × QRTi	0.1933**	0.2810***	0.2716***
	(2.51)	(3.21)	(3.05)
ΔlnSALEi × DECi × MVi		−0.0466	−0.0457
		(−1.56)	(−1.60)
ΔlnSALEi × DECi × AINTi		0.1646***	0.1576***
		(5.79)	(5.66)
ΔlnSALEi × DECi×ΔGDPc		0.3039	0.1841
		(0.21)	(0.13)
Fixed effects	No	No	Industry
Adj. R-sq.	0.508	0.587	0.591
Obs.	3197	3197	3197

Note: The table reports the results for regressions analyses that examine the difference in cost asymmetry between quarterly and semi-annual reporters. The dependent variable is the log-change in operating cost between H1 2020 and H1 2019 (ΔlnXOPR_i). The coefficient on the three-way interaction between ΔlnSALE_i, DEC_i, and QRT_i is used to test the difference in cost asymmetry. ΔlnSALE_i is the log-change in sales revenue between H1 2020 and H1 2019. DEC_i takes the value one if the sales revenue in H1 2020 is smaller than in H1 2019, and zero otherwise. QRT_i takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2019. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2020 and H1 2019. Column (1) reports the OLS estimation of Eq. (3) without control variables, Column (2) reports the OLS estimation of Eq. (3), and Column (3) reports the OLS estimation of Eq. (3) with industry fixed effects. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 4. Reduced-form instrumental variable approach.

	(1)	(2)	(3)
	QTRi	ΔlnXOPRi	ΔlnXOPRi
REGc	0.4579***	0.0234**	0.0495***
	(28.08)	(2.15)	(4.41)
MVi	0.0684***	0.0067***	0.0037
	(22.27)	(4.13)	(1.54)
AINTi	−0.0046	−0.0113	0.0311**
	(−0.63)	(−1.46)	(2.64)
ΔGDPc	1.1319***	0.1419	0.3869
	(5.59)	(0.86)	(1.55)
ΔlnSALEi		0.5315**	0.3480
		(2.08)	(0.83)
ΔlnSALEi × REGc		0.1900***	0.0392
		(4.35)	(0.71)
ΔlnSALEi × MVi		−0.0047	0.0211
		(−0.31)	(0.88)
ΔlnSALEi × AINTi		−0.0462**	−0.1769***
		(−2.10)	(−9.95)
ΔlnSALEi×ΔGDPc		0.9323	0.2665
		(1.17)	(0.29)
ΔlnSALEi × DECi			0.2089
			(0.49)
ΔlnSALEi × DECi × REGc			0.2184**
			(2.51)
ΔlnSALEi × DECi × MVi			−0.0305
			(−1.22)
ΔlnSALEi × DECi × AINTi			0.1511***
			(5.31)
ΔlnSALEi × DECi×ΔGDPc			0.9830
			(0.66)
Fixed effects	Industry	Industry	Industry
Adj. R-sq.	0.277	0.559	0.593
Obs.	3197	3197	3197

Note: The table reports the results for reduced-form instrumental variable analyses that examine the difference in cost elasticity and cost asymmetry between quarterly and semi-annual reporters. In Column (1), the dependent variable is one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise (QRT_i). REG_c takes the value one if the firm’s country in 2004 required quarterly reporting at the main stock exchange, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2019. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2020 and H1 2019. In Columns (2) and (3), the dependent variable is the log-change in operating cost between H1 2020 and H1 2019 (ΔlnXOPR_i). In Column (2), the coefficient on the two-way interaction between ΔlnSALE_i and REG_c is used to test the difference in cost elasticity. In Column (3), the coefficient on the three-way interaction between ΔlnSALE_i, DEC_i, and REG_c is used to test the difference in cost asymmetry. ΔlnSALE_i is log-change in sales revenue between H1 2020 and H1 2019. DEC_i takes the value one if the sales revenue in H1 2020 is smaller than in H1 2019, and zero otherwise. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 5. Reporting frequency and cost behaviour among differentially affected firms.

Panel A: Cost elasticity and cost asymmetry in highly and less affected firms
	(1)	(2)	(3)	(4)
	ΔlnXOPRi	ΔlnXOPRi	ΔlnXOPRi	ΔlnXOPRi
QRTi	−0.0102	−0.0017	0.0436**	0.0128
	(−0.81)	(−0.09)	(2.56)	(0.59)
MVi	0.0099***	0.0048**	−0.0004	0.0031
	(5.89)	(2.15)	(−0.10)	(0.89)
AINTi	−0.0025	−0.0171	0.0236	0.0239*
	(−0.18)	(−1.62)	(1.29)	(1.75)
ΔGDPc	−0.1859	0.3552**	0.3114	0.2441
	(−0.75)	(2.60)	(0.88)	(0.85)
ΔlnSALEi	0.4476***	0.8588**	−0.7098*	0.7751**
	(3.88)	(2.52)	(−1.91)	(2.32)
ΔlnSALEi × QRTi	0.1619***	0.0439	−0.0830	0.0172
	(2.83)	(1.02)	(−0.82)	(0.41)
ΔlnSALEi × MVi	0.0061	−0.0178	0.0815***	0.0006
	(0.68)	(−0.91)	(3.19)	(0.03)
ΔlnSALEi × AINTi	−0.0362**	−0.0899***	−0.1303***	−0.1977***
	(−2.06)	(−3.80)	(−5.58)	(−5.44)
ΔlnSALEi×ΔGDPc	0.9533	0.5462	−2.1731	1.0841
	(0.52)	(0.84)	(−0.68)	(1.18)
ΔlnSALEi × DECi			1.4159***	0.0208
			(3.71)	(0.03)
ΔlnSALEi × DECi × QRTi			0.3379***	0.0846
			(2.89)	(0.77)
ΔlnSALEi × DECi × MVi			−0.0945***	−0.0248
			(−3.77)	(−0.65)
ΔlnSALEi × DECi × AINTi			0.1022***	0.1633***
			(3.49)	(3.19)
ΔlnSALEi × DECi×ΔGDPc			3.9566	−1.4137
			(1.14)	(−0.85)
Fixed effects	Industry	Industry	Industry	Industry
Adj. R-sq.	0.668	0.446	0.697	0.488
Obs.	1337	1860	1337	1860
Panel B: Tests of differences in coefficients between highly and less affected firms
			Result	p-value
β6ΔlnSALEi × QRTi in Col. (1) > Col. (2)			0.1181**	(0.0493)
β11ΔlnSALEi × DECi × QRTi in Col. (3) > Col. (4)			0.2533*	(0.0577)

Note: In Panel A, the table reports the results for regressions analyses that examine the difference in cost behaviour between quarterly and semi-annual reporters, for highly affected firms in Columns (1) and (3), and less affected firms in Columns (2) and (4) based on Koren and Peto (2020). The dependent variable is the log-change in operating cost between H1 2020 and H1 2019 (ΔlnXOPR_i). In Columns (1) to (2), the coefficient on the two-way interaction between ΔlnSALE_i and QRT_i is used to test the difference in cost elasticity. In Columns (3) to (4), the coefficient on the three-way interaction between ΔlnSALE_i, DEC_i, and QRT_i is used to test the difference in cost asymmetry. ΔlnSALE_i is the log-change in sales revenue between H1 2020 and H1 2019. QRT_i takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. DEC_i takes the value one if the sales revenue in H1 2020 is smaller than in H1 2019, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2019. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2020 and H1 2019. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. In Panel B, the table reports tests of differences in coefficients between the highly and less affected industries in Panel A. Following Clogg et al. (1995), the z-statistic is calculated as follows: $z={\displaystyle \frac{{\hat{\beta }}_1-{\hat{\beta }}_2}{\sqrt{\mathrm{SE}{({\hat{\beta }}_1)}^2+\mathrm{SE}{({\hat{\beta }}_2)}^2}}}$.

Table 6. Reporting frequency and cost behaviour before the COVID-19 outbreak.

Panel A: Cost elasticity and cost asymmetry regression results
	(1)	(2)
	ΔlnXOPRi	ΔlnXOPRi
QRTi	−0.0041	−0.0018
	(−0.38)	(−0.20)
MVi	−0.0007	−0.0022
	(−0.45)	(−1.05)
AINTi	0.0308***	0.0255***
	(2.86)	(2.88)
ΔGDPc	0.0383	0.0472
	(0.09)	(0.10)
ΔlnSALEi	−0.0028	−0.1399
	(−0.01)	(−0.69)
ΔlnSALEi × QRTi	0.0324	0.0238
	(1.13)	(0.35)
ΔlnSALEi × MVi	0.0306**	0.0414***
	(2.62)	(3.52)
ΔlnSALEi × AINTi	−0.1179***	−0.1213***
	(−7.64)	(−10.56)
ΔlnSALEi×ΔGDPc	3.7216	3.6000
	(1.54)	(1.47)
ΔlnSALEi × DECi		0.4151
		(0.96)
ΔlnSALEi × DECi × QRTi		0.0057
		(0.05)
ΔlnSALEi × DECi × MVi		−0.0304
		(−1.32)
ΔlnSALEi × DECi × AINTi		0.0096
		(0.29)
ΔlnSALEi × DECi×ΔGDPc		−0.3105
		(−0.06)
Fixed effects	Industry	Industry
Adj. R-sq.	0.565	0.571
Obs.	3048	3048
Panel B: Tests of differences in coefficients before and during COVID-19
	Result	p-value
Β6ΔlnSALEi × QRTi in Table 2 Col. (3) > Table 6 Col. (1)	0.1038**	(0.0452)
β11ΔlnSALEi × DECi × QRTi in Table 3 Col. (3) > Table 6 Col. (2)	0.2659**	(0.0417)

Note: In Panel A, the table reports the results for regressions analyses that examine the difference in cost behaviour between quarterly and semi-annual reporters. The dependent variable is the log-change in operating cost between H1 2019 and H1 2018 (ΔlnXOPR_i). In Column (1), the coefficient on the two-way interaction between ΔlnSALE_i and QRT_i is used to test the difference in cost elasticity. In Column (2), the coefficient on the three-way interaction between ΔlnSALE_i, DEC_i, and QRT_i is used to test the difference in cost asymmetry. ΔlnSALE_i is the log-change in sales revenue between H1 2019 and H1 2018. QRT_i takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. DEC_i takes the value one if the sales revenue in H1 2019 is smaller than in H1 2018, and zero otherwise. MV_i is the logarithm of the market value on December 31, 2018. AINT_i is the log-ratio of assets to sales revenue. ΔGDP_c is the change in gross domestic product between H1 2019 and H1 2018. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. In Panel B, the table reports tests of differences in coefficients between Panel A Column (1) and Column (2) and corresponding coefficients in Column (3) of Table 2 and Column (3) of Table 3. Following Clogg et al. (1995), the z-statistic is calculated as follows: $z={\displaystyle \frac{{\hat{\beta }}_1-{\hat{\beta }}_2}{\sqrt{\mathrm{SE}{({\hat{\beta }}_1)}^2+\mathrm{SE}{({\hat{\beta }}_2)}^2}}}$.

Table 7. Managerial learning and external monitoring.

Panel A: Managerial learning
	(1)	(2)	(3)	(4)
	FYSPEEDit	HYSPEEDit	WORDCOUNTit	NUMCOUNTit
COVIDt	−0.0820***	−0.0409***	−0.0176	−0.1755
	(−10.04)	(−4.84)	(−0.24)	(−1.57)
COVIDt × QRTit	0.0580***	0.0287***	0.1555**	0.2599**
	(6.95)	(2.77)	(2.23)	(2.46)
MVit	0.0108	0.0106	0.0683	0.2053
	(1.01)	(0.80)	(0.85)	(1.62)
MBit	0.0016**	0.0016	0.0044	−0.0023
	(2.37)	(1.39)	(0.91)	(−0.34)
LEVit	−0.0096	−0.1408**	−0.6520**	0.2186
	(−0.22)	(−2.51)	(−2.16)	(0.50)
TANGit	0.0411	0.1227	0.9072**	0.0018
	(0.63)	(1.43)	(2.03)	(0.00)
CASHit	−0.0078	−0.0057	0.5371	1.0553*
	(−0.16)	(−0.11)	(1.38)	(1.79)
ROAit	0.0956***	−0.0173	0.1984	−0.0630
	(3.69)	(−0.31)	(0.38)	(−0.06)
Fixed effects	Firm	Firm	Firm	Firm
Adj. R-sq.	0.887	0.877	0.666	0.568
Obs.	5466	5296	1214	1214
Panel B: External monitoring
	(1)	(2)	(3)	(4)
	UPDATEit	NEWit	CONFIRMit	ANALYSTSit
COVIDt	1.5355***	0.9655***	0.5700***	−0.8047***
	(5.55)	(4.87)	(5.93)	(−3.54)
COVIDt × QRTit	0.9201***	0.6239***	0.2962***	0.7645***
	(3.51)	(3.22)	(3.18)	(4.18)
MVit	0.5717*	0.4800**	0.0917	0.3920**
	(1.73)	(2.12)	(0.72)	(2.17)
MBit	0.0668	0.0207	0.0461	0.0082
	(0.96)	(0.53)	(1.42)	(0.22)
LEVit	−1.7687	−0.9794	−0.7893	−2.3959**
	(−1.28)	(−1.22)	(−1.11)	(−2.33)
TANGit	1.5693	0.4979	1.0714	4.9529**
	(1.05)	(0.44)	(1.47)	(2.59)
CASHit	−1.4636	−1.4689	0.0053	1.3610
	(−1.18)	(−1.66)	(0.01)	(1.14)
ROAit	−0.4311	−0.4038	−0.0272	3.4796**
	(−0.33)	(−0.45)	(−0.05)	(2.15)
Fixed effects	Firm	Firm	Firm	Firm
Adj. R-sq.	0.781	0.771	0.692	0.804
Obs.	2558	2558	2558	1278

Note: In Panel A, the table reports the results for regressions analyses that examine the difference in earnings announcement speeds and conference call content between quarterly and semi-annual reporters. In Column (1), the dependent variable is the logarithm of the full-year earnings announcement speed in days, multiplied by −1 (FYSPEED_it). In Column (2), the dependent variable is the logarithm of the half-year earnings announcement speed in days, multiplied by −1 (HYSPEED_it). In Column (3), the dependent variable is the logarithm of the word count in the statements of managers in the discussion period of the half-year conference call (WORDCOUNT_it). In Column (4), the dependent variable is the logarithm of the number count (excluding years) in the statements of managers in the discussion period of the half-year conference call (NUMCOUNT_it). In Panel B, the table reports the results for regressions analyses that examine the difference in analyst behaviour between quarterly and semi-annual reporters. In Column (1), the dependent variable is the number of analysts that updated their sales forecast for the firm (NEW_it) plus the number of analysts that confirmed their sales forecast (CONFIRM_it) during the first 90 days of the calendar year (UPDATE_it). In Columns (2) and (3), the dependent variable is NEW_it and CONFIRM_it, respectively. In Column (4), the dependent variable is the number of analysts attending the half-year conference call (ANALYSTS_it). The coefficient on the two-way interaction between COVID_t and QRT_it is used to test the difference between quarterly reporters in 2019 and 2020 relative to semi-annual reporters. COVID_t takes the value one if the fiscal year is 2020, and zero otherwise. QRT_it takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. MV_it is the logarithm of the market value on December 31 each year. MB_it is the ratio of market value to equity book value. LEV_it is the ratio of total debt to assets. TANG_it is the ratio of net property, plant, and equipment to assets. CASH_it is the ratio of cash and short-term investments to assets. ROA_it is the ratio of earnings before interest and taxes to assets. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 8. Cumulative abnormal returns around H1 2020.

Dependent variable:	(1)	(2)
	CARi[−3, 3]	CARi[−5, 5]
QRTi	0.0151*	0.0191**
	(1.89)	(2.34)
ΔlnSALEi	0.0035	0.0024
	(0.73)	(0.47)
Fixed effects	Industry, Country	Industry, Country
Adj. R-sq.	0.014	0.022
Obs.	2653	2653

Note: The table reports the results for regressions analyses that examine the difference in cumulative abnormal returns surrounding the 2020 half-year earnings announcement between quarterly and semi-annual reporters. Cumulative abnormal returns (CAR_i) for two windows were calculated using the Fama and French (2015) European five-factor model and the estimation period of [−270, −21]. The dependent variables in Columns (1) and (2) are CAR[−3, 3] and CAR[−5, 5], respectively. The coefficient on QRT_i is used to test the difference. QRT_i takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. ΔlnSALE_i is the log-change in sales revenue between H1 2020 and H1 2019. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 9. Firm performance.

Dependent variable	(1)	(2)
	ROAit	ROEit
Y2020t	−0.0043	−0.0760***
	(−0.82)	(−3.06)
Y2021t	0.0242***	0.0149
	(3.82)	(0.63)
Y2020t × QRTit	0.0129***	0.0520*
	(2.69)	(1.93)
Y2021t × QRTit	0.0046	0.0333
	(0.78)	(1.12)
MVit	−0.0467***	−0.1932***
	(−4.65)	(−5.40)
MBit	0.0000	0.0214***
	(0.02)	(4.22)
LEVit	0.0421	0.3313*
	(1.53)	(1.74)
TANGit	−0.0390	−0.1400
	(−1.20)	(−0.81)
CASHit	−0.0372	0.0829
	(−1.37)	(0.78)
Fixed effects	Firm	Firm
Adj. R-sq.	0.779	0.435
Obs.	7995	7455

Note: The table reports the results for regressions analyses that examine the difference in firm performance between quarterly and semi-annual reporters. In Column (1), the dependent variable is the ratio of earnings before interest and taxes to assets (ROA_it). In Column (2), the dependent variable is the ratio of income before extraordinary items to equity (ROE_it). The coefficient on the two-way interaction between Y2020_t (Y2021_t) and QRT_it is used to test the difference in firm performance of quarterly reporters between 2019 and 2020 (2021) relative to semi-annual reporters. Y2020_t (Y2021_t) takes the value one if the fiscal year is 2020 (2021), and zero otherwise. QRT_it takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. MV_it is the logarithm of the market value on December 31 each year. MB_it is the ratio of market value to equity book value. LEV_it is the ratio of total debt to assets. TANG_it is the ratio of net property, plant, and equipment to assets. CASH_it is the ratio of cash and short-term investments to assets. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 10. Insider holdings and seasoned equity offerings.

Dependent variable	(1)	(2)
	INSIDERit	SEOit
COVIDt	−0.0056	0.0309***
	(−1.32)	(3.02)
COVIDt × QRTit	0.0092**	−0.0162
	(2.10)	(−1.38)
MVit	0.0005	−0.1166***
	(0.10)	(−3.69)
MBit	0.0001	−0.0003
	(0.29)	(−0.24)
LEVit	0.0096	0.2477***
	(0.29)	(3.43)
TANGit	−0.0460	−0.1429
	(−1.19)	(−1.48)
CASHit	−0.0125	−0.2456***
	(−0.51)	(−2.98)
ROAit	0.0018	−0.0987
	(0.07)	(−1.06)
Fixed effects	Firm	Firm
Adj. R-sq.	0.860	0.217
Obs.	1370	5636

Note: The table reports the results for regressions analyses that examine the difference in insider holdings and seasoned equity offerings between quarterly and semi-annual reporters. In Column (1), the dependent variable is the proportion of insider holdings (INSIDER_it). In Column (2), the dependent variable is an indicator variable taking the value one if the company issued equity, and zero otherwise (SEO_it). The coefficient on the two-way interaction between COVID_t and QRT_it is used to test the difference in analyst reactions to quarterly reporters in 2019 and 2020 relative to semi-annual reporters. COVID_t takes the value one if the fiscal year is 2020, and zero otherwise. QRT_it takes the value one if the firm reports quarterly or semi-annually with business reviews, and zero otherwise. MV_it is the logarithm of the market value on December 31 each year. MB_it is the ratio of market value to equity book value. LEV_it is the ratio of total debt to assets. TANG_it is the ratio of net property, plant, and equipment to assets. CASH_it is the ratio of cash and short-term investments to assets. ROA_it is the ratio of earnings before interest and taxes to assets. A constant term is included in all estimations, but not reported. t-statistics, reported in the parentheses, are based on standard errors clustered at the industry level (2-digit SIC). ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

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