Do prospector firms pay more dividends in times of high policy uncertainty?

Figures & data

Figure 1. Cross-sectional summary statistics for the dividend payout ratio for U.S. non-financial firms by year, from 1995 to 2018.

Figure 2. Cross-sectional summary statistics for the ratio of research and development to dividend payouts for U.S. non-financial firms by year, from 1995 to 2018.

Table 1. Summary of sample characteristics.

Panel A: Descriptive statistics for the whole sample
Variables	Sample	Mean	Median	Min	P25	P75	Max	SD
PROSPECTOR	59,546	0.5444	1	0	0	1	1	0.4980
DIVEARN	59,546	0.1900	0	0	0	0.2677	1	0.3196
DIVSALE	59,546	0.0100	0	0	0	0.0130	0.0565	0.0176
DIVCASH	59,546	0.0703	0	0	0	0.1255	0.3078	0.1105
L1.DIVEARN	59,546	0.1865	0	0	0	0.2590	1	0.3169
L1.DIVSALE	59,546	0.0099	0	0	0	0.0125	0.0570	0.0175
L1.DIVCASH	59,546	0.0695	0	0	0	0.1224	0.3078	0.1100
IO	59,546	0.5054	0.5367	0	0.2315	0.7734	0.9980	0.3089
CAPEX	59,546	0.0400	0.0123	0.0130	0	0.0345	0.1642	0.2013
LEV	59,546	0.2696	0.2149	0	0.1000	0.3776	1.0992	0.2172
SIZE	59,546	6.2922	6.1730	2.4893	4.7094	7.6983	11.9387	2.0577
RETA	59,546	−0.2990	0.1087	−8.2701	−0.1970	0.3289	0.8340	1.4556
ROA	59,546	0.0014	0.0517	−1.1280	−0.0037	0.0884	0.3226	0.1962
ROVOL	59,546	0.0761	0.0304	0.0013	0.0132	0.0770	1.0242	0.1340
AGE	59,546	2.9005	2.8911	1.1793	2.3034	3.4972	4.2671	0.7407
MB	59,546	1.8381	1.3949	0.4777	1.0742	2.0486	9.3986	1.3471
ANALYST	59,546	6.2054	3.9167	0	1	9.1667	28.4167	6.7492
Panel B: Summary statistics for dividend payout by industry
	Agriculture		Mining		Construction		Manufacturing		Transportation		Wholesale		Retail		Service
Variables	Mean	Median	Mean	Median	Mean	Median	Mean	Median	Mean	Median	Mean	Median	Mean	Median	Mean	Median
DIVEARN	0.2227	0.0000	0.1516	0.0000	0.1050	0.0000	0.1753	0.0000	0.3437	0.2087	0.1679	0.0000	0.1380	0.0000	0.1820	0.0000
DIVSALE	0.0115	0.0000	0.0089	0.0000	0.0023	0.0000	0.0083	0.0000	0.0218	0.0114	0.0046	0.0000	0.0046	0.0000	0.0110	0.0000
DIVCASH	0.0829	0.0000	0.0398	0.0000	0.0340	0.0000	0.0681	0.0000	0.1224	0.0644	0.0706	0.0000	0.0533	0.0000	0.0654	0.0000
L1.DIVEARN	0.2375	0.0000	0.1547	0.0000	0.1028	0.0000	0.1726	0.0000	0.3421	0.1984	0.1615	0.0000	0.1321	0.0000	0.1758	0.0000
L1.DIVSALE	0.0122	0.0000	0.0088	0.0000	0.0023	0.0000	0.0082	0.0000	0.0218	0.0108	0.0045	0.0000	0.0043	0.0000	0.0108	0.0000
L1.DIVCASH	0.0886	0.0000	0.0400	0.0000	0.0318	0.0000	0.0674	0.0000	0.1239	0.0702	0.0699	0.0000	0.0508	0.0000	0.0638	0.0000
Panel C: Summary statistics over the three types of business strategy with respect to dividend payout
	Defender firms (1)		Analyzer firms (2)		Prospector firms (3)
Variables	Mean	Median	Mean	Median	Mean	Median
DIVEARN	0.1451	0.0000	0.2070	0.0000	0.2958	0.1031
DIVSALE	0.0076	0.0000	0.0104	0.0000	0.0176	0.0061
DIVCASH	0.0552	0.0000	0.0756	0.0000	0.1072	0.0348
L1.DIVEARN	0.1424	0.0000	0.2037	0.0000	0.2885	0.0947
L1.DIVSALE	0.0074	0.0000	0.0103	0.0000	0.0175	0.0059
L1.DIVCASH	0.0541	0.0000	0.0754	0.0000	0.1054	0.0324

This table presents the descriptive statistics for our sample. A comprehensive description of the variables utilized in the regression analysis can be found in Appendix 1. Continuous variables are winsorized at the 1% and 99% levels.

Table 2. The impact of business strategy on dividend payments.

Variables	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)
PROSPECTOR	0.0151	0.0004	0.0031
	(8.04)***	(3.64)***	(5.05)***
L1.DIVEARN	0.6710
	(132.85)***
L1.DIVSALE		0.8652
		(224.19)***
L1.DIVCASH			0.8054
			(205.86)***
SIZE	0.0182	0.0003	0.0041
	(20.18)***	(11.95)***	(14.32)***
ROA	0.0981	0.0024	0.0134
	(21.21)***	(8.85)***	(10.38)***
LEV	−0.1232	−0.0031	−0.0252
	(-28.78)***	(-16.24)***	(-20.98)***
MB	−0.0001	0.0002	0.0024
	(-0.41)	(12.18)***	(10.00)***
CAPEX	−0.2310	−0.0292	−0.3422
	(-0.45)	(-1.23)	(-2.32)**
RETA	0.0031	0.0004	0.0012
	(4.64)***	(1.39)	(3.23)***
ROVOL	−0.0121	−0.0011	−0.0073
	(-1.88)*	(-2.71)***	(-4.83)***
AGE	0.0182	0.007	0.0060
	(13.00)***	(9.37)***	(14.32)***
IO	−0.0291	−0.0008	−0.0004
	(-7.74)***	(-8.53)***	(-8.10)***
ANALYST	−0.0010	0.0001	−0.0003
	(-6.04)***	(0.99)	(-1.01)
Constant	−0.0587	−0.0019	−0.0203
	(-2.41)**	(-3.86)***	(-3.29)***
Industry and Year effects	Yes	Yes	Yes
Adj R^2	0.5632	0.8551	0.8250
Obs	59,546	59,546	59,546

This table presents the results of the panel regression analyzing the relationship between dividend payout and corporate business strategy. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 3. Firm and year fixed effects.

Variables	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)
PROSPECTOR	0.0134	0.0002	0.0016
	(5.22)***	(2.97)***	(2.51)**
L1.DIVEARN	0.3623
	(47.26)***
L1.DIVSALE		0.5933
		(60.93)***
L1.DIVCASH			0.4995
			(60.43)***
SIZE	0.0347	0.0001	0.0009
	(12.90)***	(1.48)	(1.24)
ROA	0.0544	0.0009	0.0045
	(9.62)***	(3.53)***	(2.97)***
LEV	−0.2147	−0.0047	−0.0354
	(-27.36)***	(-16.24)***	(-17.80)***
MB	−0.006	0.0002	0.0006
	(-7.60)***	(6.23)***	(2.70)***
CAPEX	−1.3398	−0.0359	−0.6429
	(-1.74)*	(-0.95)	(-2.51)**
RETA	0.0067	0.0001	0.0012
	(5.53)***	(1.83)*	(3.71)***
ROVOL	−0.0104	−0.0008	−0.0081
	(-1.20)	(-2.17)**	(-3.60)***
AGE	0.0158	0.0003	0.0134
	(2.60)***	(1.50)	(8.32)***
IO	−0.0176	−0.0001	−0.0001
	(-2.36)**	(-0.44)	(-0.02)
ANALYST	0.0011	0.0001	0.0007
	(2.81)***	(7.21)***	(6.76)***
Constant	−0.0708	0.0027	−0.0043
	(-3.43)***	(3.61)***	(-0.79)
Firm and Year effects	Yes	Yes	Yes
Adj R^2	0.6595	0.8622	0.8034
Obs	59,546	59,546	59,546

This table presents the results of the panel regression analyzing the relationship between dividend payments and corporate business strategy while accounting for firm and year fixed effects. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 4. Subsample with prospector vs. defender firms.

Variables	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)
PROSPECTOR	0.0265	0.0007	0.0057
	(7.23)***	(5.54)***	(5.93)***
L1.DIVEARN	0.6795
	(102.74)***	0.8639
L1.DIVSALE		(173.78)***
			0.8025
L1.DIVCASH			(155.09)***
SIZE	0.015	0.0003	0.0033
	(12.85)***	(7.35)***	(10.13)***
ROA	0.0893	0.0021	0.0151
	(16.02)***	(7.83)***	(9.76)***
LEV	−0.1101	−0.0026	−0.0245
	(-19.79)***	(-10.68)***	(-14.70)***
MB	0.0001	0.0003	0.0016
	(0.08)	(9.21)***	(7.82)***
CAPEX	−0.2507	−0.0756	−0.8486
	(-0.29)	(-2.19)**	(-3.01)***
RETA	0.001	0.0001	0.0006
	(1.34)	(1.62)	(2.71)***
ROVOL	−0.0095	−0.0006	−0.0071
	(-1.22)	(-1.50)	(-3.54)***
AGE	0.0129	0.0004	0.0051
	(6.89)***	(5.70)***	(9.42)***
IO	−0.0335	−0.0011	−0.009
	(-6.72)***	(-5.72)***	(-6.47)***
ANALYST	−0.001	0.0001	0.0001
	(-3.41)***	(1.10)	(0.06)
Constant	0.0055	−0.0022	−0.0326
	(0.13)	(-1.93)*	(-2.81)***
Industry and Year effects	Yes	Yes	Yes
Adj R^2	0.5660	0.8101	0.7359
Obs	44,719	44,719	44,719

This table reports the panel regression of dividend payments on corporate business strategy using a subsample with prospector vs. defender firms. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 5. Endogeneity.

Variables	1st Stage	2nd Stage
		DIVEARN (1)	DIVSALE (2)	DIVCASH (3)
L1.PROSPECTOR	0.6981
	(202.57)***
Ex.PROSPECTOR		0.0171	0.0004	0.0039
		(5.92)***	(3.73)***	(5.15)***
L1.DIVEARN		0.669
		(125.92)***
L1.DIVSALE			0.8737
			(225.00)***
L1.DIVCASH				0.8104
				(200.68)***
SIZE	0.0216	0.0177	0.0004	0.0036
	(16.14)***	(18.35)***	(11.26)***	(13.20)***
ROA	−0.0301	0.1074	0.0021	0.0152
	(-2.42)**	(20.10)***	(8.93)***	(10.64)***
LEV	0.0287	−0.1277	−0.0027	−0.0258
	(3.84)***	(-27.37)***	(-14.60)***	(-19.26)***
MB	−0.0037	−0.0001	0.0004	0.002
	(-2.58)***	(-0.09)	(12.58)***	(10.66)***
CAPEX	22.6399	−0.2505	−0.0087	−0.2982
	(8.92)***	(-0.39)	(-0.34)	(-1.56)
RETA	0.0123	−0.0031	0.0001	0.0006
	(8.11)***	(-4.64)***	(1.99)**	(3.19)***
ROVOL	−0.0774	−0.0195	−0.0007	−0.0085
	(-4.64)***	(-2.50)**	(-2.06)**	(-4.50)***
AGE	0.0041	0.0194	0.0004	0.005
	(1.68)*	(11.64)***	(6.02)***	(11.20)***
IO	−0.028	−0.0318	−0.0013	−0.0088
	(-4.50)***	(-7.59)***	(-8.20)***	(-7.75)***
ANALYST	−0.0031	−0.0013	0.0001	−0.0001
	(-9.13)***	(-5.51)***	(0.58)	(-0.85)
Constant	0.1116	−0.0414	−0.0026	−0.0244
	(2.78)***	(-1.54)	(-3.67)***	(-3.48)***
Industry and Year effects	Yes	Yes	Yes	Yes
Adj R^2	0.5474	0.5582	0.8293	0.7493
Obs	51,771	51,771	51,771	51,771

This table reports the panel regression of dividend payments on corporate business strategy addressing endogeneity concern using 2SLS. Panel A presents the first stage, predicting corporate business strategy using prospector strategy as a dependent variable. We use the five-year lagged business strategy as an instrument in the first stage regression. Panel B presents the results on the impact of predicted prospector strategy on dividend. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 6. Propensity score matching analysis.

Panel A: Propensity score matching
	Treatment	Control	t-test
SIZE	6.4640	5.6674	1.57
ROA	0.0169	0.0183	1.07
LEV	0.2527	0.2962	1.26
MB	1.9503	1.7967	0.93
CAPEX	0.0312	0.0342	1.09
RETA	−0.0467	−0.0600	0.87
ROVOL	0.0657	0.0875	1.01
AGE	2.9184	2.6114	1.62
IO	0.5507	0.5027	1.51
ANALYST	6.6237	5.7775	1.22
Panel B: PSM Regression
Variables	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)
PROSPECTOR	0.0147	0.0003	0.0026
	(4.67)***	(2.98)***	(4.26)***
Constant	−0.0439	−0.0018	−0.0096
	(-1.47)	(-2.39)**	(-1.38)
Control variables	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes
Adj R^2	0.5599	0.8459	0.7445
Obs	22,417	22,417	22,417

This table reports the panel regression of dividend payments on corporate business strategy using the propensity score matching analysis. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 7. The effects of business strategy on corporate overinvestment and capital expenditure.

Variables	OVERINVEST (1)	CAPEX (2)
PROSPECTOR	0.0053	−0.0098
	(3.01)***	(-4.59)***
SIZE	0.0057	−0.0043
	(8.44)***	(-18.48)***
IO	−0.0097	0.0078
	(-2.77)***	(6.94)***
AGE	0.0020	−0.0017
	(1.56)	(-4.07)***
CASH	0.4427	0.0100
	(55.92)***	(4.09)***
MB	0.0053	0.0054
	(6.03)***	(19.53)***
LOSS	0.0156	−0.0078
	(6.91)***	(-10.41)***
ANALYST	−0.0003	0.0003
	(-1.82)*	(-5.68)***
CFVOL	−0.0139	0.0059
	(-3.58)***	(4.27)***
SALEVOL	−0.0167	−0.0046
	(-2.75)***	(-2.31)**
INVESTVOL	−0.0005	0.0001
	(-8.85)***	(2.13)**
Z-Score	0.0208	0.0017
	(27.20)***	(7.96)***
TANG	−0.0562	0.1544
	(-12.11)***	(87.64)***
CFO/SALE	0.0069	0.0406
	(0.99)	(14.39)***
DIVDUM	−0.0135	−0.0074
	(-7.21)***	(-12.17)***
CYCLE	0.0009	−0.0011
	(2.11)**	(-7.97)***
Constant	−2.8610	−3.5542
	(-3.69)***	(-0.65)
Industry & Year Effects	Yes	Yes
Adj R^2	0.3876	0.4678
Obs	41,874	41,874

This table reports the panel regression of the impact of business strategy on overinvestment and capital expenditure. Following Biddle et al. (2009), overinvestment is defined as a ranked variable based on the mean of a decile-based measure of cash and leverage. Consistent with Biddle et al. (2009), we multiply leverage by minus one before ranking so that variable is increasing in the tendency of overinvestment. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 8. Information environment, business strategy and dividend payments.

Variables	Panel A: Probability of insider trading (PIN)
	High PIN			Low PIN
	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)	DIVEARN (4)	DIVSALE (5)	DIVCASH (6)
PROSPECTOR	0.0224	0.0003	0.0023	0.0191	0.0002	0.0033
	(4.36)***	(1.84)*	(1.84)*	(3.02)***	(1.21)	(2.14)**
Constant	−0.1084	−0.0026	−0.0183	0.0108	−0.0015	0.0076
	(-2.13)**	(-1.32)	(-0.92)	(0.20)	(-0.61)	(0.55)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj R^2	0.4597	0.6997	0.6834	0.5064	0.8462	0.7633
Obs	7,787	7,787	7,787	6,630	6,630	6,630
Variables	Panel B: Financial statement opacity (OPAQUE)
	More OPAQUE			Less OPAQUE
	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)	DIVEARN (4)	DIVSALE (5)	DIVCASH (6)
PROSPECTOR	0.0128	0.0002	0.0016	0.0091	0.001	0.0008
	(4.57)***	(1.66)*	(1.90)*	(2.78)***	(0.08)	(1.12)
Constant	−0.0997	−0.0035	−0.0013	−0.0776	−0.0012	−0.0163
	(-2.08)**	(-2.03)**	(-0.07)	(-0.88)	(-1.05)	(-1.43)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj R^2	0.5596	0.8594	0.7755	0.4992	0.7473	0.7071
Obs	27,004	27,004	27,004	27,650	27,650	27,650

This table reports the results on how the degree of information asymmetry affects the relationship between corporate business strategy and dividend payments. We use the probability of insider trading and financial statement opacity as proxies for information asymmetry. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 9. Corporate Governance, business strategy and dividend payments.

Variables	Takeover susceptibility index (TSIND)
	Low TSIND			High TSIND
	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)	DIVEARN (4)	DIVSALE (5)	DIVCASH (6)
PROSPECTOR	0.0160	0.0002	0.0024	0.0195	0.0002	0.0034
	(5.78)***	(2.65)***	(3.62)***	(3.08)***	(1.23)	(2.18)**
Constant	−0.0165	−0.0024	−0.0191	−0.0110	−0.0015	−0.0078
	(-0.41)	(-2.37)**	(-2.05)**	(-0.20)	(-0.62)	(-0.56)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj R^2	0.5320	0.8651	0.7746	0.5165	0.8431	0.7686
Obs	10,331	10,331	10,331	10,333	10,333	10,333

This table reports the results on how governance mechanisms affect the relationship between corporate business strategy and dividend payments. The takeover susceptibility index is used as a proxy for governance measure. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table 10. Policy uncertainty, business strategy and dividend payments.

Variables	Panel A: Economic policy uncertainty (EPU)
	High EPU			Low EPU
	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)	DIVEARN (4)	DIVSALE (5)	DIVCASH (6)
PROSPECTOR	0.0152	0.0003	0.0028	0.0126	0.0002	0.0026
	(5.64)***	(3.42)***	(4.07)***	(3.64)***	(2.52)**	(3.63)***
Constant	0.0348	−0.0031	−0.0152	−0.0616	−0.0007	−0.0094
	(0.79)	(-2.04)**	(-1.31)	(-2.02)**	(-0.93)	(-1.22)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj R^2	0.5608	0.8553	0.7749	0.5529	0.7626	0.6984
Obs	21,159	21,159	21,159	23,387	23,387	23,387
	Panel B: Presidential elections (ELECT)
Variables	ELECT			Non-ELECT
	DIVEARN (1)	DIVSALE (2)	DIVCASH (3)	DIVEARN (4)	DIVSALE (5)	DIVCASH (6)
PROSPECTOR	0.0134	0.0002	0.0014	0.0093	0.001	0.0013
	(4.71)***	(1.79)*	(1.91)*	(2.88)***	(0.04)	(1.53)
Constant	−0.0321	−0.0009	−0.0128	−0.15	−0.0036	−0.0108
	(-0.54)	(-0.70)	(-1.26)	(-2.74)***	(-2.68)***	(-0.71)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry and Year effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj R^2	0.5042	0.86	0.7221	0.5732	0.7689	0.7797
Obs	15,072	15,072	15,072	44,474	44,474	44,474

This table reports the results on how U.S. policy uncertainty affects the relationship between corporate business strategy and dividend payments. Economic policy uncertainty and U.S. Presidential elections are used as proxies for U.S. policy uncertainty. The t-statistics are presented in parentheses. Superscripts *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

Table

Variables	Acronym	Description	Data sources
1. Dependent variables
Dividend payment	DIVEARN	Dividend Payout Ratio. We calculate DIVEARN as the ratio of total dividend paid divided by accounting earnings before extraordinary items (IB). Following Holmen et al. (2008), if the dividend paid when accounting earning is negative, we replace it with one, and if DIVEARN is greater than one, it will also be replaced with one.	Compustat
	DIVSALE	We calculate DIVSALE as the ratio of total dividend paid divided by sale times 100.	Compustat
	DIVCASH	We calculate DIVCASH as the ratio of total dividend paid divided by cash flow times 100.	Compustat
	DIVDUM	It is a dummy variable, which takes the value of one if firms pay dividends and zero otherwise.	Compustat
2. Firm-level variables
Prospector strategy	PROSPECTOR	A dummy variable which takes the value of one for prospector-type firms and zero for defender firms and analyzer firms (seeAppendix 2for further details).	Compustat
Firm size	SIZE	The logarithm of total assets.	Compustat
Market to book	MB	We calculate it as a ratio of the market value of total asset to the book value of total assets (AT). The market value of total asset is equal to total assets (AT) plus the market value of common equity (CSHO x PRCC_F) minus total equity (CEQ).
Leverage	LEV	The ratio of long-term debt (DLTT) to total assets (AT).	Compustat
Operating performance	ROA	We calculate it as the ratio of income before extraordinary items (IB) plus interest expense (XINT) plus deferred taxes (TXDI) to total assets (AT).	Compustat
Capital expenditure	CAPEX	This variable is defined as the total of capital expenditure (CAPEX) divided by total assets (AT) at the end of year.	Compustat
Investment	INVESTMENT	Total investment expenditure, measured by the total of research and development expenditure (XRD), capital expenditure (CAPEX), and acquisition expenditure (AQC), minus cash received from sale of property, plant and equipment (SPPE), multiplied by 100, and divided by lagged total assets (AT).	Biddle et al. (2009)
Overinvestment	OVERINV	A ranked variable based on the mean of a decile measure of cash and leverage. Consistent with previous literature (Biddle et al., 2009), we multiply leverage by minus one before ranking so that both variables are increasing in the tendency of over-investment.	Compustat
Firm age	AGE	Natural log of how long the firms have been registered at Compustat since 1950.	Compustat
Retained earnings	RETA	Retained earnings (RE) to total assets (AT).	DeAngelo et al. (2006)
The deviation of ROA	ROVOL	A standard deviation of ROA for the previous 12 quarters	Kwak et al. (2012)
Probability of insider trading	PIN	Probability of insider trading obtained from http://scholar.rhsmith.umd.edu/sbrown/pin-data	Data: Stephen Brown
Financial statement opacity	OPAQUE	The moving sum of the absolute value of discretionary accruals over the three-year period from t - 1 to t - 3, where discretionary accruals are calculated based on the modified Jones model (Dechow et al. (1995)).	Compustat
Institutional ownership	IO	The average ownership proportion of institutional investors over the four quarters of the firm’s financial year t. We obtain this data from the Thomson Reuters Institutional Holdings (13 F) database.	13F
Analyst coverage	ANALYST	The number of analysts following for year t.	I/B/E/S
Takeover susceptibility index	TSIND	The index measures the likelihood of a hostile acquisition, in which a higher takeover susceptibility index suggests a greater probability of being taken over and, therefore, indicates weaker protections for corporate governance.	Cain et al. (2017)
Economic policy uncertainty	EPU	The monthly economic policy uncertainty index compiled by Baker et al. (2016).	Policy Uncertainty
Presidential elections	ELECT	A dummy variable taking the value 1 if the USA holds a presidential election in year t and zero otherwise.	Database of Political Institutions
Operating net cash flow/Sale	CFO/SALE	Operating net cash flow (OANCF) divided by total sales (SALE)	Compustat
Operating cycle	CYCLE	the natural log of receivables to sales (RECT/SALE) plus inventory to COGS (INVT/COGS) multiplied by 360	Compustat
Loss firm	LOSS	A dummy variable with a value of one indicating if net income before extraordinary items is negative, and zero otherwise.	Compustat
Tangibility	TANG	Ratio of Plant Property and Equipment (PPE) to total assets (AT).	Compustat
Cash flow volatility	CFVOL	Standard deviation from year t-5 to year t-1 of cash flow from operation (OANCF) divided by total assets (AT).	Compustat
Investment volatility	INVESTVOL	Standard deviation from year t-5 to year t-1 of Investment (INVESTMENT).	Compustat
Sale volatility	SALEVOL	Standard deviation from year t-5 to year t-1 of sales (SALE) divided by total assets (AT).	Compustat
Financial distress	Z-Score	A measure of distress is computed following the methodology in Altman (1968).	Compustat

Table

Business strategy composite measure	Variable measurement
1). RDS5 (Research and Development to Sales Ratio) Purpose: Reflects the company’s drive to innovate and explore new products.	The ratio of research and development expenses to sales, calculated as an average over the past five years.
2). EMPS5 (Employee to Sales Ratio) Purpose: Measures the company’s capacity to efficiently produce and distribute products and services.	The ratio of the total number of employees to sales, computed as an average over the preceding five years.
3). REV5 (Change in Total Revenue) Purpose: Indicates the company’s historical growth trajectory and investment potential.	The one-year percentage change in total sales, calculated as an average over the past five years.
4). SGA5 (Marketing to Sales Ratio) Purpose: Demonstrates the company’s emphasis on promoting new products and services.	The ratio of selling, general, and administrative expenses to sales, determined as an average over the previous five years.
5). EMP5 (Employee Fluctuations) Purpose: Measures the company’s stability in terms of organizational workforce changes.	The standard deviation of the total number of employees, calculated over a five-year period.
6). CAP5 (Capital Intensity) Purpose: Reflects the company’s dedication to technological efficiency and resource allocation.	Capital intensity, measured as net property, plant, and equipment (PPE) scaled by total assets, averaged over the previous five years.

In accordance with the methodology outlined by Ittner et al. (1997) and the framework proposed by Miles and Snow (1978, 2003), we construct a discrete composite measure for business strategy, capturing the essence of a firm’s strategic approach.

Table

Variables		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
DIVEARN	1	1.00
DIVSALE	2	0.89	1.00
DIVCASH	3	0.88	0.95	1.00
L1.DIVEARN	4	0.89	0.95	0.92	1.00
L1.DIVSALE	5	0.84	0.92	0.88	0.89	1.00
L1.DIVCASH	6	0.84	0.89	0.89	0.88	0.95	1.00
PROSPECTOR	7	0.16	0.16	0.15	0.16	0.17	0.16	1.00
SIZE	8	0.17	0.17	0.16	0.16	0.15	0.15	0.01	1.00
ROA	9	−0.36	−0.38	−0.35	−0.36	−0.38	−0.35	−0.01	−0.45	1.00
LEV	10	−0.16	−0.15	−0.16	−0.13	−0.13	−0.14	0.10	0.06	−0.12	1.00
MB	11	0.43	0.44	0.42	0.42	0.43	0.42	0.17	0.60	−0.82	0.21	1.00
CAPEX	12	0.40	0.40	0.41	0.38	0.39	0.40	0.14	0.28	−0.18	−0.24	0.31	1.00
RETA	13	0.27	0.29	0.29	0.21	0.27	0.27	0.09	0.22	−0.12	−0.02	0.25	0.51	1.00
ROVOL	14	−0.38	−0.38	−0.39	−0.35	−0.37	−0.38	−0.16	−0.24	0.34	−0.02	−0.43	−0.45	−0.37	1.00
AGE	15	0.41	0.41	0.42	0.41	0.42	0.42	0.10	0.15	−0.26	−0.03	0.33	0.36	0.18	−0.26	1.00
IO	16	−0.01	0.04	0.04	−0.02	0.02	0.02	−0.13	0.19	0.07	−0.13	0.00	0.01	0.27	0.08	−0.05	1.00
ANALYST	17	0.25	0.27	0.26	0.24	0.26	0.25	0.05	0.65	−0.54	0.09	0.74	0.23	0.22	−0.25	0.12	0.28	1.00

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.