Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

Ho, Chi-Ming

doi:10.1186/s40854-022-00403-z

Financial Innovation

Table 6 Robustness analysis

From: Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

Depend Variable	Model 6		Model 7		Model 8		Model 9		Model 10
\(({R}_{i,t}-{R}_{f,t})\)	Coefficient	t-statistic	Coefficient	t-statistic	Coefficient	t-statistic	Coefficient	t-statistic	Coefficient	t-statistic
Intercept	− 0.01913***	− 2.767963	− 0.00820***	− 8.367746	− 0.07094***	− 7.366044	− 0.00250***	− 3.723975	− 0.070238	− 1.172472
(\({R}_{m}-{R}_{f}\))	1.22E − 05***	11.86222	0.85474***	35.03743	1.32E − 06	0.265783	0.86485***	35.46774	1.96E − 06	0.523152
BMR	− 0.11250***	− 18.88183	0.00017	1.500401	− 0.03253***	− 10.60821	− 7.80E − 05	− 0.684450	− 0.032641	− 0.880640
Asset	3.09E − 06***	3.659897	0.00065***	7.993936	− 4.48E − 06	− 0.532548
Size							5.65E − 09	0.466802	− 0.000282	− 0.066990
D			0.00763***	5.713188	0.18520***	4.113677	0.00124	1.621585	0.105553	1.076105
\({\sigma }_{i,AI,t-1}^{2}\)	8.90E − 13***	4.252773	− 1.15E − 07***	− 3.239342	5.08834***	2.238173	− 1.14E − 07***	− 3.209619	2.13E − 07	0.816159
\({\sigma }_{i,Fin-Tech,t-1}^{2}\)	1.38E − 13**	2.097997	6.51E − 08***	3.796569	− 5.08850***	− 2.237765	6.74E − 08***	3.933979	− 5.92E − 06	− 0.816184
D*(\({R}_{m}-{R}_{f}\))			0.026691	0.916722	1.00311**	380.3264	0.01470	0.505392	1.002839	440.6452
D*BMR			− 0.000456***	− 3.358283	0.02790**	5.500631	− 0.00017	− 1.372978	0.031740	0.885858
D*Asset			− 0.000701***	− 5.542301	− 0.00191	− 0.470347	8.75E − 09	0.712404	0.007333	0.762140
D*\(({\sigma }_{i,AI,t-1}^{2}\))			1.15E − 07***	3.239344	1.49E − 06	0.459562	1.14E − 07***	3.209628	1.34E − 06**	2.227196
D*(\({\sigma }_{i,Fin-Tech,t-1}^{2}\))			− 6.51E − 08***	− 3.796561	6.50E − 06**	2.526280	− 6.74E − 08***	− 3.933967	6.82E − 06	0.908963
\({\sigma }_{i,AI,t}^{2}\)							− 5.38E − 13	− 0.683252	1.52E − 07	0.778654
\({\sigma }_{i,Fin-Tech}^{2}\)							− 3.05E − 14	− 0.065451	− 4.22E − 06	− 0.778672
N	58,589		46,690		11,899		46,690		11,899
Adj. R	0.046774		0.101584		0.969655		0.101229		0.969712
F − statistic	575.9601***		482.8721***		322.6276***		407.1542***		322.7338***

Robustness models: \({R}_{i,t}-{R}_{f,t}={{\alpha }_{0s{\left(ns\right)}_{pre}-covid}+(\alpha }_{0s{\left(ns\right)}_{covid}}-{\alpha }_{0s{\left(ns\right)}_{pre}-covid})D+{\beta }_{1s{\left(ns\right)}_{pre}-covid}{(R}_{m,t}-{R}_{f,t})+({\beta }_{1s{\left(ns\right)}_{covid}}-{\beta }_{1s{\left(ns\right)}_{pre}-covid})D{(R}_{m,t}-{R}_{f,t})+{\beta }_{2s{\left(ns\right)}_{pre}-covid}ln{\left(ASSET, SIZE\right)}_{i,t}+({\beta }_{2s{\left(ns\right)}_{covidS}}-{\beta }_{2s{\left(ns\right)}_{pre}-covid})Dln{\left(SIZE\right)}_{i,t}+{\beta }_{3s{\left(ns\right)}_{pre}-covid}{\left(BMR\right)}_{i,t}+({\beta }_{3s{\left(ns\right)}_{covid}}-{\beta }_{3s{\left(ns\right)}_{pre}-covid}){D\left(BMR\right)}_{i,t}+{\beta }_{4s{\left(ns\right)}_{pre}-covid}{\left({\sigma }_{i,AI}^{2}\right)}_{t-1}+({\beta }_{4s{\left(ns\right)}_{covid}}-{\beta }_{4s{\left(ns\right)}_{pre}-covid})D{\left({\sigma }_{i,AI}^{2}\right)}_{t-1}+{\beta }_{5s{\left(ns\right)}_{pre}-covid}{\left({\sigma }_{i,Fin-Tech}^{2}\right)}_{t-1}+({\beta }_{5s{\left(ns\right)}_{covid}}-{\beta }_{5s{\left(ns\right)}_{pre}-covid})D{\left({\sigma }_{i,Fin-Tech}^{2}\right)}_{t-1}+{\beta }_{6s(ns)}\left({\sigma }_{i,AI,t}^{2}\right)+{\beta }_{7s(ns)}\left({\sigma }_{i,Fin-Tech,t}^{2}\right)+{\varepsilon }_{i,t}\), \({\varepsilon }_{i,t}\)=\({\mu }_{i}+{\lambda }_{i}+{\nu }_{i,t}\) (p < 0.01); Model 6 examines H1, Model 7 and 9 examine H2, and Model 8 and 10 examine H3; each model lists the coefficients and t-values of period random effects; *** (P < 0.01), and ** (P < 0.05) are statistically significant; the significance of the main variables is shown in bold

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