Relationship between search volume for digital transformation and stock returns: an empirical study in Vietnam

Pham, Tien Phat; Nguyen, Duc Ngoc; Tran, Tri Ba

doi:10.1108/RAUSP-10-2023-0214

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Research Article • RAUSP 61 • 2026 • https://doi.org/10.1108/RAUSP-10-2023-0214 linkcopy

Relationship between search volume for digital transformation and stock returns: an empirical study in Vietnam

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Purpose This study aims to examine the relationship between digital transformation search volume and stock returns in the Vietnamese stock market.

Design/methodology/approach The authors collected weekly data from Google Trends and vn.investing.com, spanning from week 33 of 2019 to week 32 of 2023. Using this data set, the authors used various quantitative approaches, including VAR-Granger, Ordinary Least Squares (OLS) and Copula, to test the relationships between variables.

Findings The results obtained from VAR-Granger analysis reveal a unidirectional causality from digital transformation search volume to the stock returns of VN-Index, VN-30 and VN-100. Findings from the OLS indicate a negative lagged impact of search volume on digital transformation for stock returns. Moreover, using the Copula approach, the authors determine that the structural dependency between the search volume for digital transformation and the VN-Index follows a normal distribution. This suggests that simultaneous positive and negative changes between the variables are equally likely to occur.

Research limitations/implications The study is meaningful further research.

Practical implications The study is meaningful for stakeholders: investors and policymakers.

Originality/value By offering these insights, this paper contributes to a deeper understanding of the relationship between digital transformation and firm performance within the stock exchange market.

Digital transformation; Stock return; Google search; Vietnam

Variable	Obs.	Mean	SD	Min	Max	Dickey-Fuller	Phillips-Perron
Panel 1 - 1. Descriptive statistics and stationary test
digital^GSVI	209	0.8934456	1.048732	−2.236945	6.389808	−8.356***	−8.562***
return^VNIndex	209	0.0009842	0.0267742	−0.1025858	0.1027658	−10.203***	−10.013***
return^VN ³⁰	209	0.0015518	0.0277948	−0.1057483	0.1014413	−10.333***	−10.170***
return^VN ¹⁰⁰	209	0.0015375	0.0280615	−0.1006965	0.1004145	−10.199***	−10.026***

Lag/Rank	FPE	AIC	HQIC	SBIC	LL	Eigenvalue	Trace statistic	5% critical value
Panel 1–2. Optimal lag selection test and co-integration test
Model 1: return^VNIndex = f(digital^GSVI)
0	0.000804	−1.45052	−1.43741	−1.4181	153.41551		101.3143	15.41
1	0.000544	−1.83993	−1.80059*	−1.74267*	188.03613	0.28430	32.0731	3.76
2	0.000534*	−1.85918*	−1.79361	−1.69708	204.07266	0.14354
3	0.000545	−1.83917	−1.74738	−1.61223
Model 2: return^VN³⁰ = f(digital^GSVI)
0	0.000869	−1.37205	−1.35894	−1.33963	143.51846		101.3389	15.41
1	0.000595	−1.75071	−1.71137*	−1.65345*	178.00572	0.28338	32.3643	3.76
2	0.000588*	−1.76279*	−1.69723	−1.6007	194.18788	0.14474
3	0.0006	−1.74222	−1.65043	−1.51529
Model 3: return^VN¹⁰⁰ = f(digital^GSVI)
0	0.000886	−1.35264	−1.33952	−1.32022	143.51512		101.6002	15.41
1	0.000598	−1.74545	−1.70611*	−1.64819*	178.2343	0.28498	32.1618	3.76
2	0.000587*	−1.76393*	−1.69836	−1.60183	194.31521	0.14390
3	0.000603	−1.73714	−1.64535	−1.5102

Variable	Return^vnindex	Digital^gsvi	All
Panel 2 - 1. VAR-Granger estimation
return^VNIndex	–	10.182***	10.182***
digital^GSVI	1.9011	–	1.9011
Variable	return^VN ³⁰	digital^GSVI	All
return^VN ³⁰	–	9.3026***	9.3026***
digital^GSVI	1.4789	–	1.4789
Variable	return^VN ¹⁰⁰	digital^GSVI	All
return^VN ¹⁰⁰	–	11.051***	11.051***
digital^GSVI	1.8535	–	1.8535

Variable	Return^vnindex		Return^vn ³⁰		Return^vn ¹⁰⁰
Variable	Coeficient	t-statistic	Coeficient	t-statistic	Coeficient	t-statistic
Panel 2 - 2. OLS estimation
return_t _-1	0.3526431***	5.02	0.3309948***	4.71	0.3509134***	5.00
return_t _-2	−0.0760245	−1.11	−0.0627852	−0.91	−0.0758118	−1.10
digital^GSVI	−0.0018637	−0.96	−0.0015548	−0.76	−0.0015325	−0.75
$d i g i t a l_{t - 1}^{G S V I}$	−0.0052159**	−2.53	−0.0052877**	−2.45	−0.005878***	−2.72
$d i g i t a l_{t - 2}^{G S V I}$	0.0028854	1.46	0.0023829	1.15	0.002905	1.40
Cons.	0.0045067*	1.77	0.0050915*	1.90	0.0051313*	1.92
N	207		207		207
F-value	7.76***		6.93***		7.83***
R-Square	0.1618		0.1471		0.1630

	Clayton	Gumbel	Gaussian
Panel 2–3. Estimated parameters of the pair variables of digital^GSVI and return^VNIndex
Parameter	−0.06499	1	−0.086†
Loglikeihood	0.4553	−2.277e-07	0.7092

Variable	Obs.	Mean	SD	Min	Max	Dickey-Fuller	Phillips-Perron
Panel 3–1. Descriptive statistics and stationary test
digital^GSVI	209	0.0013402	0.0266363	−0.0974993	0.1082318	−10.293***	−10.098***
return_no_log^VNIndex	209	0.0019366	0.0276952	−0.100349	0.1067649	−10.420***	−10.252***
return_no_log^VN³⁰	209	0.0019294	0.027929	−0.0957926	0.1056291	−10.278***	−10.099***
return_no_log^VN¹⁰⁰	209	0.0013402	0.0266363	−0.0974993	0.1082318	−10.293***	−10.098***

Lag/Rank	FPE	AIC	HQIC	SBIC	LL	Eigenvalue	Trace statistic	5% critical value
Panel 3–2. Optimal lag selection test and co-integration test
Model 1: return_no_log^VNIndex = f(digital^GSVI)
0	0.000796	−1.4603	−1.44719	−1.42788	153.14709		102.5840	15.41
1	0.000543	−1.84355	−1.80421*	−1.74629*	188.39081	0.28860	32.0965	3.76
2	0.000532*	−1.86276*	−1.79719	−1.70066	204.43907	0.14363
3	0.000543	−1.84325	−1.75146	−1.61631
Model 2: return_no_log^VN³⁰= f(digital^GSVI)
0	0.000864	−1.37856	−1.36544	−1.34614	142.94759		102.5878	15.41
1	0.000595	−1.7513	−1.71196*	−1.65404*	178.05028	0.28763	32.3824	3.76
2	0.000588*	−1.76334*	−1.69777	−1.60124	194.24148	0.14481
3	0.0006	−1.74311	−1.65132	−1.51617
Model 3: return_no_log^VN¹⁰⁰ = f(digital^GSVI)
0	0.000878	−1.36155	−1.34843	−1.32913	143.21048		102.8697	15.41
1	0.000597	−1.74862	−1.70928*	−1.65136*	178.55324	0.28928	32.1841	3.76
2	0.000586*	−1.76715*	−1.70159	−1.60505	194.6453	0.14399
3	0.000601	−1.74088	−1.64909	−1.51395

Variable	return_no_log^VNIndex	digital^GSVI	digital^GSVI
Panel 4–1. VAR-Granger estimation
return_no_log^VNIndex	–	9.901***	9.901***
digital^GSVI	1.8498	–	1.8498
Variable	return_no_log^VN³⁰	digital^GSVI	digital^GSVI
return_no_log^VN³⁰	–	8.9725**	8.9725**
digital^GSVI	1.4135	–	1.4135
Variable	return_no_log^VN¹⁰⁰	digital^GSVI	All
return_no_log^VN¹⁰⁰	–	10.704***	10.704***
digital^GSVI	1.7764	–	1.7764

Variable	Return_no_log^VNIndex		Return_no_log^VN³⁰		Return_no_log^VN¹⁰⁰
Variable	Coeficient	t-statistic	Coeficient	t-statistic	Coeficient	t-statistic
Panel 4–2. OLS estimation
return_no_log_t _-1	0.3453624***	4.92	0.3243869***	4.62	0.3449814***	4.92
return_no_log_t _-2	−0.0808398	−1.18	−0.0680587	−0.99	−0.0815003	−1.19
digital^GSVI	−0.0017943	−0.93	−0.0014848	−0.73	−0.0014667	−0.72
$d i g i t a l_{t - 1}^{G S V I}$	−0.0051437**	−2.50	−0.0051968**	−2.41	−0.0057832***	−2.68
$d i g i t a l_{t - 2}^{G S V I}$	0.0027792	1.41	0.0022742	1.10	0.0027932	1.35
Cons.	0.0047502*	1.86	0.0053509*	2.00	0.0053957*	2.01
N	207		207		207
F-value	7.44***		6.63***		7.55***
R-Square	0.1562		0.1416		0.1580

Variable	Clayton	Gumbel	Gaussian
Panel 4–3. Estimated parameters of the pair variables of digital^GSVI and return_no_log^VNIndex
Parameter	−0.06499	1	−0.086*
Loglikeihood	0.4553	−2.277e-07	0.7092

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[tbl1fn1] Note(s): * is the suggestion of lag-order selection; *** indicates that the variable is stationary at significance levels of 1%

[tbl1fn2] Source(s): the authors