Relationship between search volume for digital transformation and stock returns: an empirical study in Vietnam
DOI:
https://doi.org/10.1108/RAUSP-10-2023-0214Palabras clave:
Digital transformation, Stock return, Google search, VietnamResumen
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, covering the period from week 33 of 2019 to week 32 of 2023. Using this dataset, the authors applied various quantitative methods, including VAR-Granger causality analysis, Ordinary Least Squares (OLS) regression and Copula analysis, to examine the relationships between the variables.
Findings – The VAR-Granger results reveal a unidirectional causal relationship from digital transformation search volume to the stock returns of the VN-Index, VN-30 and VN-100. The OLS results indicate a negative lagged effect of digital transformation search volume on stock returns. Furthermore, the Copula analysis shows that the dependency structure between digital transformation search volume and the VN-Index follows a normal distribution, suggesting that simultaneous positive and negative changes in the variables are equally likely to occur.
Research limitations/implications – The findings provide a foundation for future research on the relationship between digital transformation and stock market performance.
Practical implications – The study offers valuable insights for key stakeholders, including investors and policymakers.
Originality/value – By providing evidence from the Vietnamese stock market, this study contributes to a deeper understanding of the relationship between digital transformation and stock market performance.
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Referencias
Andrei, D., & Hasler, M. (2015). Investor Attention and Stock Market Volatility. Review of Financial Studies,28(1), 33–72. https://doi.org/10.1093/rfs/hhu059
Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3), 239–264. https://doi.org/10.1007/s11408-011-0165-y
Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics,49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0
Bijl, L., Kringhaug, G., Molnár, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 45, 150–156.https://doi.org/10.1016/j.irfa.2016.03.015
Da, Z., Engelberg, J., & Gao, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461–1499.https://doi.org/10.1111/j.1540-6261.2011.01679.x
Desagre, C., & D’Hondt, C. (2021). Googlization and retail trading activity. Journal of Behavioral and Experimental Finance, 29, 1–14.https://doi.org/10.1016/j.jbef.2020.100453
Ding, D., Guan, C., Chan, C. M. L., & Liu, W. (2020). Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic.Frontiers of Business Research in China, 14(1), 1–21.https://doi.org/10.1186/s11782-020-00089-z
Do, T. D., Pham, H. A. T., Thalassinos, E. I., & Le, H. A. (2022). The Impact of Digital Transformation on Performance: Evidence from Vietnamese Commercial Banks. Journal of Risk and Financial Management, 15(21), 1–15. https://doi.org/10.3390/jrfm15010021
Ekinci, C., & Bulut, A. E. (2021). Google search and stock returns: A study on BIST 100 stocks. Global Finance Journal, 47(March), 1–13. https://doi.org/10.1016/j.gfj.2020.100518
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
Fan, M.-H., Chen, M.-Y., & Liao, E.-C. (2021). A deep learning approach for financial market prediction: utilization of Google trends and keywords. Granular Computing, 6(1), 207–216. https://doi.org/10.1007/s41066-019-00181-7
Gaglio, C., Kraemer-Mbula, E., & Lorenz, E. (2022). The effects of digital transformation on innovation and productivity: Firm-level evidence of South African manufacturing micro and small enterprises. Technological Forecasting and Social Change, 182(May), 121785.https://doi.org/10.1016/j.techfore.2022.121785
Ghi, T., Thu, N., Huan, N., & Trung, N. (2022). Human capital, digital transformation, and firm performance of startups in Vietnam. Management, 26(1), 1–18.https://doi.org/10.2478/manment-2019-0081
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica,37(3), 424–438.
Grant, D., & Yeo, B. (2022). Resource-based view of the Productivity Paradox. Technology Analysis & Strategic Management, 1–16. https://doi.org/10.1080/09537325.2022.2042509
Guo, L., & Xu, L. (2021). The Effects of Digital Transformation on Firm Performance: Evidence from China’s Manufacturing Sector. Sustainability, 13(22), 12844.https://doi.org/10.3390/su132212844
Huang, M. Y., Rojas, R. R., & Convery, P. D. (2020). Forecasting stock market movements using Google Trend searches. Empirical Economics, 59(6), 2821–2839.https://doi.org/10.1007/s00181-019-01725-1
Ivanov, V., & Killian, L. (2001). A Practitioner’s guide to Lag-order selection for vector autoregressions. In Centre for Economic Policy Research.https://repec.cepr.org/repec/cpr/ceprdp/Dp2685.pdf
Jardak, M. K., & Ben Hamad, S. (2022). The effect of digital transformation on firm performance: evidence from Swedish listed companies. The Journal of Risk Finance, 23(4), 329–348.https://doi.org/10.1108/JRF-12-2021-0199
Jedynak, M., Czakon, W., Kuźniarska, A., & Mania, K. (2021). Digital transformation of organizations: what do we know and where to go next? Journal of Organizational Change Management, 34(3), 629–652. https://doi.org/10.1108/JOCM-10-2020-0336
Johansen, S. (1988). Statistical analysis of co-integration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220.https://doi.org/10.1016/j.frl.2018.05.003
Li, M., & Guo, X. (2022). Research on the Productivity Paradox of Information Technology. Proceedings of Business and Economic Studies, 5(2), 19–27.https://doi.org/10.26689/pbes.v5i2.3821
Li, Y., Goodell, J. W., & Shen, D. (2021). Comparing search-engine and social-media attentions in finance research: Evidence from cryptocurrencies. International Review of Economics & Finance, 75, 723–746.https://doi.org/10.1016/j.iref.2021.05.003
Lin, W. T., & Shao, B. B. M. (2006). The business value of information technology and inputs substitution: The productivity paradox revisited. Decision Support Systems, 42(2), 493–507. https://doi.org/10.1016/j.dss.2005.10.011
Lütkepohl, H. (2005). New introduction to multiple time series analysis. In Springer Science & Business Media. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-27752-1
Lyu, Y., Wang, W., Wu, Y., & Zhang, J. (2023). How does digital economy affect green total factor productivity? Evidence from China. Science of The Total Environment, 857, 159428. https://doi.org/10.1016/j.scitotenv.2022.159428
Masoud, R., & Basahel, S. (2023). The Effects of Digital Transformation on Firm Performance: The Role of Customer Experience and IT Innovation. Digital, 3(2), 109–126.https://doi.org/10.3390/digital3020008
McGinn, T., Taylor, B., McColgan, M., & McQuilkan, J. (2016). Social Work Literature Searching. Research on Social Work Practice, 26(3), 266–277.https://doi.org/10.1177/1049731514549423
Mellon, J. (2014). Internet Search Data and Issue Salience: The Properties of Google Trends as a Measure of Issue Salience.Journal of Elections, Public Opinion and Parties, 24(1), 45–72.https://doi.org/10.1080/17457289.2013.846346
Nasir, M. A., Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), 1–13.https://doi.org/10.1186/s40854-018-0119-8
Nguyen, T. X. H., & Nguyen, T. T. (2021). A model for assessing the digital transformation readiness for Vietnamese SMEs. Journal of Eastern European and Central Asian Research,8(4), 541–555. https://doi.org/10.15549/jeecar.v8i4.848
Patton, A. J. (2012). A review of Copula models for economic time series. Journal of Multivariate Analysis, 110, 4–18. https://doi.org/10.1016/j.jmva.2012.02.021
Peng, Y., & Tao, C. (2022). Can digital transformation promote enterprise performance? —From the perspective of public policy and innovation. Journal of Innovation and Knowledge, 7(3), 100198. https://doi.org/10.1016/j.jik.2022.100198
Pereira, E. J. de A. L., Silva, M. F. da, Lima, I. C. da C., & Pereira, H. B. B. (2018). Trump’s Effect on stock markets: A multiscale approach. Physica A: Statistical Mechanics and Its Applications, 512, 241–247.https://doi.org/10.1016/j.physa.2018.08.069
Phan, P., Zhou, J., & Abrahamson, E. (2010). Creativity, Innovation, and Entrepreneurship in China. Management and Organization Review, 6(2), 175–194. https://doi.org/10.1111/j.1740-8784.2010.00181.x
Plekhanov, D., Franke, H., & Netland, T. H. (2023). Digital transformation: A review and research agenda. European Management Journal, 41(6), 821–844.https://doi.org/10.1016/j.emj.2022.09.007
Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying Trading Behavior in Financial Markets Using Google Trends. Scientific Reports, 3(1), 1684.https://doi.org/10.1038/srep01684
Ren, C., Lee, S. J., & Hu, C. (2023). Digitalization Improves Enterprise Performance: New Evidence by Text Analysis. SAGE Open, 13(2), 1–10. https://doi.org/10.1177/21582440231175871
Ribeiro-Navarrete, S., Botella-Carrubi, D., Palacios-Marqués, D., & Orero-Blat, M. (2021). The effect of digitalization on business performance: An applied study of KIBS. Journal of Business Research, 126(July 2020), 319–326. https://doi.org/10.1016/j.jbusres.2020.12.065
Scharkow, M., & Vogelgesang, J. (2011). Measuring the Public Agenda using Search Engine Queries. International Journal of Public Opinion Research, 23(1), 104–113.https://doi.org/10.1093/ijpor/edq048
Smales, L. A. (2021). Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73, 1–14.https://doi.org/10.1016/j.irfa.2020.101616
Solow, R. M. (1987). We’d better watch out. In New York Times Book Review. Sociology.http://ci.nii.ac.jp/naid/10007455139/en/
Swamy, V., & Dharani, M. (2019). Investor attention using the Google search volume index – impact on stock returns. Review of Behavioral Finance, 11(1), 55–69.https://doi.org/10.1108/RBF-04-2018-0033
Teng, X., Wu, Z., & Yang, F. (2022). Research on the Relationship between Digital Transformation and Performance of SMEs. Sustainability (Switzerland), 14(10), 1–17.https://doi.org/10.3390/su14106012
Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139–1168.https://doi.org/10.1111/j.1540-6261.2007.01232.x
Truong, D. L., Lanjouw, G., & Lensink, R. (2010). Stock-market efficiency in thin-trading markets: the case of the Vietnamese stock market. Applied Economics, 42(27), 3519–3532.https://doi.org/10.1080/00036840802167350
Truong, L. D., & Friday, H. S. (2021). The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange. International Journal of Financial Studies,9(3), 43. https://doi.org/10.3390/ijfs9030043
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144.https://doi.org/10.1016/j.jsis.2019.01.003
Vo, X. V., & Truong, Q. B. (2018). Does momentum work? Evidence from Vietnam stock market. Journal of Behavioral and Experimental Finance, 17, 10–15.https://doi.org/10.1016/j.jbef.2017.12.002
Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17–35.https://doi.org/10.1016/j.jbankfin.2013.12.010
Walsh, J., Nguyen, T. Q., & Hoang, T. (2023). Digital transformation in Vietnamese SMEs: managerial implications. Journal of Internet and Digital Economics.https://doi.org/10.1108/JIDE-09-2022-0018
Zhang, Y., Song, W., Shen, D., & Zhang, W. (2016). Market reaction to internet news: Information diffusion and price pressure. Economic Modelling, 56, 43–49.https://doi.org/10.1016/j.econmod.2016.03.020
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Derechos de autor 2025 Tien Phat Pham, Duc Ngoc Nguyen, Tri Ba Tran
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