Building stock market resilience through digital transformation using Google trends to analyze the impact of COVID stock market articles 2019
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Building stock market resilience through digital transformation using Google trends to analyze the impact of COVID stock market articles 2019
Similar to other types of natural disasters or major incidents, COVID-19 is an unexpected large-scale incident whose impact on the global economy is manifested profoundly in global financial markets soon after the outbreak of the pandemic. The rapid spreading of the virus and sharp increase in the death tolls have had a large emotional and material impact and are widely regarded as “market-wide shocks,” which has changed the investor perception on the micro- and macro-economic environment of stock markets. Manyika, J., Pinkus, G., Ramaswamy, S., Woetzel, J., Nyquist, S., & Sohoni, A. . The US economy: An agenda for inclusive growth . Retrieved from sey.com/~/media/mckinsey/featured insights/Employment and Growth/Can the US economy return to dynamic and inclusive growth/MGI-US-Economic-Agenda-Briefing-paper-November-2016.ashx National Association of Securities Dealers Automated Quotations System In answering “what are the sectors that have performed better even as market sentiment is affected by the pandemic,” the analysis reveals that sectors with a higher degree of digital transformation have performed better. Thus, during the COVID-19 pandemic, the market has reacted more favorably towards firms and sectors with a higher level of digital transformation and more negatively towards the laggards. An explanation for this is thus offered. Data on stock market prices and Google Search Trend are available from the corresponding author upon request. Coreynen, W., Matthyssens, P., & Van Bockhaven, W. . Boosting servitization through digitization: Pathways and dynamic resource configurations for manufacturers. Industrial Marketing Management , 60 , 42–53. Schallmo, D., Williams, C., & Boardman, L. . Digital transformation of business models — Best practice, enablers, and roadmap. International Journal of Innovation Management , 21 , 1740014. By using this website, you agree to our Terms and Conditions , California Privacy Statement , Privacy statement and Cookies policy. Manage cookies/Do not sell my data we use in the preference centre. Huang, X., Kujipers, D., Li, L., Sha, S., & Xia, C. . How Chinese consumers are changing shopping habits in response to COVID-19. Retrieved from sey.com/~/media/McKinsey/Featured Insights/Asia Pacific/How Chinese consumers are changing shopping habits in response to COVID 19/How-Chinese-consumers-are-changing-shopping-habits-in-response-to-COVID-19-v3.pdf Blackburn, S., LaBerge, L., O'Toole, C., & Schneider, J. . Digital strategy in a time of crisis . McKinsey digital. Retrieved from sey.com/business-functions/mckinsey-digital/our-insights/digital-strategy-in-a-time-of-crisis . Paparrizos, J., & Gravano, L. . Fast and accurate time-series clustering. ACM Transactions Database Systems , 42 , 1–49. Montero, P., & Vilar, J. A. . TSclust: An R package for time series clustering. Journal of Statistical Software , 62 , 1–43. Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. . Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications , 112 , 258–273. Liao, T. W. . Clustering of time series data—A survey. Pattern Recognition , 38 , 1857–1874. Rousseeuw, PJ . Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. As the focus is on the impact that Google searches impose on stock price changes and also to differentiate the impact across the different groups, a detailed investigation into the results of Eq. is then conducted. Consistent with our prediction, Google search trends are positively related to the stock price changes of Group 1, lagged by two periods . On the other hand, results also show that Google search trends have a one-period lagged negative impact on the stock price changes of Groups 2 and 3. In other words, the relationship between Google search trends and the stock performance of Group 1 rather different than the other two groups. Google search trends can cause negative changes in the stock performance of firms in Groups 2 and 3, with a shorter time lag. In sum, there is strong evidence to show that the digital transformation of firms mitigates the negative impact of market sentiment due to large-scale unanticipated incidents on stock performance. Furthermore, as stock prices dip amid the coronavirus pandemic, firms with mid to high level of digital transformation have out-performed others. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. . Industry 4.0: The future of productivity and growth in manufacturing industries. The Boston Consulting Group , 9 , 54–89. Mackinlay, A. C. . Event studies in economics and finance. Journal of Economic Literature , 35 , 13–39. quasi-public and/or highly localized sectors lagging across most dimensions. This research thus extends the above-mentioned literature by investigating the effect of market sentiment associated with COVID-19, derived using Internet search trends, upon the stock market performance of firms across different sectors. Majchrzak, A., Markus, M. L., & Wareham, J. . Designing for digital transformation: Lessons for information systems research from the study of ICT and societal challenges. MIS Quarterly , 40 , 267–277. Phase 2 modeled Google search trends and stock price changes jointly in an augmented VAR. In this phase, the analysis compared and contrasted the stock performance of firms across the three levels of digital transformation, i.e., low, medium, and high, based on the MGI Industry Digitalization Framework. The analysis in the two phases built on each other, supporting the notion that market sentiment towards the COVID-19 pandemic as reflected in Google search trends affects the stock prices. Liu, Y., Peng, G., Hu, L., Dong, J., & Zhang, Q. . Using Google trends and Baidu index to analyze the impacts of disaster events on company stock prices. Industrial Management & Data Systems , 120 , 350–365. Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. . Web search queries can predict stock market volumes. PLoS One , 7 , e40014. The results of Eq. examine the impact of stock price changes for each group of firms on Google search trends. As indicated in Table  8 , it is observed that stock price changes trigger changes in the Google search trend in the opposite direction. Stock price increase in Group 1 will drive a decline of Google search with a lag of seven periods. Similar relationships are observed in Groups 2 and 3. That is, a decline in stock price during the coronavirus outbreak could be easily attributed to the pandemic by the investors, which may further arouse market-wide concerns over the pandemic. Such concerns over the spread of COVID-19 and economic outlook can translate into a higher Google search volume. The impact on Google search trends is strongest for firms in Group1 and weakest for firms in Group 3, with Group 2 falling in the middle. In the first phase, a three-group model on how stock prices adjust to market sentiment towards the sudden emergence of the COVID-19 pandemic is established. The stock prices of a majority of firms across sectors with a higher level of digital transformation are found to have remained resilient to the impact of market sentiment, while sectors that lag across most digital transformation dimensions are among the most negatively affected. Joseph, K., Wintoki, M. B., & Zhang, Z. . Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting , 27 , 1116–1127. Lastly, this paper does not investigate the causes behind the growing gap between sectors in digital transformation. Future research may try to uncover how the different conditions lead to a widening gap between the “haves” and the “have-mores”: companies and sectors that are using their digital capabilities far more than others to innovate and transform how they operate. service sectors with a long tail of small firms having room to transform their customer transactions digitally; Nicola, M., Alsafi, Z., Sohrabi, C., Keerwan, A., Al-Jabir, A., Iosifidis, C., et al. . The socio-economic implications of the coronavirus pandemic : A review. International Journal of Surgery , 78 , 185–193. Deng, S., Huang, Z. J., Sinha, A. P., & Zhao, H. . The interaction between microblog sentiment and stock return: An empirical examination. MIS Quarterly , 42 , 895–918. Lei, Y., Bezdek, J. C., Chan, J., Vinh, N. X., Romano, S., & Bailey, J. . Extending information-theoretic validity indices for fuzzy clustering. IEEE Transactions on Fuzzy Systems , 25 , 1013–1018. This paper has limitations that offer opportunities for future research. First, our study examines how sectoral digital transformation mitigates the effect of market sentiment on negative events on the stock market, focusing on the period from the outbreak of the COVID-19 to the eventual stock market crash. Since early April 2020, much of the stock markets’ main indices have regained much of their lost territory for the year. The aggressive stimulus packages roll out by central banks and governments to boost growth, such as the U.S. coronavirus relief funds announced around early April 2020, have fueled recovery. Such confounding effects of government support and intervention on the stock market are difficult to account for in the current research model. Future research can look into how the intervention of governments and support of central banks restore investors’ confidence and accelerate the recovery phase of the market cycle. Box, G.E.P., & Tiao, G.C. . Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association , 70 , 70–79. B2B sectors with the potential to digitally engage and interact with their customers; He, Z., & Maekawa, K. . On spurious granger causality. Economics Letters , 73 , 307–313. School of Business, Singapore University of Social Sciences, 463 Clementi Rd, Singapore, 599494, Singapore Kane, G. C., Philips, A. N., Copulsky, J. R., & Andrus, G. R. . The technology fallacy: How people are the real key to digital transformation . Cambridge: The MIT Press. labor-intensive sectors with the potential to provide digital tools to their workforce; Noventum Service Management . Manufacturers’ advanced services: IoT as the key to profitability and growth, . Birmingham; Aston business school. Retrieved from sites/default/files/White Paper - Manufacturers' advanced services - IoT as the key to profitability & growth .pdf . Agarwal, R., Gao, G., Desroches, C. M., Jha, A. K. . The digital transformation of healthcare: current status and the road ahead. Information System Research , 21 , 796–809. Table 6 shows that Google search trends affect the stock price fluctuation of firms in all three groups, which confirms our earlier prediction. The stock price changes of firms in Groups 1 and 2 also affect Google search trends, but not Group 3. These results indicate that the changes in stock prices for firms in Groups 1 and 2 can trigger fluctuations in market sentiment. However, the magnitude and the directions of such impact await further investigation from the augmented VAR models, summarized in Tables  7 and 8 . Andrei, D., & Hasler, M. . Investor attention and stock market volatility. The Review of Financial Studies , 28 , 33–72. ADB . Global cost of coronavirus may reach USD4.1 trillion, ADB Says . Manila; Bloomberg. Retrieved from sites/default/files/publication/575626/ado2020.pdf . Further, considering the observation that firms which engaged in digital transformation performed ostensibly better in terms of the financial markets, the third sub-section on the topic is presented. Ding, D., Guan, C., Chan, C. stock market articles 2019 M.L. et al. Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic. Front. Bus. Res. China 14, 21 . s11782-020-00089-z Palese, P. . The great influenza: The epic story of the deadliest plague in history. The Journal of Clinical Investigation , 114 , 146. Building stock market resilience through digital transformation using Google trends to analyze the impact of COVID stock market articles 2019
Building stock market resilience through digital transformation using Google trends to analyze the impact of COVID stock market articles 2019
Sardá-Espinosa, A. . Comparing time-series clustering algorithms in R using the dtwclust package. The R Journal , 11 , 41–96. Neu, W. A., & Brown, S. W. . Forming successful business-to-business services in goods-dominant firms. Journal of Service Research , 8 , 3–17. By analyzing the extracted centroid, the times series patterns of the two clusters are then examined. For firms in Cluster 1, it is observed that a steeper declining trend of market capitalization occurs amid the COVID-19 outbreak. This is thus termed as the Sensitive Cluster . That is, these firms are likely to be more sensitive to the pandemic. In comparison, firms in Cluster 2 are found to be more resilient to the pandemic with a flatter market capitalization curve both pre and post the declaration of the pandemic by the World Health Organization on March 11, 2020. Thus, this cluster is named as the Resilient Cluster. In studying market sentiment, this paper adopts a novel approach to track it through online search trends. Thus, the second sub-section of this literature review focuses on how online search trends have been employed in studying investors’ behavior and stock market performances. Tetlock, P. C. . Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance , 62 , 1139–1168. The MGI Industry Digitalization Framework, as illustrated in Fig. 1 , shows the level of the digital transformation of the various sectors, and gives a clear picture of where the sectors stand in the development of digital transformation. It combines indicators to show how firms are building digital assets, expanding digital usage, and creating a more digital workforce. This indicates that, along with the information and communication technology sector, media, financial services, and professional services are ahead of the rest of the sectors in terms of digital transformation. By contrast, sectors such as health care, education, personal and local services, hospitality, basic goods manufacturing, and construction are lagging considerably behind. The uneven pace of digital transformation is creating a new digital divide between the digital “haves” and “have-mores” across sectors and among firms. The most highly digitally transformed sectors have posted two to three times higher rates of growth in profit margin, than others, and wages, than the national average. Firms with advanced digital assets and capabilities have generated higher rates of revenue growth and higher return to shareholders. The “have-mores” are not just large firms that dominate one sector. They can also be small, innovative firms or firms whose digital assets enable them to play in multiple sectors.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., P’erez, J. M., & Perona, I. . An extensive comparative study of cluster validity indices. Pattern Recognition , 46 , 243–256. All authors contributed to the design and implementation of the research, to the analysis of the results and the writing of the manuscript. The author read and approved the final manuscript. © 2021 BioMed Central Ltd unless otherwise stated. Part of Springer Nature . You can also search for this author in PubMed   Google Scholar As the 2019 novel coronavirus disease pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression . The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak. To investigate the sectors that have performed better even as market sentiment is affected by the unfolding of the pandemic, an empirical study was designed based on data from Google search trends and stock prices. The MGI Industry Digitalization Framework was referenced as a basis to organize the sectors. You can also search for this author in PubMed   Google Scholar Baig, A., Hall, B., Jenkins, P., Lamarre, E., McCarthy, B. . The COVID-19 recovery will be digital: a plan for the first 90 days . McKinsey Digital. Retrieved from sey.com/business-functions/mckinsey-digital/our-insights/the-COVID-19-recovery-will-be-digital-a-plan-for-the-first-90-days?cid=other-eml-alt-mbl-mck&hlkid=c10f8eca65f749e288443f04fbcffc5f&hctky=1529367&hdpid=dfb4c609-2604-4df3-aa . You can also search for this author in PubMed   Google Scholar Toda, H. Y., & Yamamoto, T. . Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics , 66 , 225–250. You can also search for this author in PubMed   Google Scholar From the initial pool, entries with missing data are eliminated. Pharmaceutical and biopharmaceutical companies play a key role on the front lines of the battle against the coronavirus. While global stock markets take a COVID-19 beating, pharmaceutical stocks are generally performing better than those of other industries, with additional investments in the race to develop coronavirus vaccines and therapeutics. Makers of diagnostic test kits, sanitizers, and protective masks have all ramped up to meet the unprecedented demand. As the current research focuses on how digitally transformed sectors show resilience towards negative market sentiment on the pandemic, the pharmaceutical and biopharmaceutical companies have been removed from the sample to avoid confounding effects, as each of these stocks has the potential for considerable gain, whether it is because they are developing a treatment or their products are in greater need amid the outbreak. The final dataset admitted for analysis included 1,568 firms across 37 sectors. The reopening of businesses and economies is also underpinning market optimism. It is plausible that sectoral digital transformation can also influence how firms perform during the recovery period. Therefore, a possible extension of our research is to investigate how digital transformation moderates the speed and extent of the stock market rebound, as it is of interest to both firms and investors. Firms in the most digitally advanced sectors are likely to be better positioned for the recovery and the post-pandemic period. Regarding the research method, this study demonstrates the feasibility of using a search trend index to model market sentiment, especially when investigating how large-scale anticipated incidents can affect the stock prices for a large number of firms or across sectors. This presents researchers with another approach to assess market sentiment and expands the research arsenal to include search indexes such as Google trends, which are readily available for use. Hai, W., Zhao, Z., Wang, J., Hou, Z.-G. . The short-term impact of SARS on the Chinese economy. Asian Economic Papers , 3 , 57–61. After carrying out the tests for misspecification, the VAR model with Lag 6 is chosen, and one additional lag into each variable is added to Eqs. and , given the maximum order of integration I. Therefore, an augmented VAR model for Eqs. and is constructed, respectively. This is followed by a Wald test, whereby the hypotheses that the coefficients of the first six lagged values of X in Eq. and the coefficients of the first six lagged values of Y in Eq. are 0 are tested. The reason not to include the coefficient of the 7th lag is that the additional lagged value is to fix the asymptotic so that the Wald test statistics would be under the null hypothesis that it follows asymptotical chi -square distribution. Rejection of the null hypothesis of the Wald test implies a Granger causality. The results are shown in Table  6 . Atsuko, O., & Karazhantva, A. . Digital resilience against COVID-19 . Bangkok: United nation ESCAP. Retrieved from scap.org/blog/digital-resilience-against-covid-19 #. In Eqs. and , Y t represents the average day-on-day stock price change in percentage, X t and Z t represent the Google search index of the keyword coronavirus and oil, respectively. Meanwhile, p is the lag order, and a p , b p , c p , d p , e p , are the coefficients of Y t-p , X t-p , and Z t-p. Additionally, a 0 and c 0 are the constant terms and u t and v t are the error terms. The null hypothesis for Eq. is H 0 : b 1  =  b 2  = ... = b p  = 0, and the alternative hypothesis is H A : Not H 0 . When H 0 is rejected, X is the Granger-cause of Y . Similarly for Eq. , H 0 : d 1  =  d 2  = ... = d p  = 0, against H A : Not H 0 , is to test the hypothesis that Y does not Granger-cause X . To interpret the equations, we are to test if Y could be better predicted by the histories of its own and X than its history. If H 0 could be rejected, then it implies a Granger causality. We did not explicitly build the hypothesis for effect between Y t and Z t , because the research question is to investigate whether there is a causal relationship between market performance and public attention to coronavirus. Kim, M., Ramakrishna, R.S. . New indices for cluster validity assessment. Pattern Recognition Letters, 26, 2353-2363 Coronavirus Aid, Relief, and Economic Security Hess, T., Matt, C., Benlian, A., & Wiesboeck, F. . Options for formulating a digital transformation strategy. MIS Quarterly , 15 , 123–139. Chan, M. L., Teoh, S. Y., Yeow, A., & Pan, G. . Agility in responding to disruptive digital innovation: Case study of an SME. Information Systems Journal , 29 , 436–455. Lee, K. J., Lu, S. L., & Shih, Y. . Contagion effect of natural disaster and financial crisis events on international stock markets. Journal of Risk and Financial Management , 11 , 1–25. The spread of the COVID-19 virus has led governments around the world to shut down their cities to slow down the rate and magnitude at which the pandemic is developing. Even as such shutdowns have brought many corporeal economic activities to a near-complete standstill, consumer purchases and even trade not only continue online, but they have increased considerably. Demand for Internet bandwidth as well as portable computing devices such as laptops and tablets also intensify as many people switch to working and learning online. Many are also predicting the emergence of a post-COVID-19 period, where digital transformation will be prominent. Given these, the markets may have given eminence and greater confidence to firms and sectors with a higher level of digital transformation as they are positioned better to sustain operations not only amid the pandemic, but also to recover faster in the post-COVID-19 period. Thus, sectors with a higher level of digital transformation have been more resilient in their performance and also performed better relative to sectors with lower levels of digital transformation. Barrett,  W.B., Heuson, A. J., Kolb, R. W., & Schropp, G. H. . The adjustment of stock prices to completely unanticipated events. The Financial Review , 22 , 345–354. In the second phase , market sentiment on the COVID-19 pandemic, as reflected in Google search trends, is affirmed to be a predictor of stock performance. There is also further evidence to show that the digital transformation of firms mitigates the negative impact of market sentiment induced by large-scale unanticipated incidents. As the stock price of firms dips amid the coronavirus pandemic, firms with mid to high level of digital transformation have out-performed others. Analysis in the second phase also suggests that a rise in stock price can affect market sentiment. Gandhi, P., Khanna, S., & Ramaswamy, S. . Which industries are the most digital ? Brighton; Harvard Business Review Retrieved from shows-which-industries-are-the-most-digital-and-why . Ding Ding, Chong Guan, Calvin M. L. Chan & Wenting Liu Kowalkowski, C., Kindström, D., & Gebauer, H. . ICT as a catalyst for service business orientation. The Journal of Business and Industrial Marketing , 28 , 506–513. Anyone you share the following link with will be able to read this content: Khurram, S., Liu, X., & Hayfa, K. . COVID-19’s disasters are perilous than global financial crisis: A rumour or fact? Finance Research Letters. Nofsinger, J. . Social mood and financial economics. Journal of Behavioral Finance , 6 , 144–160. Clement, J. . E-commerce share of total retail sales 2015–2023. Retrieved from statista.com/statistics/534123/e-commerce-share-of-retail-sales-worldwide/ . Further, this study contributes to understanding the role of digital transformation in the firms’ stock market performance, particularly due to large-scale unanticipated incidents such as a pandemic. While previous research has investigated whether the use of technology can improve organizational performance, this research is among the first to demonstrate how digital transformation may give rise to stock market performance. This suggests that digital transformation is an important factor for investors. knowledge-intensive sectors that are highly transformed digitally across most dimensions; Keywords in Google searches related to COVID-19 Da, Z., Engelberg, J., & Gao, P. . In search of attention. Journal of Finance , 66 , 1461–1499. Provided by the Springer Nature SharedIt content-sharing initiative Cam, M. A., & Ramiah, V. . The influence of systematic risk factors and econometric adjustments in catastrophic event studies. Review of Quantitative Finance and Accounting , 42 , 171–189. Frontiers of Business Research in China volume  14 , Article number:  21 Cite this article Vlastakis, N., & Markellos, R. N. . Information demand and stock market volatility. Journal of Banking & Finance , 36 , 1808–1821. Sorry, a shareable link is not currently available for this article. Consistent with expectation, more than two-thirds of the firms within the most digitally transformed sectors, such as telecommunications equipment, personal services, financial conglomerates, and advertising/marketing services, fall under the Resilient Cluste . The majority of the firms in the lowest rung , including agriculture, hotels, and healthcare, fall under the Sensitive Cluster. Firms with mid-level of sectoral digital transformation fall in between, with about half of the firms being in the Resilient Cluster . Additionally, Pearson χ 2 statistic is used to test the two-way associations with frequencies in the cells =16.667, p  < 0.05). The results provide some directional support to the relative impact of digital transformation. It also validates the relevance of the level of digital transformation as a grouping variable to be used in the analysis in Phase 2. Various event-related keywords are first explored. For example, the search for keywords such as “oil price,” “petroleum” and “oil war” are compared with those for “oil. what is the stock market doing today cnn