A note on the technology herd: evidence from large institutional investors

Abstract

Purpose The purpose of this paper is to examine intentional herding among institutional investors with a particular focus on the technology sector that was the driver of the “New Economy” in the USA during the dot-com bubble of the 1990s. Design/methodology/approach Using data on technology stockholdings of 115 large institutional investors, the authors test the presence of herding by examining linear dependence and feedback between individual investors’ technology stockholdings and that of the aggregate market. Unlike other models to detect herding, the authors use Geweke (1982) type causality tests that allow authors to disentangle spurious herding from intentional herding via tests of bidirectional and instantaneous causality across portfolio positions in technology stocks. Findings After controlling information-based (spurious) herding, the tests show that 38 percent of large institutional investors tend to intentionally herd in technology stocks. Originality/value The findings support the existing literature that investment decisions by large institutional investors are not only driven by fundamental information, but also by cognitive bias that is characterized by intentional herding.

COVID-19 Pandemic and Investor Herding in International Stock Markets

“…Irrespective of the nature of such behavior among investors, rational or otherwise, the literature generally suggests that herding is more prevalent during periods of market stress or heightened uncertainty. Although a large number of studies have examined the presence of herding behavior in financial markets from different angles and using a wide range of samples (e.g., see Uwilingiye et al 2019 for a recent review), the literature has not yet examined the role of the recent COVID-19 pandemic in this context as a driver of herding behavior among market participants. Given Risks 2021, 9, 168 2 of 11 that the recent COVID-19 pandemic has triggered a massive spike in uncertainty, quickly transitioning from a healthcare crisis into an economic one, this paper examines the role of the pandemic as a driver of investor herding in international stock markets by utilizing a daily newspaper-based index of financial uncertainty associated with infectious diseases, recently developed by Baker et al (2020).…”

Regulatory mood-congruence and herding: Evidence from cannabis stocks

“…Grinblatt et al, 1995). Fads arise any time a particular group of stocks (usually, though not necessarily, a sector) grows in popularity, prompting investors to actively target them; this can lead to herding toward that sector, as evidence from the Dot Com bubble period (Brunnermeier and Nagel, 2004;Griffin et al, 2011;Singh, 2013;Uwilingiye et al, 2019) and cryptocurrencies (Bouri et al, 2019;Kallinterakis and Wang, 2019;Vidal-Tomás et al, 2019;Kaiser and Stöckl, 2020) suggests.…”

“…This would be in line with the predictions of the real-option channel theory of capital investments, according to which economic actors prefer to delay committing resources to an investment if uncertain conditions prevail (see the discussion of the relevant literature in Pastor and Veronesi, 2012); in this case, delaying constitutes an option of high value, as it prevents the actor from making a mistake. Alternatively, if, despite the high information risk, 12 A relevant example of this is the Dot Com bubble, where investors exhibited strong herding tendencies (Brunnermeier and Nagel, 2004;Griffin et al, 2011;Singh, 2013;Uwilingiye et al, 2019) amid a period when regulators catered (actively and passively; Gerding, 2007) to the prevailing optimistic mood in the market. A less widely citedyet equally relevant caseis that of the Chinese government's active endorsement of the gambling industry's deregulation in Macau in 2002, which boosted investment activity in the region and prompted a surge in sentimentand herding -among investors targeting Hong Kong-listed stocks of companies involved in Macau-investments (Lai et al, 2007).…”

“…Herding, for example, has been found (Lakonishok et al, 1992;Wermers, 1999;Sias, 2004;Dang and Lin, 2016;Cui et al, 2019) to maintain an inverse relationship with size (appearing the strongest for smaller capitalization stocks) due to the high information risk of small stocks prompting investors to monitor their peers' actions when trading them. Additionally, sectors with greater uncertainty (in terms, e.g., of their cash flows) may be more prone to exhibiting herding, as evidence from Technology stocks has suggested in several studies (Brunnermeier and Nagel, 2004;Griffin et al, 2011;Singh, 2013;Gavriilidis et al, 2013;Andrikopoulos et al, 2017;Uwilingiye et al, 2019).…”

“…Evidence from a large body of research (Prechter, 2001;Nofsinger, 2005;Olson, 2006;Lucey and Dowling, 2005;Gavriilidis et al, 2016) has denoted that social mood constitutes a key determinant of individuals' propensity to herd in their investments. This has been particularly noticeable in cases whereby social mood experiences shifts that help propel the adoption of innovations in society and give rise to new industries, as has been empirically illustrated with respect to Technology stocks during the Dot Com bubble (Brunnermeier and Nagel, 2004;Griffin et al, 2011;Singh, 2013;Uwilingiye et al, 2019) and, in recent years, for cryptocurrencies (Bouri et al, 2019;Kallinterakis and Wang, 2019;Vidal-Tomás et al, 2019;Kaiser and Stöckl, 2020).…”