Did large institutional investors flock into the technology herd? An empirical investigation using a vector Markov-switching model

Balagyozyan, Aram; Çakan, Esin

doi:10.1080/00036846.2016.1184376

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…We find relatively large buy herding measures, they range between 0.12 and 0.16 depending on the correction for the accrued interest rate and the number of trades. These findings are similar to that of Balagyozyan and Cakan (2016). Their results indicate that herding was much more prevalent in the run-up to the Dot.com bubble than during the collapse.…”

Section: Herding Behaviour Of Pension Fundssupporting

confidence: 82%

Herding Behaviour of Dutch Pension Funds in Sovereign Bond Investments

Koetsier

Bikker

2017

SSRN Journal

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This study investigates herding behaviour exhibited by Dutch pension funds in the sovereign bond market. It uses a unique dataset on sovereign bond holdings of pension funds, mutations and transactions between December 2008 and December 2014. It covers 67 large Dutch pension funds that invest in 109 countries. We find evidence of intensive herding behaviour of Dutch pension funds in sovereign bonds. Our findings also show that institutional factors, the macroeconomic environment and the financial market environment are among the determinants of herding behaviour in sovereign bonds. Our results also indicate that high diversification is not without costs as it intensifies herding behaviour. We find mixed evidence on whether pension funds are stabilising actors. The destabilising effect is most pronounced on the sell side, while stabilisation is most prominent under more extreme price shocks. The distinction between developing and emerging economies and developed economies does not change these results.

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Section: Herding Behaviour Of Pension Fundssupporting

confidence: 82%

Herding Behaviour of Dutch Pension Funds in Sovereign Bond Investments

Koetsier

Bikker

2017

SSRN Journal

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“…Data on institutional common stock holdings and transactions reported quarterly by financial institutions with $100 million or more under management on their SEC 13(f) forms is obtained from the Thomson Reuters database. Since the main focus of the study is on institutional herding among large investors, following Zykaj et al (2016) and Balagyozyan and Cakan (2016), we limit our sample to large independent investment advisors and other uncategorized investment companies with at least $1 billion under discretionary management. 1 Furthermore, in order to avoid survivorship bias, we only include investors whose equity portfolios in September 2012 had at least 80 quarters of continuous data, leaving us with 115 investors in all.…”

Section: Datamentioning

confidence: 99%

“…Challenging earlier findings on institutional investors, Li et al (2016) show that herding behavior is more pronounced among individual investors rather than institutionals as the former group tends to rely more on public information and market sentiment. Similarly, Hsieh (2013) argues that institutional trading significantly improves stock price efficiency, while Balagyozyan and Cakan (2016) The literature on herding among institutional investors has primarily utilized tests based on holding data to detect herding. The most commonly used metric for herding in this regard is the measure by Lakonishok, Shleifer and Vishny (1992) (LSV) and Sias (2004) that is based on the changes in asset positions across investors in two consecutive periods.…”

Section: Introductionmentioning

confidence: 99%

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

Uwilingiye

Çakan

Demirer

et al. 2019

RBF

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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.

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“…The animal spirit is neither rational nor irrational and varies with the context (Baddeley, 2013). However, one main issue with animal spirit is that it makes the investor unable to respond to new information logically and categorize its relevance accordingly, thus as a result, the private information held by the investor is lost and they end up following the herd (Balagyozyan & Cakan, 2016; Villatoro, 2009). Further, one of the essential elements of herding is the interaction of participants as it helps them exchange the information and act accordingly on the signals that are received from other market participants.…”

Section: Introductionmentioning

confidence: 99%

Do investors herd? An examination of Pakistan stock exchange

et al. 2020

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The study attempts to examine herding behaviour in Pakistan stock exchange and determine how herding behaviour responds to asymmetric market conditions. This study has employed CH model, CSSD & CSAD models, and State‐space model. We have used time series data for the period from 2000 to 2016, which include daily returns of KSE all share index and 890 firms listed on Pakistan stock exchange. The outcomes of the study not only reported significant evidence of herding behaviour in Pakistan stock market over the entire sample period but also found it to be more pronounced under extreme market conditions, market volatility and financial crisis. Further, it was found that herding behaviour increases before the crisis, whereas decreases at the time of crisis. Fund managers and investors need to take herding behaviour in to account to evaluate asset prices and require a large number of stocks for achieving a correlation of inferior degree in portfolios and same degree in diversification. In addition, funds herding can be used as a signal of managerial quality. Moreover, as herding implies that fund managers and investors react to regulatory changes in a similar way, which may prevent asset prices from reaching their fundamental values and further causes financial markets to become fragile and destabilize, thus policymakers should take measures to control herding.

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Did large institutional investors flock into the technology herd? An empirical investigation using a vector Markov-switching model

Cited by 12 publications

References 36 publications

Herding Behaviour of Dutch Pension Funds in Sovereign Bond Investments

Herding Behaviour of Dutch Pension Funds in Sovereign Bond Investments

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

Do investors herd? An examination of Pakistan stock exchange

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