Short‐term Herding of Institutional Traders: New Evidence from the German Stock Market

• Author: Dieter Nautz,Stephanie Kremer
• Published date: 01 September 2013

1.

Herding behaviour of investors, defined as the tendency to accumulate on the same side of the market, is often viewed as a significant threat for the stability and the efficiency of financial markets, see Hirshleifer and Teoh () and Hwang and Salmon (). The empirical literature on herding behaviour in financial markets is particularly interested in the investment behaviour of institutional investors, i.e., of banks and other financial institutions, see e.g. Barber et al. (). Yet, the evidence on herding behaviour of institutional investors is mixed and partly elusive.

The evidence on herding is often impeded by data availability problems. In particular, positions taken by institutions on the stock market are reported only infrequently, if at all. For example, for US mutual funds reports of holdings are available only on a quarterly basis, see e.g. Choi and Sias (). Evidence for German mutual funds even had to be based on semi‐annual data, see Walter and Weber (). In highly‐developed financial markets, however, herding might also occur within shorter time intervals.

Several contributions, including Barber et al. (), attempt to overcome the problem of data frequency by using anonymous transaction data instead of reported holdings. Since those data do not identify the trader, researchers usually separate trades by size and then simply define trades above a specific cutoff size as institutional. However, even though large trades are almost exclusively the province of institutions, institutions with superior information might split their trades to hide their informational advantage. While low‐frequency data may still contain useful information about longer‐term herding, the interpretation of herding measures based on anonymous transactions is not without problems. In particular, it is not clear whether the strategic trading behaviour of institutional investors tends to increase or decrease the evidence on herding.

The current paper sheds more light on the empirical relevance of short‐term herding by introducing a new and comprehensive data‐set on German stock market transactions that includes both high‐frequency and investor‐level data. Our analysis provides new evidence on the herding behaviour of financial institutions for a broad cross‐section of stocks over the period from July 2006 to March 2009 in the German stock market. In order to investigate how the underlying data frequency may affect the empirical assessment of short‐term herding, we evaluate herding measures at daily, monthly, and quarterly frequency. Neglecting the investor‐related information contained in our data set, we explore how herding measures are affected by the use of anonymous transaction data.

The empirical results suggest that previous studies based on low‐frequent or anonymous transaction data might have overestimated the extent of short‐term herding. This conclusion holds irrespective of the herding measure applied. Confirming the results obtained with the static herding measure proposed by Lakonishok et al. (), the dynamic measure of Sias () shows that institutional trades are correlated over time. However, although there are investors who follow other traders, the main part of the correlation results from institutions that follow their own trading strategy. We find that daily herding measures typically contradict implications of herding theory. In particular, it is not confirmed that short‐term herding is more pronounced in smaller and less liquid stocks. Moreover, our results do not indicate that short‐term herding increases in times of market stress, i.e., during the recent financial crisis. It is worth noting, however, that conclusions concerning the impact of the financial crisis on the trading behaviour of institutional investors would have been misleading if herding measures were based on anonymous transaction data.

The rest of the paper is structured as follows: Section 2 briefly reviews the literature on herding. Section 3 discusses the role of data availability on the herding measure. Section 4 introduces the applied herding measures. Section 5 presents the empirical results and Section 6 offers some conclusions.

2.

2.1.

Following e.g. Bikhchandani and Sharma (), herding describes the tendency of institutions or individuals to show similarity in their behaviour and thus act like a herd. Recent economic theory distinguishes between intentional herding and unintentional, or spurious herding. Unintentional herding is mainly fundamental driven and arises because institutions may examine the same factors and receive correlated private information, leading them to arrive at similar conclusions regarding individual stocks, see e.g., Hirshleifer et al. (). Moreover, professionals may constitute a relatively homogenous group: they share a similar educational background and professional qualifications and tend to interpret informational signals similarly.

In contrast, intentional herding is more sentiment‐driven and involves the imitation of other market participants, resulting in simultaneous buying or selling of the same stocks regardless of prior beliefs or information sets. This type of herding can lead to asset prices failing to reflect fundamental information, exacerbation of volatility, and destabilisation of markets, thus having the potential to create, or at least contribute, to bubbles and crashes on financial markets, see e.g. Morris and Shin () and Persaud (). Yet, several economic theories including models of information cascades (Avery and Zemsky, ) and reputation (Scharfstein and Stein, ) show that even intentional herding can be rational from the trader's perspective.

Models of intentional herding typically assume that there is only little reliable information in the market and that traders are uncertain about their decisions and thus follow the crowd. In contrast, in the case of unintentional herding, traders acknowledge public information as reliable, interpret it similarly and thus they all end up on the same side of the market. For both types of herding, the degree of herding is linked to the uncertainty or availability of information.

2.2.

2.2.1.

The empirical literature explores the determinants of herding via the link between herding and information availability. Lakonishok et al. () segregate stocks by size because market capitalisation of firms usually reflects the quantity and quality of information available. Thus, one would expect higher levels of herding in trading small stocks as evidence of intentional herding. In line with theoretical predictions, they find evidence of herding being more intense among small companies compared to large stocks. Further empirical evidence on the link between herding and size is provided by Wermers () and Sias ().

Based on semi‐annual data, Walter and Weber () and Oehler and Wendt () report significant positive and higher levels of herding for German mutual funds compared to those found in US‐based research. Walter and Weber () link the finding of herding to the stage of development of the financial market. They argue that the German market is not as highly developed as the US and UK capital markets. There is also evidence for higher herding levels in emerging markets compared to developed ones. High herding in emerging markets may be attributed to incomplete regulatory frameworks, especially in the area of market transparency. Deficiencies in corporate disclosure and information quality create uncertainty in the market, throw doubt on the reliability of public information, and thus impede fundamental analysis, see Antoniou et al. () and Gelos and Wei (). Kallinterakis and Kratunova () argue that in such an environment it is reasonable to assume that investors will prefer to base their trading on their peers’ observed actions. Thus, intentional herding through information cascades is more likely to occur in less developed markets. In the current paper, we assume that the degree of market transparency increases with the size of the traded stocks. As a result, less herding in larger stocks may also appear because the corresponding markets are more highly developed and, thus, more transparent.

2.2.2.

The extent of herding may depend on the state of the overall market. Choe et al. () find higher herding levels before the Asian crisis of 1997 than during the crises for the Korean stock market. Using data from the Jakarta Stock Exchange, Bowe and Domuta () show that herding by foreigners increased following the outbreak of the crisis. Analysing the relationship between the cross‐sectional dispersion of returns and their volatility, Chiang and Zheng () conclude that herding behaviour appears to be more apparent during the period in which the financial crisis occurs. In contrast, using data from US and South Korean stock markets, Hwang and Salmon () find higher herding measures during relatively quiet periods than during periods when the market is under stress. In order to account for the state of the market, the following empirical analysis allows for different herding intensities before and during the recent financial crisis.

3.

3.1.

3.1.1.

Most empirical studies on herding in financial markets identify institutional transactions as changes in reported positions in a stock. However, positions are reported very infrequently. For example, the bulk of the literature considers the trading behaviour of US mutual funds who generally report only on a quarterly basis. For German mutual funds, even half‐year reports are required. Semi‐annual and even quarterly data provide only a crude basis for inferring trades and this frequency might be too low in a rapidly changing stock market environment. Interestingly, the overall effect of the data‐frequency on the resulting herding measure...