Herd Behavior in the Financial Market

Herd behavior has been studied by different scholars through a focus on its diverse facets. Empirical literature exists in the herd behavior in the US financial markets, the international markets, in oil exporting countries, herding and implied volatility index, cross market herding, and dynamic herding.  Herd behavior has a significant role in behavioral finance and ultimately leads to important decisions among investors and life at large.

There are multiple studies that examine herding behavior in numerous facets of the same, with a focus on historical trends. Therefore, the idea of herding behavior emanates from the tendency of individuals to suppress their beliefs, intuition, and convictions to adopt a collective approach and majority decisions in their investment decisions and choices, regardless of their existing disagreements with the predictions about the market.

Therefore, herding behavior is characterized by the convergence of investors’ decisions while ignoring personal signals and making decisions on the trends of others. Herding behavior has been pronounced in diverse contexts in the American stock market as well as the international markets as evidenced by diverse empirical literature.

In a study by Belgacem and Lahiani, the researchers investigated herding behavior associated with the US after announcements of macroeconomic statistics. A total of 18 European countries were involved in the study.

Using daily stock price data ranging from February 2000 to July 2011, alongside many US macroeconomic fundamentals, the study established presence of a decreased intentional herding in respect to news about US macroeconomic data. Some of the European countries adopted intentional herding while others such as Greece demonstrated spurious herding.

In a study to investigate herd behavior, daily data for the Portuguese stock market was used for years 2000 and 2016. The study further utilized cross sectional absolute deviation returns to measure dispersion and cross sectional standard deviation. Based on the rational asset pricing model, there is an increase in equity return dispersion with market return.

However, in the presence of imitative behavior among the investors, there is no observable increase in the linear relation between market return and dispersion. This makes it difficult to adopt an effective risk diversification strategy. The study of the Portuguese market thus indicated a statistical significant presence of herd behavior in conditions of information asymmetry, especially during increased market returns. The findings were consistent with those of Chen ET AL..

The factors that have potential influence of the investor behavior are hugely significant in the context of emerging markets since trade choices are affected by external shocks in the global markets. Using a dynamic parameter model varied over time, Balcilar ET AL. examine whether speculation and volatility in oil markets have influence on herding behavior among investors, at the local level and in nations that are regarded major exporters.

The study deeply focuses on the countries in the Gulf Cooperation Council, which are the major oil rich economies. The study used data obtained from individual firms from the Gulf Arab stock markets. The findings reveal that investors portray herd behavior in instances of high volatility and no herd behavior was evident during days of calmness in the markets. The over dependence of GCC countries on energy and the limitation in the supply of alternative financial stocks means that the exposures are high, thus difficulties exist in the process of diversification.

Therefore, investor behavior is likely to demonstrate herding behavior in times when market dynamics in the oil sector change whereby investors will always tend to react to signals in the market or news. At some point in studying Economics, Finance, or Sociology, it is hard for college students to combine social drivers, financial results, investors’ behavior in one logical structure.

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There is an impact of the implied index volatility index (VIX) on expected market volatility. In a study to test all major global indexes, Siriopolous and Fassas (2002), documented the information on returns and volatility underpinning the indexes. Through an empirical review, the study found that implied volatility indices contained details about future volatility more than the past volatility.

Also, Jlassi and Naoui, document the impact of herding behavior on US DJIA and US S&P100 stocks and their respective market volatility. The study sought to determine whether herd behavior was prevalent in the two stock market indexes between January 2000 and July 2012. The findings from the study revealed the presence of herding behavior, particularly on a daily basis.

The results also indicated that there was a significant adjustment of the herding behavior across sub-periods during the US subprime mortgage crisis. Therefore, there were indications of herding behavior during high volume trading and bull period days.

In an empirical review of herding behavior in East Asian stock market towards the US stock market, Yang et al., observed the existence of herding behavior in the Asian Pacific markets towards the dominant US market, especially in times of major events affecting the stock markets.

The study investigated several events such the internet bubble, the global financial crisis, the SARS outbreak, the September 11 attacks, and the Asian financial crisis. Thus the findings of the study indicated that the American stock market depicted the most influential effect over the East Asian markets.

In addition, Kataria and Choudhary, provide an empirical literature review on the existence of herding behavior across diverse financial markets. The findings in literature prove the importance of herding behavior in financial markets and the need for practitioners and academicians to consider the implications it would have in investment decisions. The existence of stock mispricing in financial markets is driven by herd behavior among investors and practitioners leading to market inefficiencies.

Many studies on herding behavior have failed to incorporate the dynamic nature of herding and its implications on financial markets. The studies have mostly attempted to focus on limited scopes and utilization of limited models that do not capture time varying aspects of herding behavior in financial markets.

Motivated by these limitations, Chiang ET AL., utilize the Kalman-filter model to examine herding through time variation. From the findings, the study proves that there exists a correlation between dynamic herding and the prevailing stock prices. The results are also consistent with the trading hypothesis on positive-feedback.

There is a tendency for investors to reduce their level of herding behavior as the extent of uncertainty increases. This is true regardless of the source of the uncertainty, locally as determined through conditional variance or internationally as measured by implied volatility index of the American stock market.

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About FinanceGAB

Ajeet Sharma is a financial blogger and I am blogging since 2017. Financegab is a personal blog dedicated to personal finance. The main aim of this blog to help people to make well-informed financial decisions.
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