Empirical analysis of herding behavior has drawn extensive attention in behavioral finance literature in recent years. Basically herding behavior is termed as convergence behavior, when market participants tend to suppress personal beliefs to follow the bandwagon in trading assets. It’s a behavior considered unlikely rational in view of personal preferences in portfolio building, returns expectations and investment horizon; resulting in driving away assets prices from its intrinsic value (Galariotis et al. [1] ; Nofsinger and Sias [2] ), and this divergence in pricing results in creating arbitrage opportunities to earn abnormal profits. The long term implication of herding may be rather alarming! As assets fail to converge to its fundamental value as herding persists in market segments, leading to inefficient and destabilized markets.

Existing substantial literature has primarily focused on the potency of herding behavior on stock markets and reported mixed findings in support of herding behavior (Bekaert et al. [3] ; Galariotis et al. [1] ). However, there is paucity of literature on testing herding behavior in emerging markets, as these markets are fast integrating into global financial system, but significant void exists in market and institutional development. In lieu of this, the primary motive of the present study is to bridge the existing gap and provide complete picture of herding behavior in the context of Indian emerging market settings.

The paper attempts to contribute to herding literature in a variety of ways. First, investigating herding behavior in an entirely different class of assets; namely equity index futures. Second, it studies herding behavior following the macroeconomic news announcements, during periods of market stress and spill-over’s from other market segments. Third, by studying herding in futures market, as futures market contains information about expected future prices; and hence contributes to return predictability of the underlying asset. Fourth, helping to resolve some of the mixed findings in herding behavior and lastly, to best of our knowledge, this is the first attempt to study this phenomenon in India particularly in futures market as financial engineered assets have seen a quantum leap in terms of trading volume across global financial markets.

Using the methodology as adopted by Christie and Huang [4] , we presented evidences of herding behavior around the announcement of macroeconomic news releases, in periods of market stress, during extreme low (high) trading volume, and spillovers’ from other market segments as reported in the previous study of Galariotis et al. [1] . The remaining paper is organized as follows. Section 2, presents a brief literature review; Section 3, describes the model specification to detect herding behavior in index futures; Section 4 provides the data; Section 5, presents empirical results and lastly summary and conclusions are presented in Section 6.

Numerous studies have attempted to understand herding behavior in financial markets (Banerjee [5] ; Bikhchandani et al. [6] ; Welch [7] ) reporting that market participants mimic each other’s actions i.e., engage in herding disregarding personnel beliefs (Cipriani and Guarino [8] ). Lot of academic rigors have gone in to understand herding behavior as Hwang and Salmon [9] argued that herding violates the propositions of efficient market theory, drives asset prices away from the equilibrium as considered by traditional finance theory and that the prices no longer reflect the true valuation of firms, intuitively resulting in a behavior which may cause financial bubbles in stock markets (Banerjee [5] ).

In order to understand herding behavior, previous literature has classified herding as; unintentional herding and intentional herding. Bikhchandani and Sharma [10] argued that the former state refers to situation when investors converge to consensus sharing similar set of signal’s to make similar investment decisions (Hirshleifer et al. [11] ) whereas intentional herding is the consequence of investors overtly disregarding personal beliefs to infer from the trading activities of the others in the anticipation that they share superior private information (Shiller et al. [12] ). Moreover, studies by Hirshleifer and Teoh [13] and Hwang and Salmon [9] , argued that intentional herding tend to destabilize asset prices and impair the proper functioning of financial markets.

While herding is explored in different markets but the results of the studies are far from being homogenous. Gleason et al. [14] studying nine different exchange traded funds (ETFs) in US markets provided no support for herding behavior whereas studies in emerging markets by Chang et al. [15] posited significant herding behavior in South Korean and Taiwanese markets and whereas to lesser extent in Japan. Chiang and Zheng [16] tested herding behavior in 28 different markets found evidences of herding in many advance economies and Asian countries exception being the US and the Latin American markets. Blassco and Ferreruela [17] examined herding behavior in seven different countries and found supporting evidences of herding behavior only in Spain among the sampling countries.

Further, empirical studies by Galariotis et al. [1] for leading U.S and U.K stock reported herding behavior by US investors in periods of release of macroeconomic information and the herding spill-over from the U.S to the U.K in periods of turmoil’s. While Borensztein and Gelos [18] study on mutual funds of emerging markets, provided evidences of herd behavior, as result of different market conditions, whereas Zhou and Anderson [19] investigated herding behavior using quantile regression in US real market reported that investors herd under turbulent market conditions, in addition, they found asymmetric effect of herding in declining markets than in rising markets. Table 1 presents a comprehensive review of herding behavior from different markets.

Like most of the former studies, herding behavior towards market consensus was analyzed using cross-sectional dispersion of returns (Christie and Huang [4] ; Galariotis et al. [1] ). Though these measures were explored for herding effects in stock markets not in futures market; and in order to accommodate for some of the unique market microstructure of futures market which sets them apart from that of the stock markets, we have modified the established methodology for the study. As posited by Christie and Huang [4] when stock returns herd around the market consensus, the returns dispersions should be moderately low and under hypothetical perfect herding conditions all stocks offers exactly the returns as that of the market index i.e. R i , t = R m k t , t . Using similar analogy, we tested herding behavior in stock index futures by calculating cross-sectional absolute deviation (hereafter CSAD) as

C S A D t F = ∑ t = 1 N | R t S , F − R m k t , t I , F | n (1)