A study on Factors Influencing the Herding Behavior of Indian Stock Market

During the previous several decades, the Indian stock market's expansion and potential attracted a large number of investors. In the stock market, investors exhibit herding behaviour and often attempt to imitate others' actions or conclusions. The majority of empirical studies on herding behaviour in emerging stock markets found that it exists at least in distinct states of the market. Identifying the factors of herding behaviour will assist in explaining a variety of behavioural concerns that occur among investors, and the study will provide further clarity and throw light on the peculiarities of herding behaviour in Indian stock markets. There is enough data in the literature to suggest that a variety of variables impact the herding behaviour of investors. The goal of this chapter is to identify and show the many elements that govern herding behaviour in the Indian stock market across the research period. It also discusses how investors digest available data and make their investment choices appropriately, as well as how to recognise the market's unstable factors. This research considers the involvement of both business fundamentals and market conditions as the regulating source of herding behaviour in the Indian stock market, which is a developing market (1). A number of empirical investigations have shown the presence of a link between herding behaviour and other parameters. Only a few research have looked at how macroeconomic conditions in the stock market influence herding behaviour. Furthermore, as compared to big organisations, small enterprises are less transparent due to their restricted information availability. However, the research on herding behaviour has inconsistent results on the size component, with herding toward both small and big stocks being seen. In addition, when comparing industrialised and developing nations, it is discovered that the variables influencing herd behaviour vary. It's also worth noting that the outcomes in the emerging market vary from one another. An in-depth examination of herding behaviour in the Indian stock market can benefit investors and practitioners in a variety of ways, including a better understanding of the destabilising implications of these elements in the markets and further insight into the driving drivers of herd behaviour. By evaluating numerous variables discussed in the following section, it is necessary to conduct an analysis utilising various criteria in order to determine the causes of herding behaviour in the Indian stock market and hence the origins of herding (2).

The research used the following approach to determine the impact of different variables on herding behaviour.

• Using the state space model, calculate the herding measure using the log cross-sectional standard deviation of beta of the market component (using the kalman filter approach).

 Collect the variables that will be studied.

the log values of the two series.

• Calculate the monthly market volatility using Schwert's (1989) technique.

• Transform the series into a log format.

• To normalise the data, divide net institutional investment, net foreign institutional investment, and net domestic institutional investment by the BSE market capitalization.

• Convert the information (volume) to a log format.

• Preliminary analysis should be done using descriptive statistics and a line graph.

• ADF and PP tests may be used to determine the stationarity of the variables independently.

• Find the controlling influence of the specified variable on the herding measure using the state space model.

• (both individually and collectively)

The summary statistics of the numerous variables employed in this investigation are explained in Table 1. In the Indian stock market, the mean value and standard deviation of cross sectional standard deviation of beta on market factor are both positive. It also shows the data's apex (3).

Note: * denotes significance at 1%level and **, *** denotes significance at 5% and 10% respectively. Net foreign institutional investment, net mutual fund intervals for all of the series are monthly. CSSD (cross-sectional standard deviation) is an abbreviation for cross-sectional standard deviation. The state space model was used to obtain a time-varying herding metric (4).

Figure 1 shows a quick overview of each variable utilised in the research from April 2002 to March 2012. It aids in the analysis of variable movement over time and data conversion into log form for smoothing. The line graphs vividly depict the differences caused by various market occurrences, particularly the crisis.

MRBSEH is the herding series derived using the kalman filter, and MRBSE indicates the log cross sectional standard deviation of beta. The graph demonstrates that the variance is greater in both situations during the 2007-2008 crisis period (07-08 in the graph).

RBSE is for BSE market return, VOLBSE and VOLUMEBSE stand for log market volatility and log market volume, respectively, and show greater variance during the crisis. The SMB and HML factors net mutual fund investment, and net institutional investment, respectively. EQFII showed no trend, whereas EQMFI and NETBSE showed a nearly identical pattern, but there is more variation during the 2004-06 period, and the withdrawal of FII during the crisis is more visible from the graph (5).

The researchers looked at the impact of market return, market volatility, and market volume independently, as well as all three components combined as a control variable, on herding behaviour. The coefficients of the monthly return on the BSE are insignificant (model 14) when looking at Table 2, showing that market return is not a sensitive factor for investor herding or that market return variations have no influence on herding (6).

The model's findings (15) explain how market volatility affects herding behaviour. The study used log market volatility calculated using the Schwert (1989) methodology, and it can be seen that the estimates of the herding measures are significant at the 1% level, as is the coefficient of the volatility factor, and the negative sign explains that the log cross sectional dispersion of the beta increases (decreases) as market volatility decreases (increases). Even after adjusting for volatility, the data imply that herding is still a factor. It's also worth noting that the value of dropped from 0.27 to (0.13), and the negative coefficient

The impact of foreign institutional investment, the function of mutual fund investment, and the combined effect of both in promoting herding behaviour in the Indian stock market were also examined. The analysis is done using the net FII flow and net mutual fund flow to the market, and the research employed monthly net investment flows and model (19), (20), (21). (both the factors are normalised by dividing with market capitalization of the index). The effect of net institutional investment was also investigated, and the results of multiple models (19), (20), and (21) are reported in Table 4.3. The table demonstrates that herding occurs in the Indian stock market, since both and are very significant (at the 1% level), with a signal percentage of approximately 54 percent. The considerable persistence factor, , explains that herding behaviour exists in the analysed markets, and the mutual fund estimate is negative herding behaviour. When comparing and , it is obvious that there is little fluctuation in which explains why FII movement has a less impact on herding behaviour in the examined market. Furthermore, the positive coefficient of net FII investment explains why rises with the degree of net FII flow, indicating that herding diminishes as net FII flow rises (7). As a result, it may be argued that net foreign institutional investment has a negligible impact on herding behaviour. Furthermore, the net flow of institutional investment (NET) is positive and small, implying that net institutional investment has little power on herding behaviour in the market under consideration. As a result, institutional investors do not have a significant influence on herding behaviour in the Indian stock market.

Figure 4 depicts the development of herding behaviour as a function of net FII flow into the market under consideration. On the left axis, EQFIIBSE is the net flow (normalised with the index's market capitalization) and on the right axis, FIIBSEH is the extracted herding measure. It is difficult to discern a specific trend or pattern of herding behaviour from the figure, which shows a very low level of herding on average (8-10).

The research also included the Fama French factors SMB and HML as explanatory variables in its analysis. The research followed Fama and French's (1993) and (1995) style method, and Table 4 shows how the SMB and HML factors, or size and value factors, explain the link between herding. The research discovered that none of the parameters produced significant results, indicating that HML and SMB had little influence on herding behaviour in the Indian stock market. The research also looked at the combined impact of SMB and HML based on the size and value component, and

ϕm 0.419666** (2.2654) 0.461649*** (2.6110) 0.436862** (2.37084 ) µ -2.57916*** (-44.177) -2.56359*** (-42.9332) -2.57231*** (-43.9072 ) σm,n 0.172116*** (2.6041 ) 0.168357** (2.4823 ) 0.269257 **

(2.50636 )

σm,v 0.183373*** (5.4353) 0.18335*** (5.6109 ) 0.431574*** (5.55216 )

SMB 0.378694 (1.605) - 0.263435 (1.1198) HML - 0.346982 (1.4845 ) 0.166333 (0.71272)

Proportion of Signal 0.321765 0.314736 0.321765 Maximum likely hood values

18.5072 18.3149 18.6364

The most prevalent behavioural bias that has an effect on financial markets is herding behaviour. Investors who rely on word-of-mouth for the majority of their financial information are more likely to exhibit herding behaviour than those who rely on any other kind of information. Investors with the same racial or ethnic background tend to behave in a similar way. According to the findings of this research, men exhibit less herding bias than women. Compared to youthful stock investors, older investors showed a greater tendency to follow the herd. Investors' marital status had just a little effect on the perception of herding bias.

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Research Scholar, LNCT University Bhopal