1 Introduction

The enactment of SEBI Act of 1992 and setting up of SEBI (Securities and Exchange Board of India) as a regulatory authority together with the structural reforms that the economy underwent in the post 1990s have vitalized the activities in the Indian stock market. Technological upgradation in trading mechanisms and practices, reforms in the disclosure standards, introduction of rolling settlements, regulation of merchant banks, portfolio manager, brokers and sub-brokers, capital adequacy reforms and corporate registration of intermediaries, entry of FIIs, mutual funds and funds, promotion of retail trading, reforms in clearing and settlements, measures of investor protection, opening of derivative trading and other various measures taken up during this period have brought in the higher level of transparency and have raised Indian securities market to comparable standards in both the developed and emerging market economies. The total market capitalization of the Indian market which was Rs. 507272 in March 1996 has surged to Rs. 18785799 by March 2016. The market has witnessed an increase in the listings, coverage of sectors and in types of investment instruments traded as well.

The participation of different categories of investors with varying sophistication is expected to bring in rich information content into the prices and efficient price discovery in the market through their trading activities. But investors enter the market with different motives such as to trade on the information that they possess, to speculate on the market information, risk sharing, to profit on the rumour or noise in the market, for the requirement of liquidity. Trade volume is the channel by which all the investor motives enter the market and further into the prices determined. Indian market in the post-reform period has witnessed entry of various types of investor categories, and the research studies on the price efficiency in the market have come out with different observations. There are studies that support the random walk hypothesis or price efficiency in Indian equity market (Gupta 1990; Singh and Kumar 2009; Mishra and Mishra 2011; Nalini 2015). At the same time, there are other studies that have reported significant dependence or inefficiency in return series from both BSE and NSE (Pant and Bishnoi 2001; Poshakwale 2002; Pandey 2003; Gupta and Basu 2007; Mehla and Goyal 2012; Garg and Varshney 2015). The market has also experienced a high level of volatility in returns, especially in the post-financial crisis of 2008.

Therefore, an investigation into the trading behaviour of investor categories and its impact on the market is essential which would shed light on the nature of return formation in Indian equity market and would benefit both the investors and regulators to carry out their trading strategies and policies more effectively to bring in transparency and efficiency into the system.

2 Brief Survey of Literature

2.1 Trade Volume, Return and Volatility

Since trade volume is the channel through which investors behaviours are reflected in the market, this sections focuses on the findings of research studies on the linkage between trade volume, returns and volatility in the market. There are a number of arguments in line with the volume return and volatility relationship in the literature. The mixture of distribution hypothesis (Epps and Epps 1976) on trade volume–return relationship pointed out that the movement of returns would be in accordance with the flow of news that is an outcome of the above-average trading activity. This hypothesis gives no space for volatility–volume relationship as they change simultaneously with the news. Information arrival hypothesis (Copeland 1976) portrays information flow in the market in sequence or in random, but it is the difference in the time of acquiring this information across investor categories that leads to correlated volume return movement in the market. Suominen (2001) proposed a theoretical model of volume return relationship wherein dependency in the price variability in the market is considered as the outcome of other traders adjustment of trading strategies with respect to private information content in the past trade volume of informed traders.

There are numerous studies that examined trade volume, return and volatility relationship empirically. Chordia and Swaminathan (2000) reported trade volume as a significant determinant of the lead lag patterns observed in stock returns. The study also found that the return of portfolios containing high trade volume led returns of portfolios comprised of low trading volume and attributed it to the slow information absorption of low volume portfolios. Mubarek and Javid (2009) observed a significant positive effect of the previous day trade volume on the current market returns and a negative relationship in the case of individual stocks. Similarly, Chandrapala (2011) observed that stocks with low trade volume changes in the past outperforming the stocks with high trade volume changed in the subsequent periods and attributed the relationship to investors’ misspecification about future earnings or risk of illiquidity in the market. Yonis (2014) from their examination of Hong Kong, South Korea, Singapore, Taiwan, and USA observed a positive contemporaneous relationship between return and trade volume across these economies. The study also observed a significant positive effect of trade volume on conditional volatility in the market except in the case of South Korea. In the case of Indian market, Mahajan and Singh (2008) observed a positive contemporaneous relationship between volume, return and volatility in the market and supporting evidences of sequential arrival of information in the market and lack of simultaneous availability of the information to all traders that result in price inefficiency in the market.

2.2 Trading Behaviour of Categories of Investors

The behavioural finance literature points out that individual investor’s behaviour is in line with argument of prospect theory and that the risk-taking behaviour of the person varies with uncertain outcomes in both positive and negative domains. Under uncertain situations, the decision makers resort to heuristics such as representativeness, availability and adjustment and anchoring, by assessing their similarity to judge the probability of the outcomes and to reduce the complexity. This dependence on heuristics leads to violation of Bayesian updation and to biased decision as they ignore prior probabilities and sample size in formulation of subjective probabilities (Tversky and Kahneman 1973, 1974, 1979, 1983). This behavioural traits cause inefficient investment decision making and inefficiency in price discovered in the market. Wood and Zaichkowsky (2004) in a survey-based study observed segmentation of investors which resulted in different levels of trading behaviour in the market. In the case of Indian market, Sashikala and Girish (2015) reported that factors like broker’s advice, personal analysis, the current price of equity stock, analysts’ recommendations play a significant role in moulding the trading behaviour of retail investors in Indian market.

Karolyi (2002), on the trading behaviour of foreign, local institutions and individual investors in Japanese equity market, observed positive feedback trading or following of momentum trading strategy by FIIs and contrarian trading strategy by the Japanese banks, financial institutions and investment trusts and companies. Richards (2005) reported FIIs following momentum strategy in emerging market economies in Asia extracting information from the emerging markets, and it results in larger level changes in the prices of such markets.

In the case of Indian market, Sehgal and Tripathi (2009) reported FIIs evincing different trading behaviours depending on the data analysed. They were found to be following positive feedback trading strategies in monthly pattern, while in the case of quarterly pattern the behaviour found to be herding at the aggregate level. Phansatan et al. (2012) examined investor-type trading behaviour in the Thai stock market. The study observed that foreign institutional investors following positive feedback trading and momentum strategies which results in superior short-term market timing but adverse security selection performance than local investors which ultimately leads to cancellation of overall net trade gains. Proprietary traders were found to be profiting from their liquidity provision role to the markets with short-term market trading gains. Individual investors were found to be following herd behaviour and gains from security selection at the expense of other investor categories which often get cancelled out with their poor market timings. Kamesaka (2013) in the analysis of investor behaviour of Japanese market post-Great East Earthquake observed institutional investors taking contrarian investment strategies and FIIs following momentum trading strategies.

From the previous sections, we learned the significant relationship between trade volume, which is an outcome of the combined behaviour of investor categories participating in the market, and return and volatility formation in the equity markets. Since trade volume is the aggregate reflection of all the investor categories in the market, it would be informatory to identify the commonalties in the trading behaviour of all investor categories and further examine how each of these is related to economic fundamentals, irrational sentiment and equity market indices’ returns based on different characteristics. This type of decomposition of aggregate trade activities in the market would help us unearth the hidden characteristics in the trade volume and each of investor categories absorption to it which ultimately affect the return formation in Indian equity market. This type of approach in analysing investor-type trading behaviour is scarcely found in the literature which is carried out in this work.

3 Data and Methodology

The study is carried out in the Indian equity market based on the data drawn from both BSE and NSE for a period spanning from 2004 to 2016. In order to examine the intrinsic characteristics of the trading behaviour of investor categories, we relied upon the net trade data of FIIs, DIIs, NRIs, client trading, proprietary trading, banks and insurance companies. Even though DIIs net trading data comprise those of banks and insurance companies, we have considered both of them in the analysis. The data are of monthly frequency spanning from April 2004 to December 2016. Principal component analysis technique is carried out to extract the common variation in the variables and indices representing statistically significant common components are constructed based on the respective loadings of the variables into it. These indices were re-examined with the variables such as money supply, index of industrial production, trade balance and an irrational sentiment index to examine their association with economic fundamentals and irrational sentiment present in the market. Irrational sentiment index we have used is the similar one after modification that is prepared by us (Suresh and George 2016). Similarly, causal relationship between these indices and BSE Sensex, NSE Nifty, BSE 500, NSE 500, BSE Large Cap, BSE Mid Cap, BSE Small Cap, NSE Mid Cap and NSE Small Cap index returns is also examined.

4 Empirical Results and Discussion

4.1 Identification of Common Characteristics Across Investor Categories

Table 1 presents the correlation coefficients of the monthly net trade position of the investor categories. The coefficients indicate the degree of linear correlation between the variables. These coefficients with their sign show the level of association and direction of movements between them. It is observed that client trading activities are positively associated with those of FIIs, banks, insurance and DIIs, while they move inversely with NRI and proprietary trading activities. In the case of NRI trading, all investor categories except DIIs show a positive association with their trading activities. Proprietary trading activities were found to be in the inverse direction with those of FIIs, banks, insurance, DIIs, indicating contrarian trading by them to these categories. FIIs showed a positive association with those of all categories except proprietary trading. Banks, insurance and all DIIs showed a positive association with those of other investor categories except those of proprietary traders.

Table 1 Pearsonian correlation coefficients across net trade ** of investor categories
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In order to extract the common variations in net trade position of the investor categories, we applied principal component analysis technique. Table 2 presents the seven unobservable components that explain the variations in the trading activities of investor categories in Indian equity market. The first row shows the eigenvalues of each component, which depicts the common patterns in the data. Variance proportion indicates the level of variation in the entire data explained by the particular component, and the cumulative proportion shows the cumulative proportion of variance explained by each component about the patterns in the system of data. Since only the first three components have the eigenvalue above one, that together explain 68% of variation in the data, we consider only those ones in the further analysis.

Table 2 Common Variations in the trading behaviour of investor categories
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Table 3 shows the loadings of each investor categories into the patterns observed in the data. In the case of component 1, we observe that except NRI and bank trading activities whose loadings are significantly small all other categories contribute to it. It is dominated by the FIIs, DIIs and proprietary traders. In the case of component 2, the dominant contributors are NRIs and insurance companies followed by client trading activities that is trading through intermediaries mainly individual investors, while DIIs, banks, proprietary trading activities are significantly low. Finally, in the case of the third component, banks, NRIs and insurance companies contribute more than other investors, but the former two including client trading are taking negative values in it.

Table 3 Contribution of each of the variables** into the unobservable characteristics
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The analysis reveals a pattern of intrinsic characteristic inherent in the trading behaviour of investor categories in the Indian equity market. But this does not give us clarity of what this pattern represents in the real market. Therefore, we constructed indices of each of the characteristics after eliminating the statistically insignificant variables.

4.2 Common Characteristics of Investor Categories Trading and Economic Fundamentals

Table 4 presents the direction and degree of association of each of the components with the macro-economic fundamental variables. The results show that none of the trading characteristics of investor categories in Indian market are associated significantly with economic fundamentals except the case of commodity prices, that is of gold and silver, which shows a negative association with first two components of the trading behaviour and money supply in the case of component 2. The table also presents the absence of association of irrational sentiment index constructed by us (Suresh and George 2016) to economic fundamentals in the Indian economy. We have already observed that although all these components explain around 68% of the common variations in trading behaviour, none are related to economic fundamental which indicates the disconnection of trading activity by investor categories with the performance of the economy.

Table 4 Linear association of trading characteristics with economic fundaments
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4.3 Investor Categories Trading Behaviour and Irrational Behaviour in Indian Equity Market

Trading in Indian market is found to be not associated with movement of economy’s performance. In this section, we examined their association with irrational sentiment prevailing in Indian equity market measured by irrational sentiment index constructed on the basis of various sentiment measures from the Indian equity market. It is observed from Table 5 that component 1 is highly associated with the sentiment in the market. It is clear from Table 2 that this component is heavily loaded positively by the commonality in client trading, FIIs, banks, insurance and DIIs, while negatively loaded by proprietary trading. Similarly, we learn from Table 2 that the first component explains 38% of the common variation in the market. Therefore, it can be concluded that all the investor categories in the Indian market partially brings in noise into the trading activities and contribute to the inefficiency in prices in the market.

Table 5 Irrational sentiment in the equity market and investor trading behaviour in India
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4.4 Trading Behaviour of Investor Categories and Major Indices in Indian Market

Table 6 presents the linear association of trading characteristics with the major indices in Indian equity market. It covers benchmark indices, broad market indices and indices based on market capitalization. It is observed that the first component which was found to be highly correlated with market sentiments is showing a relatively high negative association with all the indices returns except BSE Large Cap, while the second component does show any significant relationship with them. On the contrary, the third component shows a positive association with the index returns in the market.

Table 6 Association of trading characteristics and major indices in Indian market
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One contradicting observation from the analysis in Table 6 is that the component which is significantly positively related to the irrational sentiment in the market and negatively associated with economic indicators though not significant showed a negative association with all the major indices in the market, while the third component which showed a negative relationship with irrational sentiment and a positive association with economic sentiment shows a positive relationship with most of the major indices returns in Indian equity market. FIIs, DIIs and proprietary traders (negative) are the major contributors to component 1, while banks (negative), NRIs (negative) and insurance companies contribute more than other investors in component 3.

Therefore, it can be inferred from the analysis that FIIs, DIIs and client trading activities are much associated with irrational sentiment in the market which refers to noise traders in the market but following contrarian trading strategies, while proprietary trading takes opposite position in the market by trading against these groups but indirectly increases the errors in the market in the guise of market making. Insurance companies are the group which trade in line with economic fundamentals and positively influence the movements in Indian equity market.

5 Conclusion

From the analysis, we have observed that the trading behaviour of investor categories is quite complex in Indian market. FIIs, DIIs and individual investors are found to be contrarian noise traders in the equity market taking position against domestic institutions, especially against insurance companies. NRI investors and banks are found to be profiting from trading against the movements of the economy.