Foreign Institutional Investor (FII) categorically defines an institution established or incorporated outside India that proposes to make investment in Indian bourses. These foreign origin firms are registered as FIIs according to Section 2 (f) of the SEBI (FII) Regulations 1995. FII as a category stayed till 2014, and posts that the category has been renamed as Foreign Portfolio Investors or FPIs. Three independent but similar entities have been clubbed to construct the new entity called “FPI” (FIIs, Sub Accounts and Qualified Foreign Investors were merged). Traditionally it has been observed that in India, that FIIs used to buy a little lesser than 10% of a registered company’s stake, and on the other hand FDIs used to pile up stake in excess of 10%.

There was no such rule, prevalent in India till Union Budget 203-14, until the Finance Minister came out with a clear differentiator of FII/FPI and FDI, as 10% and below stake holding buy a qualified foreign entity will be considered as FII/FPI and more than that has to be automatically considered as a FDI. FPIs are heavy investors in the securities market, including the OTC segment and mostly trade in shares, debentures, futures and warrants. The regulations for FII/FPI in India have been framed by the RBI as Sections 6 and 47 of the Foreign Exchange Management Act (FEMA), 1999 and notified vide Notification No. FEMA 20/ 2000-RB dated 3rd May 2000 viz. Foreign Pension Funds, Mutual Funds, Investment Trusts, Sovereign Wealth Fund, Foreign Central Banks, Insurance and Reinsurance Companies of foreign origin can be registered as an FPI. All the relevant data and daily statistics of FPI are maintained by Security Exchange Board of India (SEBI). Buoyed by the sustained and strong support from the FII/FPIs the Indian bourses enjoyed unprecedented bull rally for almost three quarters of a decade in the early 2000s. FIIs were predominantly return chasers in the short run, as proved by eminent researchers globally; however they changed their game plan completely to eradicate the unnecessary risk associated with their day trading. This study investigates the FII/FPI influence under a rationally defined time and space domain in Indian context post global crisis of 2008.

LITERATURE REVIEW:

There are many a relevant articles that are found. Some are on volatility measurement of FPI, classification of FPI, Pattern of FPI inflows, relation with the broad based indices, regulation impact, causality link with stock returns and FII flows, FDI and FII comparative study etc.

Gupta (2011) and Saraogi (2008) have examined the causal link within FII flows with market returns in India, Pal (2005) did similar kind of study in an event driven approach focussing on 2004 general elections in India. Rai and Bhanumurthy (2007) tried to investigate causal link between FII inflow, stock market returns and two different types of inflation. However they could not establish any causal link between them. Their study however had a complexity of multi-colinearity as different types of inflation were in contention. Kumar (2009) tried to find long run equilibrium between NSE movements and FII flows. He found that stock returns do Granger cause the FII flows.

The instant impact of market venting to FIIs is the paramount growth in trading volume and capital inflows to domestic bourses, resulting a subsequent boom in the stock prices, individually or collectively. Capital markets do not necessarily go up and above all the way during this phase of capital infusion. According to Calvo and Mendoza (2000) it goes up rapidly in the initial phase, but consolidates and corrects itself even when it receives substantial capital from the FIIs. Henry (2000) however finds the two sets of possible and plausible consequences of market liberalization with the available asset pricing models. First after effect of market liberalization (as it is impacting the cost of capital) is a surge in a country’s equity prices as market understands that domestic markets will liberalize more in days to come. Secondly it impacts on physical investment that will increase due to the fall in cost of capital. New and growing up companies will start more and more investment projects. Economic growth will surely be fuelled by this second parameter. Gompers (2001) on the similar trail proved that institutional investors invested in liquid and blue-chip stocks having low returns during the past years. This entire phenomenon will work smoothly if the demand and the supply curves of the said stock or index become and stay as an inelastic subject. Han (2004) in his innovative work have studied the impact of FIIs on the stock prices in a new and different way. Since the FIIS need to maintain a time line for holding scrip, so despite having a good news or a bad news they cannot sell or buy the stocks at will. So the higher the constraints to buy and sell higher will be the price-driven momentum on that stock. Lin (2006) concluded that sticky funds on the higher side from FIIs do play a favourable role in their growth and even are considered to be good for momentum.

Griffin (2004) did find that FII flows are considerable predictors of returns for Korea, Taiwan, Thailand and India. Not too many researchers point out a structural break in 1998-99 Asian crises when FII went down, before and after which are radically different in comparison. Rajput and Thaker (2006) has found that no long run positive correlation exists between markets and exchange rates in Indian context except for year 2002 and 2005.

Sethi (2007) and Mittal, Agarwal (2012) compared FDI and FII relative participation in the economic growth. Coondoo and Mukherjee (2004) tried to strike a common chord between call money rates, stock returns with FII. Certain research gaps were identified by the researcher. Coondoo, Bose and Mukherjee (2002) did an in-depth causal link identification of FII movements (1992-2002 phase) with relation to Sensex, SandP500 etc. They found, weak link between the variables, they identified return-chasing pattern of FIIs, high level of autocorrelation in the data (which makes it non-stationary and thus difficult to predict) and lastly they found that FII sale is on account non-performance of Indian firms. Loomba (2012) administered a longitudinal correlation study of certain significant time zone data (top 25 crash data) of Sensex and FII flows to confirm a correlation. They worked extensively on the path shown to calculate the FII flow to India by Chakrabarti (2001). They have considered FIIN as a sus-set of MCAP (further mentioned by Ratio_FIIN). Coondoo, Bose (2004) worked on FII pre regulation and post regulation phase to investigate the impact of regulations on the average FII flow. They found a structural break post Asian crisis phase.

Ghosh (2012) conducted mathematical test to find out the linkage between the broad-based SandP BSE 100 with FII and DII flow. The results showed insignificant effect on the part of FIIs.

Research gaps leading to the cardinal research question

Firstly no study has been found post 2008 crisis, trying to delve out the plausible rationale of FII/FPI behaviour, no study has been focussed on blue chip stocks as a cluster; no study has been conducted on a market cycle where rebuilding phase is going on post carnage, very few studies considered Panel Data approach, they either followed time series, or cross sectional data set. However rationally it has been quite evident that this kind of study is a combination of both cross-sectional and it has got an important time series dimension too. Last but not the least, it has been found that all the studies so far have taken only FII data, whereas currently the FPI universe data structure may throw some light in some unchartered territories of pattern recognition.

RESEARCH METHODOLOGY:

Data of 2009 to 2012 time period has been considered for 29 out of 30 Sensex stocks. Day wise data of FPI inflow and outflow has been in use. The total dataset that has been used were 145000 (29 stocks, 5 observations, 1000 days). The three fixed layers of this Panel data work has been the Years, the stock prices (at the end of the year) and the net FPI flow (at the end of the year). Stock wise, day wise and flow wise in-depth data analysis has been conducted in this study. Since, that time FII data was available separately along with Sub Accounts and Qualified Foreign Investors data, so in order to generate FPI data, all these categories were added and the data set were created. To increase efficiency and decrease the standard error factor, both FPI data and Stock data at the functional level has been converted as LOGs. The sole intention of the study is to search for any relation or pattern identification among FPI net flow and Stock prices, and then subsequently build a robust model to predict it within given set of constraints. As 2008 was there year of global crisis, so naturally in the following years, or recovery years the FPIs will definitely have a well-directed action plan to make amends in order to cut risk and optimize profit. Rationally speaking when the going is comfortable, neither the investor nor the companies dig deep to investigate, but the plot reverses dramatically and action plans are often formed more as a knee jerk reaction. So, 2009 to 2012 probably are quite apt from the perspective of studying the FPI investment behaviour. In a bull market, FPIs target the Small and Microcaps, and the when the going gets tough, historically they fall back upon the large caps, as they are traditionally the first one to recover. Big stocks often observe a herding behavioural pattern among all types of investors including the FPIs during this kind of recovery period. So, BSE 30 or Sensex has been chosen. Out of 30 stocks, Zee Telefilms had certain missing data points, so, it has been excluded. Within Sensex the top 29 stocks with complete data set were taken in to consideration. Panel Data Regression has been carried out with Stock L as dependent variable and FPIL as independent variable. Fixed Effect Panel Data model method has been chosen, as both the cross section as well as the time series are well defined and fixed in nature. Since the stochastic behaviour and the random movements reached nadir during 2008-10, hence the distribution pattern of both stock price and FPI net flow should ideally be covered in a narrow band within the same time period, so instead of Random Effect, Fixed Effect Model has been preferred. Also heterogeneity as well as heteroscedasticity is also linked to the base effect of the capital markets along with the different strategies (rational or rather irrational) of the FPIs. After such a huge crash (2008), herding is the most common behavioural pattern that becomes evident among FPIs, that too makes the distribution pattern range-bound within a narrow range.

Ho: FPIL cannot determine StockL

Ha: FPIL can determine StockL

STUDY OUTPUT:

Table 1.1

Variable	Coefficient	Std. Error	t-Statistic	Prob.	Occurrence
FPIL	0.1319	0.048824	2.701806	0.0083	99.17%

Table1.2

R-squared	0.928965	Mean dependent var	2.67827
Adjusted R-squared	0.905011	S.D. dependent var	0.416863
S.E. of regression	0.128479	Akaike info criterion	-1.04811
Log likelihood	90.79046	Schwarz criterion	-0.33598
Log likelihood	90.79046	Hannan-Quinn criter.	-0.75903
Prob. F-statistic	0	Durbin-Watson stat	2.031926

The Panel Data Regression Model is

StockL=1.2179+0.1319*FPIL

Diagram 1

INTERPRETATION:

Probability of FPIL is 0.0083, that means 99.17% times FPIL will be able to define StockL. As P Value is lower than 5%, so Ho cannot be accepted, on the contrary Ha will be accepted.

However the impact of the prediction will be relatively feeble as the coefficient is around 0.1319. R Squared is around 92% (important to note here, as the control variable is single here in this study, so, Adj. R Squared has not been in consideration) and Durbin Watson is 2.03 signifies the Panel Data Regression with Fixed Effects is quite apt to define such a phenomena with relative more accuracy in prediction and the regression is surely not Spurious in nature (when R²> DW, then the regression becomes spurious in nature). Also, DW value indicates that the impact of Autocorrelation will not affect this model. That too makes it robust. Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) are quite low in value. The lower AIC or SIC is the lower is the volatility of the model. This is turn confirms the solidity and the high chance of predictability of this model. ANOVA F Stat is more than 1, means the MSR >MSE and the Probability is zero, confirming rather reaffirming the strength of the model. RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) are quite low and the forecasting is quite symmetric in nature.

CONCLUSION:

After obtaining the results it is clearly evident that on the onset of the global recovery (that started in 2009 and somewhat got over by 2012) the FPI oligarch community did follow a clear strategy while investing in the large cap space (SandP BSE 30). They shifted from their traditional approaches to a large extent and focussed on a more rational and safer approach based on an unknown algorithm, which could surely be defined as one of the most accurate econometric models for defined financial engineering. This study confirms a clear, rational, calculative, mathematical way, which was used by the FIIs during this phase of regeneration. Also, the de-coupling of Indian economy with the emerged western economy helped them to diversify their troubled portfolio and must have helped them to reduce their overall risk level.

LIMITATIONS OF THE STUDY:

This study is selectively conducted for Sensex stocks, random-effects were not considered, balanced panel is used instead of Generalized Method of Moments (GMM), confined to only post-crash rehab-phase, considered herding as an assumption under given circumstances.

REFERENCES:

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Received on 01.12.2016 Modified on 15.12.2016

Asian J. Management; 2017; 8(1):107-111.

DOI: 10.5958/2321-5763.2017.00017.8

In search for FPI trail in blue-chip Indian bourse during a phase of rehabilitation- An investigative study