Market Timing Skill of Foreign Portfolio Investors in India

Market Timing Skill of Foreign Portfolio Investors in India

Accepted Manuscript

Market Timing Skill of Foreign Portfolio Investors in India Dr K N Badhani, Dr Ashish Kumar PII: DOI: Reference:

S0970-3896(17)30067-8 https://doi.org/10.1016/j.iimb.2019.07.012 IIMB 345

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IIMB Management Review

Received date: Revised date: Accepted date:

19 February 2017 29 November 2017 15 July 2019

Please cite this article as: Dr K N Badhani, Dr Ashish Kumar, Market Timing Skill of Foreign Portfolio Investors in India, IIMB Management Review (2019), doi: https://doi.org/10.1016/j.iimb.2019.07.012

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Market Timing Skill of Foreign Portfolio Investors in India Dr K N Badhani

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Dr Ashish Kumar

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Abstract

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This study examines the market timing skill of foreign portfolio investors (popularly called foreign institutional investors or FIIs) in India as a group. A simulated portfolio is constructed that invests in market index replicating the patterns of aggregate net FII flow. Similar portfolios are constructed replicating the trading behaviour of domestic institutional investors (DIIs) and investors of other categories. The performance of these portfolios is compared with the performance of two benchmark portfolios constructed based on buy-and-hold strategy and systematic investment strategy, respectively. The results based on bootstrapped modified internal rate of return (MIRR) of investment portfolios suggest that the FII portfolio does not outperform the benchmark portfolios. On the contrary, the average returns of benchmark portfolios are significantly higher than the average return of FII portfolio. FII portfolio does not outperform the portfolios representing investment strategies of DIIs and other investors also. Similarly, FII strategy does not outperform two benchmarks for forty-two selected securities at individual level. Therefore, our results suggest that the FIIs do not have a superior market timing skill.

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Key words— foreign portfolio investors, market timing skills, modified internal rate of return, bootstrapping

∗ Professor, Accounting and Finance Area, Indian Institute of Management Kashipur, Kundeswari, Kashipur (Udham Singh Nagar), Uttarakhand-244713, India. e-mail: [email protected], mobile : +91 9759108565 † Assistant Professor, Accounting and Finance Area, Indian Institute of Management Kashipur, Kundeswari, Kashipur (Udham Singh Nagar), Uttarakhand-244713, India. e-mail: [email protected], mobile: +91 8192097311

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Contents 1 Introduction

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2 Data and Methodology 2.1 Data . . . . . . . . . . . . . . . . . . 2.2 Investment Strategy of FIIs and DIIs 2.3 Construction of Portfolios . . . . . . 2.4 Calculation of Return on Portfolios .

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4 Conclusion

List of Figures

Cumulative FII/DII Investment viz-`a-viz Nifty . . . . . . . Impulse Response Function . . . . . . . . . . . . . . . . . . Average Returns on Different Investment Strategies . . . . . Performance of FII investment strategy vis-a-vis benchmark Performance of FII and DII investment strategies . . . . . .

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Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic Interaction among the Variables . . . . . . . . . . . . . . . . . . . Returns for Different Investment Strategies for Entire Sample Period . . . . Annual Returns for Different Investment Strategies . . . . . . . . . . . . . . Average Returns and their Variability for Different Investment Strategies . Comparative Performance of Different Investment Strategies at Aggregate Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparative Performance of Different Investment Strategies at Stock Level

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List of Tables 1 2 3 4 5 6

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3 Results and Discussion 3.1 Performance of FII Strategy viz-a-viz Other Strategies Period . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Bootstrap Based Comparison of Performance . . . . . 3.3 Analysis at Stock Level . . . . . . . . . . . . . . . . .

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Introduction

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The role of foreign institutional investors (FII) in emerging markets has been a matter of debate for last several decades both among academic researchers and policy makers. On the one hand, it is argued that they bring valuable capital and foreign currency, reduce cost of capital through base-broadening and risk pooling, and indirectly contribute in improvement of corporate governance, market efficiency and innovation. On the other hand, concerns are raised about destabilizing role of foreign portfolio investment flow due to their cyclic and speculative nature. In India FII-flow is believed to play quite important role in deciding the direction of the market. Several researchers have studied the impact of FII-flow on stock prices and volatility (Dhingra et al., 2016; Hiremath & Kattuman, 2017). Studies have also focused on the impact of FII flow on integration of Indian financial markets with global markets and increased risk due to contagious effects of global shocks on Indian financial markets (Poshakwale & Thapa, 2010; Yaha et al., 2017). This study raises a different but related question - ’how much profit do the FIIs earn on their investment in India?’. This question is important because the investment in secondary market is a zero-sum game and one investor earns profit only at the cost of another investor. If the perception about the FIIs that they have the power to move the market is true, this power can be used by them to manoeuvre the market in their own benefit and to profit at the cost of other investors. Therefore, the profitability of FII transactions have important policy implications. Using a dataset on FII transactions at aggregate and individual firm level at daily frequency, this study first time try to evaluate the profitability of investment strategy of FIIs as a group.

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An active investor may earn profits higher than the profits generated by the market to a passive investor either through superior stock picking skills or through market timing skills. This study focus on market timing skills of FIIs . However, the term ‘market timing’ has used in a liberal sense here as the study not only analyses the profitability of FII investments at broader market level but also evaluate the profitability of their transactions at individual firm level. Therefore, market timing skill is defined as ability to predict future market price movements (of individual stocks as well the market as a whole) and making buy or sell decisions to benefit from those expected price movements. Looking at patterns of their transactions, this study attempts to infer if FIIs had some prior knowledge about (or skill to predict) future price movements and they have bought and sold the stocks to benefit from this prior knowledge. If such knowledge or skill is present, the profitability of FII portfolio will be higher than the profitability of an investor following a passive investment strategy (such as buy-and-hold strategy and systematic investment strategy). Therefore, we simulate investment portfolios replicating the patterns of transactions made by FIIs and compare the profitability of these portfolios with the profitability of simulated portfolios following passive investment strategy. We have also simulated the portfolios replicating the patterns of transactions made by domestic institutional investors (DIIs) and investors of other categories (which mainly comprise of individual investors) and the profitability of FII portfolio is also compared with the profitability of the portfolios representing to these categories of investors. Conceptually the profitability of FII investments involve two dimensions, both of them have been extensively explored and debated in the literature. The first dimension involves the profitability of ‘institutional investors’, and the second dimension involves the profitability of ‘foreign investors’. Institutional investors are perceived to be more informed and skilful investors in financial literature, and often used as proxy for ‘informed traders’ in empirical studies. They have access to better information and greater resources to pro1

ACCEPTED MANUSCRIPT cess that information (Grossman & Stiglitz, 1980; Glosten & Milgrom, 1985; Kyle, 1985). Economies of scale allow them to obtain information from different sources, employ highly skilled professionals and use superior technology at lower cost of per dollar invested. Despite having numerous advantages over individual retail investors, there is no conclusive evidence that institutional investors perform better than individual investors.

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One strand of the literature exhibit that the institutional investors have certain skill set or resources which help them to earn higher return. i.e.(Daniel et al., 1997), Chen et al. (2000) Wermers (2000) and (Kacperczyk et al., 2005) show that institutional investors specifically mutual funds have better stock selection ability which help them to perform better. Lakonishok et al. (1992), Nofsinger & Sias (1999) and Puckett & Yan (2011) are of opinion that institutional investors exhibits superior trading skills. Rubinstein (1993), Kim & Verrecchia (1994), and Kandel & Pearson (1995) suggest that these institutional investors interpret the public news differently which creates value relevant information to them. Bodnaruk et al. (2009), Jegadeesh & Tang (2011), and Acharya & Johnson (2010) state that institutional investors often have direct communication with corporate houses and brokerage firms which give them an access of corporate news ahead of the other investors. Irvine et al. (2007) argue that most of the institutional investors, specifically mutual funds and hedge funds hire buy-side analysts who usually have linkage with sellside analysts which helps them to have better access to market information in time. Yan & Zhang (2009), Barras et al. (2010) and Boulatov et al. (2013) find that institutional investors can predict the future earning which leads them to outperform the market. Campbell et al. (2009) and Hendershott et al. (2015) claim that institutional investors have information advantage over the other investors which help them to earn better.Griffin et al. (2012) find that institutional investors can able to perform better because of their ability to process information in a timely manner.

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In contrast, another strand of the literature suggests that they do not outperform the market after considering the management costs. Carhart (1997) examine the performance of mutual funds over a period of 30 years ranging from 1962 to 1993 in US and find that they don’t possess any superior skill set to earn higher returns. Barras et al. (2010) find that number of mutual funds having positive alpha has significantly come down after 1995. Jegadeesh & Tang (2011) and Griffin et al. (2012) study the institutional investors’ investment behaviour around takeover and earning announcements and find no evidence of information asymmetry among them and individual investors.

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Another dimension of this debate is related to performance of foreign investors in comparison with the performance of local investors. A school of researcher is of the opinion that the foreign investors have less information about the local market in comparison to their local counterparts; so, they face the problem of information asymmetry, which leads to lower returns on their investments (Chan et al., 2005; Leuz et al., 2008; Ferreira et al., 2017). This information asymmetry is considered responsible for phenomenon of homebias, a tendency of investors to invest more in their home country rather than in foreign markets (French & Poterba, 1993; Lewis, 1999; Karolyi & Stulz, 2003). A number of empirical studies confirm that foreign investors earn a lower rate of return than the returns earned by local investors, for example Shukla & Van Inwegen (1995) in the United States; Hau (2001) in Germany; Chiao & Lin (2004) and Chiao et al. (2010) in Taiwan; Choe et al. (2005) in Korea; Dvoˇr´ak (2005) in Indonesia; and Teo (2009) in Asia. On the other hand, another school of researchers claims that FIIs have broad fund base, better access to talent and more sophisticated investment tools which help them to perform better than local investors particularly in emerging markets (Grinblatt & Keloharju, 2000; Chiao et al., 2006). Albuquerque et al. (2009) propose a theory of equity trading in international 2

ACCEPTED MANUSCRIPT market which argues that the foreign investors do have some private information that is valuable for trading in many countries simultaneously. The results of some of the empirical studies seem to support this school of thought also. For example, Grinblatt & Keloharju (2000) in Finland, Seasholes (2000) in Taiwan, Thailand and Korea, Froot et al. (2001) in emerging markets, Bailey et al. (2007) in Thailand and Singapore, Froot & Ramadorai (2008) in closed-end funds of 25 countries, observe that the foreign investors earn higher returns than the local investors. Therefore, the debate remains inconclusive.

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Despite of large volume of the literature available on the profitability of institutional investors and foreign investors, no study has so far analysed the profitability of foreign institutional investors in India. Among all of the emerging markets, India is the preferred destination for the foreign investors to park their money. Indian market offers a strong opportunity for foreign investments from a macro as well as a micro perspective. As per IMF forecast, GDP growth for the world economy is expected to be 3.5 percent and 3.6 percent for the year 2017-18 and 2018-19, respectively while for the same periods Indian economy is expected to grow at the rate of higher than 7.0 percent. With moderating inflation and strengthening of Indian currency, INR in foreign exchange market, India is proving attractive rate of returns to international investors on real basis. At policy level, the Government of India is promoting entrepreneurship, manufacturing and expansion of infrastructural facilities. With improving corporate governance practices and regulatory procedures Indian markets are becoming more attractive for foreign investors. As SEBI data the cumulative net investment by FIIs had reached to the level of USD 223.6 billion by March 31, 2016. The turnover (purchase plus sales) of FIIs is INR 26,670 billion in 2015-16. The turnover of FIIs in cash segment is 22 percent of total turnover of National Stock Exchange(NSE) of India in that segment. As per a report by DataStream FIIs were holding more than 20 percent ownership in companies listed in NSE (this ownership is more than 40 percent if only floating stocks of the companies are considered); which is around double of the holding of DIIs in these companies. The magnitude of these figures indicates the important role played by foreign institutional investors in Indian equity market and highlights the importance of this study.

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Our results reveal that FIIs and DIIs are using different trading strategies - While FIIs are behaving like momentum traders, DIIs are following contrarian trading strategy. However, the portfolio replicating their trading patterns could not outperform the portfolios following simple benchmark strategies of buy-and-hold and systematic investment. Between FIIs and DIIs, the investment strategy of DIIs seems to be marginally more profitable than the investment strategy of FIIs.

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The study contributes to the literature in several ways. The profitability of foreign portfolio investment in India is an unexplored area of research; looking at the concerns regarding destabilizing role of FII-flow, an understanding of the profitability of FIIs has important policy implications. No much research has been done on profitability of institutional investors using daily or transaction level data because such data is not generally available in public domain. Therefore, most of the studies rely on quarterly returns of the companies on shareholdings to analyse the profitability of institutional investors (Puckett & Yan, 2011). This study uses daily data reported by security and Exchange Board of India (SEBI); therefore, the study is able to obtain a more accurate estimation of profitability of FII-investment in India. This add to our understanding of the ability of institutional investors to generate profits superior to the profit available to passive investors in the market. At the level of the methodology used in the analysis, the study does not make any assumption regarding the distribution of returns and the statistical inferences are based on a comprehensive bootstrapping exercise. Rather than analysing the profitability of the 3

ACCEPTED MANUSCRIPT investment on daily basis (or for any other frequency as per the frequency of the data available), this study evaluates the profitability of the investment using modified rate of return (MIRR) for a holding period based on more realistic assumptions. Rest of the paper structured as follows: Section 2 describes the dataset and research methodology used in the study. Section 3 presents the empirical results and Section 4 concludes.

2.1

Data and Methodology

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Data

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The study is based on daily aggregate net investment by FIIs and DIIs in equity market segment. In 1992, India first time allowed foreign portfolio investment in Indian capital market, however it adopted a selective and careful approach to avoid possible destabilizing impacts of foreign portfolio investment. Initially a selective group of ‘broad based’ institutional investors were allowed to invest in the market; the list was subsequently amended and extended but government remained sceptical and cautious on investors with speculative motives such as hedge funds. Till 2014, the foreign portfolio investors were officially called as ‘foreign institutional investors’ or FIIs. FIIs were allowed to participate in markets for their own investment and investment on behalf or their registered sub-accounts (SA). Wider participation in Indian markets was possible through an offshore derivative called participatory note (PN). In 2012, some individuals groups and associations not covered under the definition of FIIs were permitted to participates in the market as ‘Qualified Foreign Investor’ (QFI). In 2014 Securities and Exchange Board of India (Foreign Portfolio Investors) Regulations, 2014 were implemented. These regulations integrate FIIs, sub-accounts and QFIs into a single investors category - the ‘foreign portfolio investors’ (FPI). Although, officially FPIs have replaced FIIs, the term FIIs is continued to be used in popular media. In this study our FII data includes FII investment flow before 2014 and FPI investment flow after 2014. FII investment data is provided by SEBI, disseminated through the websites of two depository services in India - NSDL and CDSL. We have used FII investment data from April 2003 to March 2015 for this study.

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The Domestic Institutional Investors include Banks,domestic financial institutions (DFIs), insurance companies, mutual funds(MFs) and New Pension System. Their trading data is compiled on the basis of trading/client codes entered by the trading members in the trading system of their respective exchanges (NSE and BSE). The data is available since April 2007. Using the daily patterns of aggregate net investment by FIIs and DIIs, we have constructed portfolios for replicating the trading strategies of FIIs, DIIs and investors of other categories. The performance of these portfolios has been evaluated on the basis of their modified internal rate of return(MIRR) both in terms of Indian Rupee(INR) and US Dollar (USD). For this calculation we have also used daily closing value of stock market index - Nifty 50 and daily USD-INR exchange rate. All the data has been obtained from Bloomberg database. For the analysis of performance of FIIs with respect to their investment in individual stocks, we have used an unique transaction level database provided by SEBI.

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Investment Strategy of FIIs and DIIs

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Before evaluating the performance of the investment strategies of FIIs and DIIs, it is important to know what are the salient features of their investment strategies - how they dynamically interact with each other and with the financial markets. Figure 1 shows the cumulative FII investment viz-a-viz daily Nifty closing values from April 2003 to March 2015. Cumulative DII investment from May 2007 to March 2015 is also included in the figure. The index and cumulative FII investment are moving together and following similar patterns in their short run fluctuations. However, cumulative DII investment seems to follow a reverse pattern. Market is receiving more DII investment with falling stock prices while DIIs are liquidating their position when market prices move upwards. Figure 1 about here To understand their strategies, we estimate a vector autoregression (VAR) model using following four variables: (i) Relative FII flow = ln(FII Purchase) −ln (FII Sales)

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(ii) Relative DII flow = ln(DII Purchase) −ln(DII Sales)

(iii) Stock Returns = daily logarithmic returns on stock market index (iv) Change in Exchange Rate = daily logarithmic returns on exchange rate of INR in terms of USD

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Net FII flow (FII Purchase-FII Sales) and net DII flow (DII Purchase -DII Sales) show increasing trend in their magnitude (absolute values), which induce heteroscedasticity in data; therefore, both the variables are redefined here in relative terms taking the differences of the logarithmic value of purchase and sales by respective category of investors. Now,Relative FII flow and Relative DII flow show the logarithmic value of the ratio of purchase and sales; a positive value indicating positive net flow and a negative value indicating native net flow. Table 1 shows the descriptive statistics of the variables. The Augmented Dicky-Fuller unit-root test confirms the stationarity of all the four variables used in VAR model. However, first three variables show significant excess kurtosis. Table 1 about here

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The standard VAR model with these variables is estimated with six lags as suggested by Akaike information criterion(AIC). Panel (a) of Table 2 shows the contemporaneous correlation among these variables during the sample period 2007-15. Panel (b) presents the results of Granger causality tests based on the estimated VAR. Figure 1 shows the impulse response functions based orthogonalized shocks using Cholesky factorization. The variables are entered in the VAR in the following order for this purpose - FII flow, DII flow, stock market returns and change in exchange rate. Table 2 and Figure 2 about here The relative FII and DII flows are negatively correlated with each other both contemporaneously and dynamically. It appears that DII provide liquidity to FIIs. FIIs seems to behaving as momentum traders. FII flow shows positive contemporaneous correlation with stock returns and change in exchange rate. It also shows positive response to lagged shocks in these variables. On the other hand, DII flow is contemporaneously negatively related to both stock returns and change in exchange rate and also shows negative response to their 5

ACCEPTED MANUSCRIPT lagged shocks. The Granger causality test shows that impact of lagged stock returns on DII flow is statistically significant, although the impact of lagged changes in exchange rate is not significant. This suggests that DIIs as a group generally follow contrarian strategy with respect to the stock market. since, their pay-off is not affected by fluctuations in exchange rate, its as per expectations that they do not respond to change in exchange rate. Stock returns show some moderate lagged reaction to change in FII-flow.

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Construction of Portfolios

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We construct a portfolio replicating aggregate trading pattern of FII to evaluate the profitability of their trading strategy. The objective behind the construction of this portfolio is to answer a simple question - ‘can an investor earn higher than the market rate of return by timing his investment according to net FII flow ?’. If the return on this portfolio is higher than the return generated by the market for a portfolio following a passive investment strategy with no efforts of market timing, it will be an indicator of the superior market timing skills of FIIs. Similar portfolios are constructed to replicate the trading patterns of DIIs and other investors. The performance of these portfolios is compared with the performance of two benchmark portfolios following buy-and-hold investment strategy and systematic investment strategy, respectively.

Portfolio replicating FII Strategy

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The performance of a portfolio is evaluated for an investment horizon of length T (in terms of years) from time t = 0 to time t = T . At different time points in this horizon (i.e. on different dates), the portfolio buys or sales the notional units of index (Nifty) for an amount equal to net FII flow on that date. Net FII flow is the difference between aggregate FII purchase and aggregate FII sale on that date. So, the portfolio replicating FII strategy will buy the units of index on a particular date if net FII flow is positive on that date, while it will sell the units of index if net FII flow is negative on that date.

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The portfolio is not allowed to sell short in the market 1 . To ensure this, we calculate the cumulative investment of the portfolio in terms of units of index across the entire investment horizon. If at any point of time the cumulative investment is negative, the portfolio is assumed to be holding at the beginning of the investment horizon (i.e at t = 0), the units equal to the absolute value of the minimum of the cumulative investment. On the other hands if the minimum holding during the investment horizon is positive, the portfolio starts with zero initial holding. With this assumption the portfolio holding becomes nonnegative for the entire investment horizon. More formally this can be presented as follows:

Let Ft is net FII flow and Pt is the closing value of index at time t. The units of index bought at time t ( Bt ) is: Bt = Ft /Pt

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1 In practice FIIs can take short position in the market using derivative products. FIIs are allowed to trade in derivatives on indices and individual stocks subject to some positional limits. Similarly DIIs can also take short positions in the market. However, in this study our objective is to evaluate the performance of these institutional investors in cash segment of the market only. Therefore, the portfolios replicating investment strategies of FIIs and DIIs are not allowed to have short position during the investment horizon.

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t X

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Now for short-sale restriction, the holding at the beginning of the horizon is adjusted as follows:

if M < 0 if M ≥ 0.

(3)

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and |M | is the absolute value of M . The number of units held by the portfolio at time t is: ∀t ∈ {1...T }

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The performance of the portfolio is evaluated on the basis of its cash-flow (Ct ) during the investment horizon. At the beginning of the investment horizon the portfolio is assumed to be buying its all initial investment holding at the closing index value for that day. Therefore:

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At the end of the investment horizon the portfolio is assumed to be selling its entire holding at the closing index value for that day, hence Ct=T = Nt=T Pt=T .

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For rest of the investment horizon, the cash-flow of the portfolio is equal to (with opposite sign) the net investment flow of the investor group whose investment strategy is being replicating. For portfolio replicating FII investment strategy, it will be equal to:

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∀t ∈ {1...T − 1}

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Portfolio replicating DII Strategy

The same approach is used to construct the portfolio replicating DII strategy. The cashflow for this portfolio is calculated by replacing the net FII flow (Ft ) by net DII flow (Dt ).

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Portfolio replicating Strategy of Other Investors

Although, the daily buying and selling data is available only for two groups of the investors (FII and DII), from this data we can infer the net investment flow of another group of investors -“other investors”. Since, in the secondary market total buying is always equal to total selling (each transaction requires two counter-parties), the net investment of all the investors taken together is zero. Therefore, we can calculate the net investment of other investors (Ot ) as follows: Ot = −(Ft + Dt )

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using these values, we construct a portfolio replicating the investment strategy of other investors and calculate the cash-flow of this portfolio using the same method as used for calculation of cash-flows of portfolios replicating FII and DII investment strategies.

Benchmark Portfolios

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We have used two different benchmark portfolios to evaluate the performance of three portfolios replicating the investment strategies of FIIs, DIIs and other investors. The first benchmark portfolio uses the ‘buy-and-hold’ strategy. It buys one unit of the index on first day of investment horizon, hold it for the entire horizon and sell it on the last day of the horizon. The second portfolio uses the‘systematic investment’ strategy. It invests one rupee on every trading day in the index, hold the investment till last day of investment horizon.

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The units of index bought by this portfolio at time t is: Bt = 1/Pt

(8)

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Bt

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Ct=T = Pt=T

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Calculation of Return on Portfolios

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Let Ctn is the money invested by an investor in the market at time t ∈ 1...T , and hence a negative cash-flow for the investor. Similarly Ctp is the money dis-invested at time t, hence a positive cash-flow). For the calculation of MIRR, all positive cash-flows are compounded to their future value VT (C p ) on t = T and all negative cash-flows are discounted to their values V0 (C n ) on t = 0 as follows:

V0 (C n ) =

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Where r1 is the discounting rate and r2 is the compounding rate. In present study we have used the same rate of discounting and compounding for different investors and time horizons, as the objectives of the study is to calculate comparable rates of return for different investors and therefore, they are assumed to have same reinvestment opportunities and same cost of financing. The MIRR in terms of Indian Rupee (INR) is calculated assuming a discounting and compounding rates of eight percent; while this rate is assumed to be equal to two percent for calculation of MIRR in terms of UD Dollar (USD). This choice of rates is based on available risk-free rate of returns in India and the US. We have examined the sensitivity of the results with changing compounding and discounting rates; however, the major results of the study remain unaffected. MIRR (r) is then defined as : ln VT (C p ) − ln V0 (C n ) T

(12)

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The same method is used for calculation of reruns of investment portfolios replicating the investment strategy of DIIs and other investors as well as for the the benchmark portfolio using systematic investment strategy. The return on buy-and-hold strategy is simply calculated as follow: ln PT − ln P0 T

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Returns in terms of USD

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The return on a foreign investment portfolio not only depends on the movements of the stock prices in terms of local currency; but the movements of exchange rate of the local currency also affect its pay-off. Therefore, the the return on FII portfolio is required to be calculated in terms of some international currency and the US Dollar (USD) is an obvious choice for that purpose. The returns on different portfolios included in the study (the FII portfolio as well as other comparable portfolios) are calculated in terms of Indian Rupee (INR) and USD both. Since the cash-flow of the portfolios is defined in terms of INR, it is first converted into USD using daily exchange rates; then the returns in term of USD is calculated using the same method as discussed above for calculation of returns in terms of INR.

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3.1

Results and Discussion Performance of FII Strategy viz-a-viz Other Strategies during the Sample Period

Table 3 shows the return on different portfolios during the entire sample period. During 2003-2015, the portfolio replicating FII investment strategy earned a return of 9.65 percent 9

ACCEPTED MANUSCRIPT in INR against the return of 17.95 percent for a portfolio following buy-and-hold strategy and 11.41 percent for a portfolio following systematic investment strategy. In terms of USD the return on FII portfolio is 4.19 percent only against the returns of 15.66 percent and 6.44 percent on buy-and-hold and systematic investment portfolios. Table 3 about here

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During 2007-2015, the FII portfolio earned a return of 8.41 percent. It is again lower than the returns on two benchmark portfolios following buy-and-hold strategy(9.15 percent) and systematic investment strategy (10.07 percent), during this period also. It is slightly higher than the returns of portfolios based on investment strategies of DIIs and other investors (portfolios based on investment strategies of DIIs and other investors have earned the return of 8.36 percent and 7.92 percent respectively).

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Since, FIIs target the returns in terms of their home currency rather than the local currency, they are likely to design their market timing strategy keeping in view the expected changes in exchange rate as well as the stock market. However,contrary to what is expected, FII portfolio is not showing better returns in term of USD in comparison to the benchmarks and other other investment groups . The portfolio replicating FII investment strategy has earned only 2.46 percent return in term of USD during this period which is lowest among the comparative groups.

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Table 4 shows the annual performance of portfolios replicating different investment strategies during the period under study. This analysis also suggests that the performance of FII portfolio is not better than the performance of benchmark portfolios and portfolios representing other comparative groups. Out of twelve years, FII portfolio outperform buy-and-hold strategy only for two years in term of returns defined in INR and for three years if returns are defined in terms of USD. It outperform the systematic investment strategy only for one year out of twelve years whether returns are defined in terms of INR or USD. It is noticeable that FII portfolio has perform relatively better when market was in stress (during 2007-09 and 2011-12). DII portfolio outperformed FII portfolio during seven years out of eight years if returns are calculated in INR and during six years if returns are calculated in USD. The portfolio representing the investment strategy of other investors outperformed FII portfolio during seven years out of eight years whether return are calculated in INR or USD. Table 4 about here

Bootstrap Based Comparison of Performance

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The comparative performance of portfolio replicating FII investment strategy and other investment portfolios presented in Table 3 and Table 4, is based on limited number of observations; therefore, this cannot be used for statistical inference on the effectiveness of the aggregate time-skill of FIIs. We use block-bootstrap replications to make some statistical inference on relative effectiveness of investment strategies of FIIs and other investment groups. The following process is followed for this purpose: 1. Let N is the trading days in the total sample period under study (2003-15 or 200715). All the trading days are arranged in chronological order and assigned numbers 1...N . 2. For a bootstrap sample, a sample window of ` trading days is created with minimum 10

ACCEPTED MANUSCRIPT length of 250 trading days and maximum length of N-250 trading days. Therefore, the sample space for ` is : ` ∈ {250 : N − 250}. A random number is generated from this sample space using uniform distribution for deciding the length of sample window ` in the bootstrap sample. 3. The first day of the window for the bootstrap sample, a is decided based on a random number generated from the sample space a ∈ {1 : N − `} using uniform distribution. The last trading day of the sample window b, is equal to a + `. The sample window for this particular bootstrap sample will be the calendar days corresponding to the trading days a and b.

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4. Using the methodology discussed in preceding paragraphs the returns of different portfolios is calculated for the sample window.

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We have used 500 thousand bootstrap samples to analyse the performance of different portfolios in each of the two study periods. The average returns and their standard deviations based on these 500 thousand samples are presented in Table 5( Figure 3 also shows a comparative view of average returns on different investment strategies based on above bootstrap samples). We then divide these 500 samples into five-thousand blocks of 100 samples each and calculate the mean returns of different portfolios for each block. The distributions of mean retunes for five thousand blocks (which were found to be normally distributed based on standard tests) can be used for statistical inference. The standard error (s.e.) in Table 5 shows the standard deviations of mean returns for five thousand blocks. Table 5 and Figure 3 about here

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Based on bootstrap samples from the period 2003-15, The average return of FII portfolio is 9.04 percent in terms of INR and 3.36 percent in terms of USD. In both the cases it is lower than corresponding returns for two benchmark portfolios. Table 6 shows that out of 500 thousand bootstrap samples, FII outperform simple buy-and-hold strategy P (A − B) only in 28.70 percent cases; in other 71.30 percent samples returns from buy-and-hold strategy was higher than returns on FII portfolio. In all the five thousand blocks, the average returns of simple buy-and-hold strategy of 100 bootstrap samples were higher than the average returns of FII portfolio; in other words, in zero percent of the cases the average returns of FII portfolio was higher than average returns of buy-and-hold strategy, ¯ P (A¯ − B). Table 6 about here

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Similarly, FII portfolios gives lower average return than portfolio based on systematic investment strategy by 1.87 percent. Only in 18.52 percent cases FII portfolio outperforms the systematic investment strategy in 500 thousand bootstrap samples. In none of the cases the average return of FII portfolio is higher than the average return of systematic investment strategy for five thousand blocks of 100 bootstrap samples each. In USD terms, buy-and-hold strategy outperforms FII portfolio by 5.91 percent (FII portfolio returns are 3.36 percent against 9.27 percent for buy-and-hold strategy). In all the five thousand blocks of bootstrap samples, the average returns of buy-and-hold strategy is higher than the average return of FII portfolio. Systematic investment strategy outperform the FII portfolio by 2.30 percent and average returns of systematic investment strategy is higher than the average returns of FII portfolio in all the blocks. Similar patterns of returns is observed in bootstrap samples taken from the period from 11

ACCEPTED MANUSCRIPT 2007 to 2015. In this sample DII data is also available, therefore, the returns of FII portfolio can be compared with the returns of portfolios replicating the investment strategies of DIIs and other investment groups. The average return of FII portfolios is lower than not only the average returns of two benchmark portfolios but it is also lower than two comparative portfolios replicating the investment behaviour of DIIs and other investors.

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The average returns of FII portfolio is less than the average returns of DII portfolio by 1.43 percent in INR terms and by 2.04 percent in USD terms. FII portfolio outperforms DII portfolio only in 39.62 percent cases in INR terms and 37.48 percent cases in USD terms. The average returns of FII portfolio is higher than the average returns of DII portfolio in only 12 blocks in INR terms and in 4 blocks in USD terms out of 5,000 blocks of bootstrap samples. This clearly suggests that during the sample period (2007-15) the performance of portfolios replicating DII’s market timing strategy was better than portfolios replicating FII’s market timing strategy. Similarly, the portfolios replicating the investment strategy of other investors show better performance than portfolio replicating FII’s strategy.

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Figure 4 shows the performance of FII portfolio against the performance two benchmark portfolios for a rolling window of the investment horizon of one year from 2003 to 2015. For most of the times the returns on FII portfolio is less than benchmark portfolios. One major exception is observed during 2009-08, when prices corrected sharply after global financial crisis and FIIs dis-invested from the market. Figure 4 about here

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Figure 5 shows the performance of FII portfolio agains DII portfolio for rolling window of the investment horizon of one year form 2007 to 2015. for most of the times returns on DII portfolio is higher than the returns of FII Portfolio. Figure 5 about here

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However, these observations about the relative performance of FIIs, DIIs and other investors cannot be generalised much beyond a limit. We have very limited data regarding the trading behaviour of DIIs (and therefore also regarding the other investors). Moreover, this data is related to a peculiar time window - 2007-2015. At the beginning of this time window stock prices were slipping down due to global financial crisis. FIIs withdrew their investment during this period and DIIs provided them liquidity through investing in the market. Since 2010, FIIs are continuously investing in the market and both DIIs and other investors are selling to them. During this period, market index has broadly followed an upward trend. The assumption that the investors have adequate balance of stocks(or units of index) at the beginning of the investment horizon to avoid short selling requirement, implies that DIIs and other investors had accumulated the stocks in 2007-08, when index was at their lowest level and they are selling those stocks to FIIs when prices are high. Therefore, the returns of portfolios representing DIIs and other investors might be biased upward due to this assumption. To examine the sensitivity of results to the assumption of no short-selling, we recompute the returns of portfolios representing the trading strategies of FIIs, DIIs and other investors without assuming that the portfolio has adequate balance of stock at the beginning of the investment horizon. Now the portfolio will cover its short position at the end of the investment horizon. The results(not produced here), show that there is no significant difference between performance of FIIs and DIIs. Both of them are unable to outperform the benchmark portfolios. The portfolio representing other investors shows the lowest returns among all the portfolios.

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ACCEPTED MANUSCRIPT Based on these results it can be concluded that FIIs do not have market timing skill at aggregate level. The portfolio constructed using their aggregate trading patterns does not outperform two benchmark portfolios following simple ’buy and hold, and systematic investment strategy respectively. Portfolio representing FII’s trading strategy also does not outperform other two portfolios representing the trading strategies of DIIs and other investors respectively.

3.3

Analysis at Stock Level

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We extended this analysis at individual stock level taking 42 stocks which have been part of Nifty-50, hence these are quite liquid and attract FII investments. Using net FII flow data, we have calculated modified internal rate of return (MIRR) for FII investment in each stock for the investment horizon of 14 years from April 2003 to June 2015. This MIRR is compared with MIRR of two benchmark investment strategies - buy-and-hold and systematic investment with respect to each selected stock.

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The data for this analysis is taken from the Security and Exchange Board of India (SEBI) and covers period from April 1, 2003 to June 30, 2016. SEBI provides detailed transaction level data of FII buying and selling. Using ISIN (International Securities Identification Number) Code, the data are filtered for individual securities and aggregated at daily frequency. The daily net FII investment in terms of number of stocks (Si,t ) for each individual security is calculated by subtracting the number of stocks bought from number of stocks sold. Similarly daily net FII flow in terms amount (Fi,t ) is calculated by subtracting daily sales from daily purchase.

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The cumulating stock holding (Ni,t ) for the portfolio replicating FII trading strategy is calculated for each security. April 1, 2003 is assumed the starting date of investment horizon (t = 0). For a few scripts, which were listed in exchange after April 1, 2003; the listing day is assumed as the beginning of investment horizon. On the first day of investment horizon, FII portfolio is assumed to hold sufficient number of stocks so that the cumulating stock holding does not become negative (no short sale allowed). The cumulating stock holding (Ni,t ) is adjusted for stock splits and bonus shared issued during investment horizon.

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The Cash-flow on this date is assumed to be :

(13)

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Ci,t=0 = (−)Ni,t=0 Pi,t=0

At the end of the investment horizon the portfolio is assumed to be selling its entire holding the closing index value for that day, hence Ci,t=T = Ni,t=T Pi,t=T

(14)

for rest of the investment horizon, the cash-flow of the portfolio is equal to (with opposite sign) the net investment flow of the investor group who’s investment strategy it is replicating. For portfolio replicating FII investment strategy, it will be equal to: Ci,t = (−)Fi,t

(15) 13

ACCEPTED MANUSCRIPT The cash-flow for two benchmark strategies - buy-and-hold and systematic investment; was also calculated. Under buy-and-hold strategy, the investor is assumed to holding one stock at the beginning of the horizon and selling her cumulative holding, after taking into accounting the bonus and stock splits, at the end of the investment horizon. Under systematic investment strategy, the investor is assumed to buying one stock on each trading day and selling all her cumulative holding at the end of investment horizon.

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After defining the cash-flow for each security for the portfolios following different investment strategies, their return (MIRR) is calculated using the procedure defined in section 2.4 above. Table 7 presents the returns for portfolio based on FII trading strategy viz-a-viz returns for two benchmark strategies in terms of INR and USD for 42 stocks included in the analysis.

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The average return of FII portfolio is less than average returns of two benchmark portfolios both in terms of INR and USD. Out of 46 securities, only in case of one security the returns of FII portfolio is higher than returns of buy-and-hold strategy, when returns are defined in terms of INR. On the other hand, when returns are defined in terms of INR, return on FII portfolio is less than returns on buy-and-hold strategy for all the securities. The null-hypothesis that FII returns are not different from returns of buy-and-hold strategy is rejected in both the cases using both paired t-test and bootstrapping at 0.01 level of significance (t-values are 11.83 and 13.93 for returns defined in terms of INR and USD respectively).

Conclusion

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The returns of FII portfolio is higher than returns of systematic investment strategy for 15 securities when returns are defined in terms of INR and for 14 securities when returns are defined in terms of USD. The hypothesis that FII returns are not different from returns of systematic investment strategy is rejected only at 0.05 level but not at 0.01 level of significance using paired t-test (t-values are 2.08 and 2.13 for returns defined in terms of INR and USD respectively).

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The present study attempts to evaluate the market timing skill of foreign institutional investors in India at aggregate level. Using net FII investment flow we construct a portfolio investing in aggregate market index. Its performance is compared with two benchmarks following ’buy-and-hold’ and systematic investment strategies. Based on a large number of block-bootstrap samples we observe that the average return of the portfolio representing FIIs investment strategy is significantly lower than average returns of benchmark portfolios. Similarly, the average returns of an investor following FIIs trading strategy with respect to individual securities are not not found superior than returns on two benchmark strategies. Using a smaller sample period (2007-2015), we compare the performance of portfolio representing FII’s trading strategy with two similar portfolios representing the trading strategies of domestic institutional investors(DIIs) and other investors. Results based on bootstrapping suggest that the average returns of portfolio representing FII’s trading strategy are not higher than other two competing portfolios. Therefore, FIIs do not show superior market timing skills in comparison to DIIs and other investors.

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The scope of this study is limited to the market timing skills of FIIs as a group, with respect to their investment in India. Study does not evaluate the performance of their investment portfolios at global level. It also does not explore whether some of the FIIs at individual level have superior timing skills or not. The study is confined to time skills of FII and does not analyse their security selection skills (how they change their weights of individual securities in their overall portfolio).Further exploration around these questions will further enrich our understanding of effectiveness of investment strategy of foreign institutional investors and their role in the financial market.

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References Acharya, V. V., & Johnson, T. C. (2010). More insiders, more insider trading: Evidence from private-equity buyouts. Journal of Financial Economics, 98 (3), 500–523. Albuquerque, R., Bauer, G. H., & Schneider, M. (2009). Global private information in international equity markets. Journal of Financial Economics, 94 (1), 18–46. Bailey, W., Mao, C. X., & Sirodom, K. (2007). Investment restrictions and the crossborder flow of information: Some empirical evidence. Journal of International Money and Finance, 26 (1), 1–25.

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Barras, L., Scaillet, O., & Wermers, R. (2010). False discoveries in mutual fund performance: measuring luck in estimated alphas. Journal of Finance, 65 (1), 179–216. Bodnaruk, A., Massa, M., & Simonov, A. (2009). Investment banks as insiders and the market for corporate control. Review of Financial Studies, 22 (12), 4989–5026. Boulatov, A., Hendershott, T., & Livdan, D. (2013). Informed trading and portfolio returns. Review of Economic Studies, 80 (1), 35–72.

AN US

Campbell, J. Y., Ramadorai, T., & Schwartz, A. (2009). Caught on tape: institutional trading, stock returns, and earnings announcements. Journal of Financial Economics, 92 (1), 66–91. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52 (1), 57–82.

M

Chan, K., Covrig, V., & Ng, L. (2005). What determines the domestic bias and foreign bias? evidence from mutual fund equity allocations worldwide. Journal of Finance, 60 (3), 1495–1534.

ED

Chen, H. L., Jegadeesh, N., & Wermers, R. (2000). The value of active mutual fund management: an examination of the stockholdings and trades of fund managers. Journal of Financial and Quantitative Analysis, 35 (03), 343–368.

PT

Chiao, C., Chen, S.-H., & Hu, J.-M. (2010). Informational differences among institutional investors in an increasingly institutionalized market. Japan and the World Economy, 22 (2), 118–129.

CE

Chiao, C., Cheng, D. C., & Shao, Y. (2006). The informative content of the net-buy information of institutional investors in the taiwan stock market: a revisit using conditional analysis. Review of Pacific Basin Financial Markets and Policies, 9 (4), 661–697.

AC

Chiao, C., & Lin, K.-I. (2004). The informative content of the net-buy information of institutional investors: evidence from the taiwan stock market. Review of Pacific Basin Financial Markets and Policies, 7 (2), 259–288.

Choe, H., Kho, B.-C., & Stulz, R. M. (2005). Do domestic investors have an edge? the trading experience of foreign investors in Korea. Review of Financial Studies, 18 (3), 795–829. Daniel, K., Grinblatt, M., Titman, S., & Wermers, R. (1997). Measuring mutual fund performance with characteristic-based benchmarks. Journal of Finance, 52 (3), 1035– 1058.

16

ACCEPTED MANUSCRIPT Dhingra, V. S., Gandhi, S., & Bulsara, H. P. (2016). Foreign institutional investments in India: an empirical analysis of dynamic interactions with stock market return and volatility. IIMB Management Review , 28 (4), 212–224. Dvoˇr´ak, T. (2005). Do domestic investors have an information advantage? evidence from Indonesia. The Journal of Finance, 60 (2), 817–839. Ferreira, M. A., Matos, P., Pereira, J. P., & Pires, P. (2017). Do locals know better? a comparison of the performance of local and foreign institutional investors. Journal of Banking & Finance.

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French, K. R., & Poterba, J. M. (1993). Investor diversification and international equity markets. Advances in Behavioral Finance, 1 , 383. Froot, K. A., Oconnell, P. G., & Seasholes, M. S. (2001). The portfolio flows of international investors. Journal of Financial Economics, 59 (2), 151–193. Froot, K. A., & Ramadorai, T. (2008). Institutional portfolio flows and international investments. Review of Financial Studies, 21 (2), 937–971.

AN US

Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 14 (1), 71–100. Griffin, J. M., Shu, T., & Topaloglu, S. (2012). Examining the dark side of financial markets: do institutions trade on information from investment bank connections? Review of Financial Studies, 25 (7), 2155–2188.

M

Grinblatt, M., & Keloharju, M. (2000). The investment behavior and performance of various investor types: a study of Finland’s unique data set. Journal of Financial Economics, 55 (1), 43–67.

ED

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review , 70 (3), 393–408. Hau, H. (2001). Location matters: an examination of trading profits. Journal of Finance, 56 (5), 1959–1983.

PT

Hendershott, T., Livdan, D., & Sch¨ urhoff, N. (2015). Are institutions informed about news? Journal of Financial Economics, 117 (2), 249–287.

CE

Hiremath, G. S., & Kattuman, P. (2017). Foreign portfolio flows and emerging stock market: is the midnight bell ringing in india? Research in International Business and Finance.

AC

Irvine, P., Lipson, M., & Puckett, A. (2007). Tipping. Review of Financial Studies, 20 (3), 741–768. Jegadeesh, N., & Tang, Y. (2011). Institutional trades around takeover announcements: skill vs. inside information. SSRN: https://ssrn.com/ abstract=1568859. Kacperczyk, M., Sialm, C., & Zheng, L. (2005). On the industry concentration of actively managed equity mutual funds. The Journal of Finance, 60 (4), 1983–2011.

Kandel, E., & Pearson, N. D. (1995). Differential interpretation of public signals and trade in speculative markets. Journal of Political Economy, 103 (4), 831–872. Karolyi, G. A., & Stulz, R. M. (2003). Are financial assets priced locally or globally? Handbook of the Economics of Finance, 1 , 975–1020. 17

ACCEPTED MANUSCRIPT Kim, O., & Verrecchia, R. E. (1994). Market liquidity and volume around earnings announcements. Journal of Accounting and Economics, 17 (1-2), 41–67. Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53 (6), 1315– 1335. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32 (1), 23–43. Leuz, C., Lins, K. V., & Warnock, F. E. (2008). Do foreigners invest less in poorly governed firms? The Review of Financial Studies, 22 (8), 3245–3285.

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Lewis, K. K. (1999). Trying to explain home bias in equities and consumption. Journal of economic literature, 37 (2), 571–608. Nofsinger, J. R., & Sias, R. W. (1999). Herding and feedback trading by institutional and individual investors. Journal of Finance, 54 (6), 2263–2295. Poshakwale, S. S., & Thapa, C. (2010). Foreign investors and global integration of emerging indian equity market. Journal of Emerging Market Finance, 9 (1), 1–24.

AN US

Puckett, A., & Yan, X. S. (2011). The interim trading skills of institutional investors. Journal of Finance, 66 (2), 601–633. Rubinstein, A. (1993). On price recognition and computational complexity in a monopolistic model. Journal of Political Economy, 101 (3), 473–484. Seasholes, M. (2000). Smart foreign traders in emerging markets. unpublished Harvard Business School working paper .

M

Shukla, R. K., & Van Inwegen, G. B. (1995). Do locals perform better than foreigners?: an analysis of UK and US mutual fund managers. Journal of Economics and Business, 47 (3), 241–254.

ED

Teo, M. (2009). The geography of hedge funds. Review of Financial Studies, 22 (9), 3531–3561.

PT

Wermers, R. (2000). Mutual fund performance: an empirical decomposition into stockpicking talent, style, transactions costs, and expenses. Journal of Finance, 55 (4), 1655– 1703.

CE

Yaha, A., Singh, N., & Rabanal, J. P. (2017). How do extreme global shocks affect foreign portfolio investment? an event study for india. Emerging Markets Finance and Trade, 53 (8), 1923–1938.

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Yan, X. S., & Zhang, Z. (2009). Institutional investors and equity returns: are short-term institutions better informed? Review of Financial Studies, 22 (2), 893–924.

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ACCEPTED MANUSCRIPT Table 1: Descriptive Statistics Stock Returns

Exchange Rate

FII Flow

DII Flow

0.04 1.55 -0.28 10.65 -17.99

-0.02 0.46 -0.28 7.07 -17.31

0.09 0.31 0.52 2.37 -10.72

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Sample Period: 2007-2015 Mean Standard Deviation Skewness Excess Kurtosis ADF Statistic

0.01 1.57 0.03 11.49 -15.25

-0.03 0.54 -0.22 4.99 -14

0.04 0.26 0.18 2.15 -8.48

0.01 0.37 0.14 0.04 -8.52

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Sample Period: 2003-2015 Mean Standard Deviation Skewness Excess Kurtosis ADF Statistic

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Table show the descriptive statistics of the variables used in vector autoregression (VAR) model presented in the next table (2). FII flow represents the relative FII flow defined as the difference between logarithmic values of FII Purchase and FII Sales. Similarly DII flow represents the relative DII flow defined as the difference between logarithmic values of FII Purchase and FII Sales. Stock returns are the continuously compounded returns based on daily closing value of index (Nifty). Exchange rate represents the relative changes in value of INR in terms of USD. All data are taken at daily frequency. For the period 2003-2007, DII flow data is not available. The model used for calculation of Augmented Dickey Fuller unit root statistic (ADF statistic) includes a constant and augmented lagged variables based on Akaike information criterion. The test suggests that all the variables included in the VAR model are stationary.

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Table 2: Dynamic Interaction among the Variables (b) Correlation Matrix:2007-15

(a) Correlation Matrix:2003-15

RET

1.00 0.39 0.26

1.00 0.41

EX FII DII RET EX

1.00

FII

DII

RET

EX

1.00 -0.65 0.44 0.31

1.00 -0.16 -0.15

1.00 0.47

1.00

2003-15

Null Hypothesis

2007-15

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FII RET EX

FII

prob.

Wald Statistic

prob.

Relative DII flow does not Granger cause Relative FII flow Stock market returns do not Granger cause Relative FII flow Exchange rate does not Granger cause Relative FII flow

84.61 15.03

0.00 0.02

26.18 73.76 21.80

0.00 0.00 0.00

Relative FII flow does not Granger cause Relative DII flow Stock market returns do not Granger cause Relative DII flow Exchange rate does not Granger cause Relative DII flow

-

-

65.05 25.38 3.12

0.00 0.00 0.79

Relative FII flow does not Granger cause Stock market returns Relative DII flow do not Granger cause Stock market returns Exchange rate does not Granger cause Stock market returns

14.57 12.06

0.02 0.06

12.88 5.48 11.85

0.00 0.48 0.07

Relative FII flow does not Granger cause exchange Rate Relative DII flow does not Granger cause exchange rate Stock market returns do not Granger cause exchange rate

12.46 15.52

0.33 0.02

6.88 6.18 13.41

0.33 0.40 0.04

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Wald Statistic

(c) Granger Causality

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Panel (a) and Panel (b) show the contemporaneous correlations during sample period 2003-15 and 2007-15 respectively, among the four variables -Relative FII flow (difference between logarithmic values of FII Purchase and FII Sales), Relative DII flow (difference between logarithmic values of FII Purchase and FII Sales), stock market returns (continuously compounded) and relative changes in exchange rate (value of INR in terms of USD). All data are taken at daily frequency. For the period 2003-2007, DII flow data is not available. Panel (c) shows the results of Granger causality (block exogeneity) test based on a vector autoregression (VAR) model of these four(three) variables with six lags (lag length is based on Akaike information criterion).

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Table 3: Returns for Different Investment Strategies for Entire Sample Period

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Investment Strategy Buy and Hold Systematic FII DII Other Investors

2003-2015

2007-2015

in INR

in USD

in INR

in USD

17.95 11.41 9.65 -

15.66 6.44 4.19 -

9.15 10.07 8.41 8.36 7.92

3.78 4.23 2.46 2.58 3.18

A portfolio investing in aggregate market index (Nifty) is constructed replicating the net daily FII inflow/ outflow patterns. Adequate opening balance is assumed to avoid any short selling. Similar portfolios are constructed replicating the aggregate trading patterns of DIIs and Other Investors. Two benchmark portfolios are also constructed following buy-and-hold and systematic investment strategies. The systematic investment portfolio invests one rupee on each trading day in the market. The returns of these five portfolios are calculated using modified internal rate of return (MIRR). The returns are calculated in terms of INR and USD separately. For calculation of MIRR in USD all the cash-flows are converted in terms of USD using exchange rate for the day. The return on reinvestments (for compounding) and opportunity cost of capital (for discounting) are assumed to be equal to eight percent for calculation of MIRR in terms of INR. These rates are assumed to be equal to two percent for calculation of MIRR in terms of and USD.

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Table 4: Annual Returns for Different Investment Strategies in INR

in USD

Buy and Hold

Systematic

FII

58.79 11.25 49.95 9.66 25.04 -45.6 49.42 8.43 -11.13 5.97 15.47 23.03 16.69 8.83

31.69 17.61 36.85 12.39 2.32 -11.84 17.48 8.86 5.43 7.46 15.27 11.03 12.88 7.00

23.94 8.63 24.48 7.07 18.06 -16.24 12.19 3.89 1.24 4.27 9.07 9.28 8.82 5.22

2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Average(2003-2015) Average(2007-2015)

DII

Others

1.74 -12.00 15.42 11.98 8.59 10.28 11.60 12.41

12.37 -1.36 28.57 10.74 4.17 9.23 10.69 15.74

7.50

11.27

Buy and Hold

Systematic

FII

67.26 10.91 47.97 12.25 32.19 -69.64 60.90 8.11 -24.90 -0.94 5.48 18.76 14.03 3.75

33.95 17.42 33.04 13.63 -0.40 -25.45 19.90 8.13 -3.98 4.76 13.02 5.81 9.99 2.72

24.78 5.10 19.78 4.08 16.25 -31.23 10.33 0.63 -7.26 -0.33 4.05 3.13 4.11 -0.55

DII

Others

-2.97 -24.08 14.78 7.03 1.47 2.12 1.83 7.35

7.28 -6.10 24.20 6.12 -0.07 5.29 6.51 11.50

0.94

6.84

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The table shows the annual returns (MIRR) on five different portfolios. These portfolios are based on replication of investment strategies of FIIs, DIIs and other investors. Two benchmark portfolios are based on buy-and-hold and systematic investment strategies.

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Table 5: Average Returns and their Variability for Different Investment Strategies Returns in Indian Rupee

Returns in US Dollar

Average

Average

ED

Strategy

s.d.

s.e.

s.d.

s.e.

9.27 5.66 3.36

14.84 8.76 5.54

1.45 0.84 0.54

5.15 4.84 1.87 3.91 4.99

14.75 8.54 5.61 5.51 3.70

1.48 0.87 0.57 0.56 0.38

(a) sample period 2003-15

CE

PT

Buy and Hold Systematic FII

AC

Buy and Hold Systematic FII DII Others

13.23 10.91 9.04

11.38 6.73 4.35

1.11 0.67 0.43

(b) sample period 2007-15 10.95 10.78 8.24 9.67 9.57

11.44 6.52 4.62 4.03 3.68

1.15 0.66 0.47 0.41 0.37

The table is based on 500 thousand bootstrap samples of performance of different investment strategies using the data for sample period 2003-2015 and 2007-2015. The block bootstrap samples are of random length (investment horizon), but the minimum investment horizon is set as 250 trading days. Each bootstrap sample starts on a randomly selected date. For each bootstrap sample the returns (MIRR) of different trading strategies are calculated. The average and s.d. show the average of the returns and their standard deviation respectively for a particular strategy based 500 thousand bootstrap samples. 500 thousand bootstrap samples are divided into 5 thousand blocks of 100 samples each. For each block the average of returns for a particular strategy is calculated. s. e. (standard error of mean) represents the estimated standard deviation of mean returns for 5 thousand blocks.

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Table 6: Comparative Performance of Different Investment Strategies at Aggregate Level In INR Strategy A

Strategy B

Differential Return (A-B)

In USD

Bootstrap Probabilities ¯ < 0) P (A < B) P (A¯ − B

Differential Return (A-B)

Bootstrap Probabilities ¯ < 0) P (A < B) P (A¯ − B

(a) sample period 2003-15 FII FII

Buy and Hold Systematic

-4.19 -1.87

FII FII FII FII DII DII DII Others Others

Buy and Hold Systematic DII Others Buy and Hold Systematic Others Buy and Hold Systematic

-2.72 -2.53 -1.43 -1.33 -1.28 -1.11 0.10 -1.38 -1.21

28.70 18.52

0.00 0.00

-5.91 -2.30

27.18 21.31

0.00 0.00

-3.28 -2.97 -2.04 -3.12 -1.24 -0.93 -1.08 -0.16 0.16

40.28 18.82 37.48 26.08 49.39 43.91 26.92 52.59 48.12

0.00 0.00 0.00 0.00 0.14 0.08 0.01 0.44 0.57

(a) sample period 2007-15 0.00 0.00 0.00 0.00 0.09 0.01 0.62 0.06 0.03

CR IP T

38.85 12.71 39.62 40.33 49.40 42.25 43.76 45.01 37.92

Nifty

2000

100

2004

2006

2008

2010

2012

2014

Figure 1: Cumulative FII/DII Investment viz-`a-viz Nifty

22

Nifty

CE

4000

6000

PT

DII

8000

ED

400 300 200

FII

0

AC

Cumulative Investment(billion INR)

M

AN US

The table is based on 500 thousand bootstrap samples of performance of different investment strategies using the data for sample period 2003-2015 and 2007-2015 separately. The block bootstrap samples are of random length (investment horizon) of more than 250 trading days starting on a randomly selected date. The differential return (A − B) is the average of the difference of the returns between Strategy A and Strategy B. P (A < B) shows the fraction of times the return of Strategy A is higher than the return of Strategy B in 500 thousand samples. 500 thousand bootstrap samples are divided into 5 thousand blocks of 100 samples each. The average of the differential returns ¯ < 0) shows the fraction of times in 5 thousand blocks the between Strategy A and Strategy B is calculated for each block. P (A¯ − B mean differential return is positive .

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Table 7: Comparative Performance of Different Investment Strategies at Stock Level In INR Systematic

CE

AC Average

21.27 23.61 36.54 43.63 20.29 25.50 26.42 21.76 34.21 20.88 19.94 21.07 26.00 30.83 30.27 24.35 7.37 17.11 28.33 26.50 23.19 18.90 24.11 42.10 33.74 47.34 38.67 34.55 8.10 17.29 15.53 24.48 9.63 24.15 22.27 39.16 25.68 24.24 17.87 11.75 26.19 12.91

9.60 11.27 17.45 15.85 14.80 11.03 5.96 15.83 8.11 13.25 15.81 7.47 12.13 18.39 16.14 12.54 2.76 14.87 14.09 12.62 10.69 11.71 -5.20 15.96 12.38 19.83 14.19 17.82 3.69 7.05 8.44 8.90 9.22 8.04 8.35 16.79 15.15 11.61 3.90 1.44 14.79 10.59

8.45 9.76 9.91 12.32 8.54 9.74 10.26 8.91 10.25 9.43 10.15 8.63 10.36 14.15 11.95 8.62 6.70 9.69 10.81 9.30 8.66 9.01 7.45 12.26 10.38 13.80 11.86 13.30 8.86 8.47 11.56 9.42 7.98 9.43 10.66 10.42 17.35 6.79 7.51 6.37 9.02 11.00

11.32

9.99

M

ED 24.95

FII

Buy and Hold

Systematic

18.53 20.82 33.46 40.18 17.58 22.67 23.57 19.01 31.16 18.15 17.22 18.30 23.15 27.87 27.40 21.54 4.95 14.47 25.44 23.65 20.41 16.21 21.31 38.90 30.72 44.01 35.54 31.07 4.80 14.64 8.99 21.66 7.35 21.35 19.50 36.02 22.05 21.44 15.21 9.23 22.51 10.36

3.75 5.42 11.01 9.49 8.66 5.12 0.24 9.90 2.32 7.34 9.76 1.77 6.74 12.10 9.99 6.56 -2.43 8.95 7.97 6.61 4.78 5.86 -10.54 9.61 6.26 13.28 8.03 11.73 -1.82 1.48 2.32 3.53 4.06 2.23 2.26 10.48 8.94 5.77 -1.63 -3.82 8.62 4.89

2.55 4.12 3.76 6.28 2.85 4.11 4.33 3.15 4.45 3.52 4.30 3.07 5.00 8.16 5.92 2.71 0.87 3.67 4.75 3.22 2.85 3.09 1.61 6.04 4.38 7.51 5.85 7.43 3.14 3.01 5.23 4.43 2.33 3.56 4.84 4.27 10.83 0.96 1.54 0.56 3.14 4.89

21.96

5.42

4.10

AN US

Buy and Hold

PT

ACC Ambuja Cement Asian Paint Axis Bank Bajaj Auto Bank of Baroda BHEL BPCL Bharati Airtel Cipla Dr Reddy GAIL Grasim Industries HCL Tech HDFC Bank Hero Motor Corp HINDALCO HUL HDFC ITC ICICI Bank Infosys Jindal Steel Kotak Bank L& T Lupin M&M Maruti Ltd NTPC ONGC Power Grid Punjab National Bank Ranbaxy Labs RELIANCE SBIN Sun Pharma TCS Tata Motors Tata Power Tata Steel Ultratech Cement WIPRO

In USD FII

CR IP T

Company

The table shows the annual returns (MIRR) of FII trading strategy along with two benchmark strategies on 42 individual stocks over the sample period of 14 years from April 2003 to June 2015.

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.4

.20

.3

.15 .10

.2 .05

.1

.00 -.05

.0

-.10

-.1 -.15

-.2

-.20

2

3

4

5

6

7

8

9

10

1

(a) Response of FII flow to DII flow

2

3

4

5

6

7

9

10

(b) Response of DII flow to FII flow

.20

.016

.15 .012 .10 .05

.008

.00 .004

-.05 -.10

.000

AN US

-.15

-.004

8

CR IP T

1

-.20

1

2

3

4

5

6

7

8

9

10

1

(c) Response of FII flow to Stock Returns

.4

2

3

4

5

6

7

8

9

10

(d) Response of DII flow to Stock Returns

.016

.3

.012

M

.2

.1

.0

ED

-.1

-.2 1

2

3

4

5

6

7

8

.008

.004

.000

-.004

9

10

1

2

3

4

5

6

7

8

9

10

.016

.016

.012

CE

.012

PT

(e) Response of FII flow to Exchange Rate(f) Response of DII flow to Exchange Rate

.008

.004

.004

.000

.000

AC

.008

-.004

-.004 1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

(g) Response of Stock Returns to FII flow (h) Response of Stock Returns to DII flow

Figure 2: Impulse Response Function The figure shows the cumulative impulse response of one variable to a shock of one standardized unit of other variable. The response function is estimated using Cholesky factorization to obtain orthogonal shocks. The variables enter in the following order - Relative FII flow, Relative DII flow, Stock Market Returns, Change in Exchange Rate

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13.23

in INR

10.95

in USD

10.78

in INR

in USD

9.57

10

12

9.67 10.91

8.24 8

9.04

8

10

9.27

6

5.15

4.99

4.84

6

5.66

4

3.91

4

3.36

0

0

2

2

1.87

Buy and Hold

Systematic

FII

Buy and Hold

FII

DII

Others

(b) sample period 2007-2015

CR IP T

(a) sample period 2003-2015

Systematic

Figure 3: Average Returns on Different Investment Strategies

AN US

The figure shows average returns based on 500 thousand bootstrap replications of different trading strategies in terms of INR and USD. The beginning date and length of investment horizon selected randomly.

0.0

2006

2008

2010

buy and hold

2012

systematic

2014

ED

2004

M

−0.5

returns

0.5

FII

Figure 4: Performance of FII investment strategy vis-a-vis benchmark

CE

PT

This figure shows the returns of FII investment strategy against the performance of two benchmarks (buy and hold and systematic investment) for a rolling window of one-years investment horizon.

DII

0.0 −0.5

returns

AC

0.5

FII

2009

2010

2011

2012

2013

2014

2015

Figure 5: Performance of FII and DII investment strategies This figure shows the returns of FII and DII investment strategies for a rolling window of one-years investment horizon.

25