Mutual Fund Characteristics and Investment Performance in India

Abstract

In this study, we examine the role of fund characteristics in determining mutual fund performance in India. The data comprises of 237 open-ended Indian equity (growth) schemes during the period April 2007 to March 2013. Using daily dividend adjusted net asset values (NAVs), the risk-adjusted performance is estimated employing conditional version of Carhart (1997) four factor model in a time series regression framework. A range of fund characteristics, namely, the size of fund, growth in size of fund, expense ratio, portfolio turnover, NAV and age of fund, are examined in predictive model in a panel data regression framework that may determine the future performance of the fund. The Hausman specification test is conducted to decide if individual effects are random or fixed. The results of panel regression, based on fixed effects estimator, show that the size of fund, growth in size of fund and NAV negatively affect one period ahead risk-adjusted performance in India, while the age of fund has a positive impact. Expense and portfolio turnover ratios do not play a significant role. Identification of significant fund characteristics offer valuable insights to investors as it will allow them to make prudent selection of mutual funds and make judicious investment decisions.

Keywords

Asset pricing model mutual fund performance panel data expenses net asset value

Introduction

In the current scenario, mutual funds as a means of investment are attaining immense popularity and drawing the attention of investors at large to invest in various securities through the expertise and knowledge of qualified professionals. The Mutual fund industry in India is more than five decades old. The year 1964 saw the birth of the first asset management company (AMC) in India: Unit Trust of India (UTI). Unit Scheme 1964 (US-64) was the first scheme launched by UTI in 1964 followed by Mastershare in 1986. As the mutual fund industry gained momentum, the Indian market witnessed the setting up of many public sector mutual funds from 1987. From 1993, the doors were opened to private sector players, both Indian and foreign. In 1996, a comprehensive regulatory framework was established under Securities and Exchange Board of India, SEBI (Mutual Fund) Regulations 1996¹ which brought all the mutual funds under its framework and provided a level playing field to them. The year 2002 witnessed the splitting up of UTI into two separate entities: UTI-I which includes US-64 and UTI-II which manages net asset value (NAV)-based schemes. In March 2016, 42 mutual fund houses operated in India (33 private and 9 public) offering 2,420 schemes with assets under management (AUM) as on 31 March 2016 being US$ 189.66 billion.

The booming mutual fund industry in India is catching the attention of academicians, researchers, policymakers and investors, both retail and institutional. The academic world has always remained inquisitive of this growth and its underlying reasons. A number of studies conducted in this area not only analyze the performance of mutual funds but also try to identify fund characteristics determining fund performance. Empirical evidence suggests that fund-specific characteristics such as the size of fund, management fee and age of fund have significant influence on performance of the fund (Carhart, 1997; Christensen, 2003; Droms & Walker, 1996; Elton, Gruber & Blake, 1993; Ippolito, 1989; Low, 2012; Mansor, Bhatti & Ariff, 2015 Sharpe, 1966). Also, there is a segment of research which analyses managerial characteristics such as education, age and experience of managers in determining fund performance (Chevalier & Ellison, 1999; Golec, 1996; Gottesman & Morey, 2006; Huang & Shi, 2013).

Identification of significant fund characteristics is of great consequence for investors as it will allow them to make a prudent selection of mutual funds and make judicious investment decisions. Thus, the objective of the study is to examine the role of fund characteristics in determining mutual fund performance in India.

In the Indian context, influence of fund characteristics on fund performance is less explored, with focus limited to characteristics such as sponsorship of funds (public or private, Indian or foreign), investment objective (growth, income and balanced), fund structure (open-ended and close-ended) and past performance of fund (Alekhya, 2012; Arshdeep, 2011; Bawa & Brar, 2011; Dhar, 2003; Garg, 2014; Pandey & Sudesh, 2005; Panwar & Madhumati, 2006). International evidence provides other important fund attributes, for instance, the size of fund, growth in size of fund, NAV and portfolio turnover that impact the performance of funds (Belgacem & Hellara, 2011; Low, 2010; Prather, Bertin & Henker, 2004; Yap & Pierce, 2008). Further, Indian studies have estimated the performance of funds using traditional measures such as Sharpe, Treynor and Jensen measures (Alekhya, 2012; Arshdeep, 2011; Panwar & Madhumati, 2006; Prasad & Prasad, 2012). Also, these studies have primarily used a lower (monthly) frequency of data while analyzing fund performance (Arshdeep, 2011; Goel, Sharma & Mani, 2012). The application of panel data estimation to capture the effects of various fund characteristics on future fund performance is limited.²

The present study attempts to fill this important research gap by examining a range of fund characteristics, namely, the size of fund, growth in size of fund, expense ratio, portfolio turnover, NAV and age of fund that may determine future performance of the fund. The risk-adjusted performance of mutual funds is measured using the conditional version of Carhart (1997) four factor model (following a conditional performance evaluation approach suggested by Ferson and Schadt, 1996). Since the risk exposure of mutual funds change over time, the Carhart model is estimated using daily observations as it allows the performance of funds to be evaluated over short time horizons in a more reliable manner (Bollen & Busse, 2001; Gallefoss, Hansen, Haukass & Molnar, 2015; Goetzmann, Ingersol & Ivkovic, 2000; Sehgal & Jhanwar, 2008). Following Chevalier and Ellison (1999) and Yap and Pierce (2008), a predictive model in a panel data framework has been adopted for examining relationship between fund performance and its characteristics. The predictive relationship model is superior to a realized relationship model as it allows the investors and fund managers to have a futuristic orientation towards fund characteristics that are expected to influence performance of fund. The study covers a large number of mutual fund schemes (namely 237) for a more recent time period (2007 to 2013).

The study is organized as follows: the second section provides a relevant review of the literature. The third section describes data and their sources. The fourth section provides details of the mutual fund performance model. The fifth section covers methodological issues. The sixth section details the empirical results. The last section provides a summary and concluding observations.

Review of Literature

Review of the previous literature helps in preparing the following list of fund characteristics to be used in the current study for examining the relationship between these characteristics and performance.

Size of fund: Empirical studies considering different time frames and market contexts find varying relationships between fund size and fund performance. Grinblatt and Titman (1989), Dahlquist, Engstrom and Soderlind (2000), Yap and Pierce (2008), Ferreira, Keswani, Migues and Ramos (2012) and Low (2012) find the performance of a fund to be inversely related to the size of fund, implying diseconomies of scale and suggesting that larger funds, due to their increasing size, face challenges to locate meaningful and sensible investments which ultimately hampers the selectivity ability of managers. Also, Lobao and Gomes (2015) and Pastor, Stambaugh and Taylor (2015) find a negative relation between the fund’s size and its performance, indicating decreasing returns to scale for the fund. The empirical literature also states the benefits that large funds may provide over small funds. Large funds have access to a larger asset base, better investment opportunities, more resources for research and better negotiating power leading to economies of scale (Ciccotello & Grant, 1996; Ferreira et al., 2012). Substantiating these facts, Otten and Bams (2002), Białkowski and Otten (2011), Belgacem and Hellara (2011), Tang, Wang and Xu (2012), Kaushik and Pennathur (2012), Goel et al. (2012), Vijayakumar, Muruganadam and Rao (2012) and Zabiulla (2014) document a positive relationship between size and performance of fund. Ban and Choe (2013), for Korean mutual funds, find that small funds perform worse than large funds because the managers tend to ignore very small funds and, rather, hold cash. However, an insignificant relationship is evinced by a few studies, for instance, Sharpe (1966), Droms and Walker (1996), Carhart (1997), Prather et al. (2004) and Huang and Shi (2013). Based on the literature, it may be suggested that there is mixed evidence of relationship of fund size with performance of the fund.

Growth in size of fund: A larger asset base, better investment opportunities and more resources for research lead to economies of scale for growing funds. But such opportunities may be available to funds up to a certain point. After a point, these economies may even reverse and become diseconomies (Ciccotello & Grant, 1996; Ferreira et al., 2012). Higher growth need not necessarily generate higher performance for funds as reported by Yan (2008) and Low (2010). However, Belgacem and Hellara (2011) show that growth does not have any influence on performance. Further, Pastor et al. (2015) state that as the size of an active fund increases, the fund’s ability to outperform its benchmark declines, indicating the decreasing returns to scale for fund. Based on the literature, the growth in size of fund may have a negative relationship with performance.

Expense ratio: Empirical evidence exhibits different relationships between the expense ratio and performance. A negative relationship is evinced in Golec (1996), Carhart (1997), Otten and Bams (2002), Yap and Pierce (2008), Babalos, Kostakis and Philippas (2009), Elton Gruber and Blake (2012), Goel et al. (2012), Kaushik and Pennathur (2012), Huang and Shi (2013) and Mansor et al. (2015), implying that, on an average, managers are not able to produce higher abnormal performance with increased research costs. In contrast, a positive relationship is reported by Droms and Walker (1996) and Bauer, Otten and Rad (2006). Prather et al. (2004) and Vijayakumar et al. (2012) suggest that fund houses undertake operating or administrative expenses with the assumption that these charges would adequately support research, managerial expertise and advertising of funds and, therefore, a positive influence is expected on fund performance. However, Ippolito (1989) reports an insignificant relationship stating that performance is measured after adjusting for expenses; therefore, estimates of expenses must not be significantly different from zero, suggesting that mutual funds utilize their money or resources efficiently. Insignificant relationship is also displayed by Christensen (2003), Fortin and Michelson (2005), Karlsson and Persson (2005), Yan (2008) and Low (2010).

Portfolio turnover: Ippolito (1989) finds an insignificant relationship between turnover and performance, indicating that fund houses churning over their portfolio frequently generate incremental returns which are just enough to meet their higher transactions costs. Similar results are drawn by Droms and Walker (1996), Prather et al. (2004), Yan (2008) and Low (2010). In contrast, Dahlquist et al. (2000) find a positive relationship, that is, transactions costs do increase with increased trading, indicating that a manager is selling or buying on new and good information. Also, Kaushik and Pennathur (2012) find that performance of a fund is significantly and positively impacted by fund turnover. However, Carhart (1997) demonstrates a negative influence of turnover on performance. Similar results are reported recently by Elton et al. (2012), Vijayakumar et al. (2012) and Lobao and Gomes (2015).

Net asset value (NAV): According to Belgacem and Hellara (2011), lower NAV has a greater probability of attracting new investors and gaining market share. An insignificant relationship between NAV and fund performance is observed by Prather et al. (2004) and Belgacem and Hellara (2011).

Age of fund: It is the duration for which the fund has been in existence and measures if the fund is capable of surviving an extremely competitive environment (Prather et al., 2004). A positive relationship for UK mutual funds is observed indicating the existence of economies of experience by Blake and Timmerman (1998). Belgacem and Hellara (2011) and Lobao and Gomes (2015) also report a positive coefficient of fund age indicating a positive relationship between age and performance of fund. According to Ferreira et al. (2012), newer funds have to face higher start-up costs and challenges which may put them at a disadvantage in relation to older funds. They also counter-argue by suggesting that since young funds are new to the industry, they will be more hard working and dedicated to achieve recognition and maintain their survival in the industry. They find no relation of age with performance in case of US funds and negative relationship in case of non-US funds. Also, Otten and Bams (2002) and Yap and Pierce (2008) find a negative relationship. Besides, Prather et al. (2004), Low (2010), Suppa-Aim (2010), Białkowski and Otten (2011) and Murcia (2011) find insignificant relationship between age and performance.

Data

Mutual Funds Data

The study focuses on 237 open-ended Indian equity-based schemes with growth as their objective. The sample period spans from April 2007 to March 2013³ which witnesses the global financial crisis in August 2007. Considering 9 August 2007 (Filardo et al., 2010), when global financial recession sets in, as the demarcation date, we account for its effect on risk-adjusted performance of fund managers.

Daily dividend adjusted NAVs⁴ for the sample schemes are drawn from MFI explorer, the mutual fund database offered by ICRA online limited. The daily NAV values are used to calculate percentage returns, which are employed in the subsequent estimation procedure. The sample observations vary from scheme to scheme as certain mutual fund schemes are being launched subsequent to April 2007. Thus, the observations considered for such schemes are from the date of their inception till March 2013.

Benchmark Indices

For estimating the risk-adjusted performance of mutual funds employing the conditional Carhart (1997) model, the S&P Bombay Stock Exchange 500 index (henceforth, BSE 500 Index) is used as a proxy for market performance. Covering 20 major industries of the economy, it represents about 93 per cent of the total market capitalization on the BSE. It is a broad-based, value-weighted (free-float weighted) index constructed on lines of S&P 500, USA. Daily closing index values are used to compute percentage daily market index returns.

The Carhart (1997) model requires the construction of size, value and momentum factors in addition to the market factor. For the purpose, the constituent stocks of BSE 500 Index are used as sample securities to construct size, value, momentum factors on daily basis. Closing-adjusted stock prices of the BSE 500 Index stocks are used to compute percentage daily returns on sample securities. Closing prices of the securities are adjusted for capitalization changes, such as stocks dividends, stock splits and rights issues.

For constructing size and value factors, natural log of Market Capitalization (MCAP is total market value of a company’s outstanding shares) and Price–to-Book value (P/B ratio is security’s market price divided by its book value) of BSE 500 Index stocks at the end of March of year t are employed respectively. For constructing price momentum factor, the daily closing adjusted share prices of BSE 500 Index stocks for period April of year t − 1 to March of year t are used. The annual data for MCAP and P/B ratio has been obtained from March 2007, whereas the stock price data has been collected from April 2006 to estimate price momentum. Data source is CMIE Prowess, a popularly used financial software in India.

Additionally, the 91-day treasury-bills rate is used as a proxy for risk-free rate of return and is extracted from the official website of the Reserve Bank of India (RBI), the Central Bank of India.

Public Information Variables

Following Ferson and Schadt (1996), we employ economic variables that have been widely used in several studies for predicting security returns and risks over time (see Bauer, Derwall & Otten, 2007; Elton et al., 2012; Moreno & Rodriguez, 2009; Roy & Deb, 2004; Sawicki & Ong, 2000). The variables are as follows: (a) Dividend Yield (DY) is defined as previous 12 months dividend payments for index stocks divided by the price level at the end of the previous month. DY on CNX 500 Index is collected from the National Stock Exchange of India (NSE) website; (b) Treasury Bill yield (TB) is the yield on the treasury bill index as compiled by the NSE. Data source is the NSE website; (c) Term Structure of interest rates (TS) is computed as the difference of yield on 10-year government bond index (long-term bonds) and treasury bill index (short-term bonds). Data on 10-year government bond index and treasury bill index are collected from Bloomberg database and NSE website respectively; and (d) Corporate Default Risk spread (DRS) in the corporate bond market is measured as difference of yield on BBB-rated 10-year corporate bond (low-grade bonds) and AAA-rated 10-year corporate bond (high-grade bonds). Data is collected from Bloomberg database.

Mutual Funds Characteristics Data

The choice of the study period, that is, April 2007 to March 2013 is ascribed to the non-availability of data on fund characteristics for preceding years. Particularly, data on portfolio turnover ratio (PTR) is accessible from March 2007. Portfolio turnover, being an important fund characteristic representing the actively managed mutual fund portfolio, is not omitted from study.

Fund characteristics used in the study are the size of fund, growth in size of fund, expense ratio, portfolio turnover ratio, NAV and age of fund. Since, we use predictive model for panel data regression where relationship between fund performance of year t and characteristics of year t − 1 is examined, hence, the time period used for dependent variable, that is, risk-adjusted performance, is from April 2007 to March 2013 and for independent variables, that is, fund characteristics is from April 2006 to March 2012. Average values are computed for the characteristics for each financial year.

Exhibit A.

Fund Characteristics Used in the Study

Fund Characteristic	Measure
Size of fund (AUM)	It refers to assets under management of the scheme expressed in ₹5 million. The natural log of AUM is used as a variable to compute average fund size from monthly values for every fund on annual basis.⁵
Growth in size of fund (GRAUM)	It represents the rate of change in size of fund which is computed annually as the difference between average AUM in years t − 1 and t − 2.
Expense ratio (EXP)	It represents the total of all of a mutual fund’s annual fund operating expenses, expressed as a percentage of the fund’s average net assets. It is taken as an average of bi-annual values of expense ratio available for each fund.
Portfolio turnover ratio (PTR)	It is defined as minimum of aggregated sales or aggregated purchases of securities, divided by the average 12-month total net assets of fund. Expressed in percentage form, the average annual PTR is computed from monthly values for every fund.
Net asset value (NAV)	NAV for a fund is its price per unit expressed in ₹. It represents market value of each unit of a fund or price at which investors can buy or sell units. For each fund, annual (end of the year) NAVs are used.
Age of fund (FUNDAGE)	It is number of years the fund is in existence since its launch date. For computing age of the fund in years, number of days from the launch date till March of year t is divided by 365. In other words, age of fund increases by one year as time (t) increments by one.

Source: Authors’ own.

The data pertaining to all these variables except FUNDAGE have been collected from MFI explorer, the mutual fund database offered by ICRA online limited. In case of FUNDAGE, launch date and redemption date of mutual fund schemes have been collected from the website valueresearchonline.com.

Model Specification

The relationship between fund characteristics and fund performance is examined by following a two-step procedure involving time series regression (performance measurement of mutual funds) and panel data regression (fund characteristics determining performance of mutual funds). First, a time series regression is carried out to estimate each fund’s risk-adjusted performance on an annual basis. Next, the intercept (alpha, a risk-adjusted measure of performance) generated in time series regression is used as the dependent variable in panel data regression with various fund characteristics serving as independent variables.

Time Series Regression

A comprehensive study of performance benchmarks, conducted by Otten and Bams (2004) for US mutual funds, finds Carhart (1997) four factor model to be statistically the strongest model in conditional setting. In Indian context, Sehgal and Babbar (2017) also suggest conditional version of Carhart (1997) four factor asset pricing model as the optimal performance benchmark to evaluate mutual fund performance. The previous research uses Carhart (1997) model in conditional as well as unconditional settings as it includes size, book-to-market equity and momentum factors, and it lends more explanatory power to estimate alphas of funds (Bollen & Busse, 2001; Kaushik & Pennathur, 2012; Lai & Lau, 2010; Sehgal & Jhanwar, 2008).

Therefore, in the current study, the conditional version of Carhart (1997) four factor asset pricing model is employed as a performance benchmark to measure risk-adjusted performance of mutual funds in India.

Traditionally, the performance of mutual funds is measured using unconditional measures of performance based on the assumption that the expected returns and betas of funds remain constant over time as managers use no information available about the economy to form expectations and to modify their investment strategies. However, empirical evidence provides that the expected returns and risks of funds are not constant but time variant (Bansal & Harvey, 1999; Bessler, Drobetz & Zimmerman, 2009; Ferson & Schadt, 1996; Sawicki & Ong, 2000). In such a case, unconditional measures of performance using fixed betas for the evaluation period may lead to unreliable estimates of alphas by confusing common time variation in risks and risk premiums with superior performance generated by mutual fund managers (Ferson & Schadt, 1996).

Acknowledging the dynamic nature of betas, Jagannathan and Wang (1996) examine the conditional version of capital asset pricing model (CAPM), where expected return of an asset is a linear function of its conditional beta depending on the information available at any point in time. They argue that the assumption of unconditional CAPM of betas being constant over time is not an appropriate assumption and conclude that the conditional CAPM explains the cross-section of returns much better than unconditional model. In a similar vein, Ferson and Schadt (1996) advocate a conditional performance evaluation (CPE) approach, consistent with the semi-strong form of market efficiency as interpreted by Fama (1970).⁶ They evaluate the conditional performance of mutual funds by exploring the effects of incorporating lagged information variables (IVs) in the analysis of investment performance. According to them, an actively managed investment strategy that may be reconstructed by using information available in public domain should not be accredited with a superior performance.

The present study uses the CPE approach as proposed in Ferson and Schadt (1996). They propose following conditional form of model based on CAPM incorporating time varying beta β_pm (Z_t_–1) of portfolio p which is conditional on an observable set of instruments (Z_t_–1):

R ​_{p t} - R ​_{f t} = β ​_{p m} (Z_{t - 1}) (R ​_{M t} - R ​_{f t}) + u ​_{p t}

(1)

E (u ​_{p, t} / Z ​_{t - 1}) = 0

(2)

E (u_{p, t}, (R ​_{M t} - R ​_{f t}) / Z_{t - 1}) = 0

(3),

that is, errors on average are zero and not correlated with market risk premium,

where,

R_pt – R_ft = excess returns on managed portfolio p at time t,

R_Mt – R_ft = excess returns on market index at time t,

R_ft = risk-free rate of return at time t,

β_pm (Z_t_–1) = conditional betas of portfolio p that depends on information vector (Z_t_–1) at time t − 1.

The time variation in betas is incorporated in the performance measurement model using lagged publicly available IVs. Hypothesizing that the fund manager uses only public information available at time t − 1 (denoted by Z_t_–1) to predict returns and risks at time t, it is deduced that the fund’s portfolio beta depends only on Z_t_–1.

Using the Taylor series expansion, beta of the fund (β_pm(Z_t_–1)) is expressed as a linear function of Z_t_–1, that is,

β_{p m} (Z_{t - 1}) = B_{p, M K T} + B_{p, I V} z_{t - 1}

(4),

where,

z_t–1 = Z_t_–1 – E(Z) = a vector of deviations of Z_t_–1 from the unconditional means measuring the unexpected change in the IV.

B_p,MKT = E(β_pm(Z_t_–1)) = an expected conditional beta which is a constant and may be interpreted as an average beta, that is, the unconditional mean of the conditional beta.

B_p,IV = the sensitivity of the conditional beta with respect to deviations of the IVs from their means. This part varies depending on the information available at a point in time.

Primarily, this class of models assumes that returns and risks in time t can be predicted at time t − 1, utilizing publicly available IVs whose values are realized in time t − 1. The core idea of CPE is to eliminate, from the performance measure, an effect of investment strategy which may be replicated using publicly available information, consistent with the semi-strong form of market efficiency as interpreted by Fama (1970).

Following Ferson and Schadt (1996), the present study assumes beta of the fund to be a linear function of information vector (Z_t_–1) which comprises of IVs, namely, DY, TB, slope of term structure and corporate default risk spread. All IVs are demeaned and used in their lagged form.

Conditional Carhart (1997) Four Factor Model

Carhart (1997) four factor asset pricing model adds Jegadeesh and Titman’s (1993) momentum factor besides market, size and value factors. Momentum is a phenomenon which implies that past winners continue to be future winners, while past losers continue to be future losers over the next 3 to 12 months of the portfolio-holding period. In a time series regression framework, the excess returns on equity portfolios are regressed on market premium, zero investment portfolios for size, book equity to market equity and a year momentum in stock returns. The four factor model explains the significant amount of variation in excess portfolio returns relative to CAPM and Fama and French (1993) three factor models. In the present study, P/B is used in place of book equity (BE)-to-market equity ratio (ME). Hence, LMH (low minus high P/B) factor is constructed in place of HML (high minus low BE/ME) factor. Low P/B stocks represent value stocks.

The conditional version of Carhart (1997) four factor model is estimated in the following form:

\begin{array}{l} \begin{matrix} R_{p t} - R_{​} = α_{p} + β_{p, m k t} (R_{M t} - R_{f ​ t}) + β_{p, d y} [(R_{M t} - R_{f ​ t}) * (d y_{t - 1})] + β_{p, t b} [(R_{M t} - R_{​}) * \end{matrix} \\ \begin{matrix} (t b_{t - 1})] + β_{p, t s} [(R_{M t} - R_{f ​ t}) * (t s_{t - 1})] + β_{p, d r s} [(R_{M t} - R_{f ​ t}) * (d r s_{t - 1})] + s_{p, m k t} (S M B_{t}) + \end{matrix} \\ \begin{matrix} s_{p, d y} [(S M B_{t}) * (d y_{t - 1}) + s_{p, t b} [(S M B_{t}) * (t b_{t - 1})] + s_{p, t s} [(S M B_{t}) * (t s_{t - 1})] + \end{matrix} \\ \begin{matrix} s_{p, d r s} [(S M B_{t}) * (d r s_{t - 1})] + l_{p, m k t} (L M H_{t}) + l_{p, d y} [(L M H_{t}) * (d y_{t - 1})] + l_{p, t b} [(L M H_{t}) * \end{matrix} \\ \begin{matrix} (t b_{t - 1})] + l_{p, t s} [(L M H_{t}) * (t s_{t - 1})] + l_{p, d r s} [(L M H_{t}) * (d r s_{t - 1})] + w_{p, m k t} (W M L_{t}) + \end{matrix} \\ \begin{matrix} w_{p, d y} [(W M L_{t}) * (d y_{t - 1})] + w_{p, t b} [(W M L_{t}) * (t b_{t - 1})] + w_{p, t s} [W M L_{t}) * (t s_{t - 1})] + \end{matrix} \\ \begin{matrix} w_{p, d r s} [(W M L_{t}) * (d r s_{t - 1})] + \in_{p t} \end{matrix}, \end{array}

(5)

where,

R_pt – R_ft = excess returns on managed portfolio p at time t.

R_Mt – R_ft = excess returns on market index at time t.

α_p = the intercept term, conditional alpha, measures the stock selection ability of mutual fund portfolio p. It is the average difference between the actual returns on mutual fund portfolio and the expected returns on dynamic investment strategy conditional on state of the economy.

SMB_t = size factor measured as difference between the average returns on portfolio of small and large market capitalization stocks.

LMH_t = value factor measured as difference between the average returns on portfolio of low and high P/B stocks.

WML_t = stock momentum factor measured as difference between the average returns of portfolio of winners and losers stocks.

β_mkt,s_mkt, l_mkt, w_mkt = sensitivity coefficients with respect to fundamental risk factors, namely, market, size, value and stock momentum respectively.

β_p,dy, β_p,tb, β_p,ts, β_p,drs, s_p,dy, s_p,tb, s_p,ts, s_p,drs, l_p,dy, l_p,tb, l_p,ts, l_p,drs, w_p,dy, w_p,tb, w_p,ts, w_p,drs = sensitivity coefficients with respect to interaction terms between fundamental risk factors and lagged values of IVs, namely, DY, TB, TS and DRS.

dy_t_–1, tb_t_–1, ts_t_–1, drs_t_–1 = demeaned IVs.

∈_pt = error term.

Panel Data Regression

Further, to examine the role of fund characteristics in determining mutual fund performance in India, panel data regression analysis is conducted. Following Chevalier and Ellison (1999)⁷ and Yap and Pierce (2008), relationship of fund characteristics with risk-adjusted performance is determined using predictive model in panel data regression where risk-adjusted performance (dependent variable), measured as alpha or intercept provided by conditional Carhart model, for year t is regressed against fund characteristics for year t − 1(independent variables).

The following equation is used in the study:

\begin{matrix} α_{p, t} = β_{0} + β_{1} A U M_{p, t - 1} + β_{2} G R A U M_{p, t - 1} + β_{3} E X P_{p, t - 1} + β_{4} P T R_{p, t - 1} + \\ β_{5} N A V_{p, t - 1} + β_{6} F U N D A G E_{p, t - 1} + e_{p, t} \end{matrix},

(6)

where

p = 1, 2, 3………, 237. N = 237 (number of schemes)

t = 2007–2008, 2008–2009………., 2012–2013. T = 6 (time Period)

α_p,t = conditional alpha which measures the stock selection ability or risk-adjusted performance of mutual fund p in period t estimated using conditional Carhart (1997) four factor model employing equation (5),

β₀ = a constant,

β₁, β₂, β₃, β₄, β₅, β_{6 =} sensitivity coefficients,

AUM_p,t_–1 = size of fund p in period t − 1,

GRAUM_p,t_–1 = growth in size of fund p in period t − 1,

EXP_p,t_–1 = expense ratio of fund p in period t − 1,

PTR_p,t_–1 = PTR of fund p in period t − 1,

NAV_p,t_–1 = net asset value of fund p in period t − 1,

FUNDAGE_p,t_–1 = age of fund p in period t − 1,

e_pt = an error term.

Estimation Procedure

The risk factors are constructed to be incorporated as explanatory variables in time series regression, that is, conditional Carhart (1997) four factor asset pricing model. We compute daily excess returns for each sample fund for the sample period using dividend adjusted NAVs. The NAV-based returns are net of expenses and management fees but are gross of any load charges.⁸ Excess returns on fund are computed as the difference between NAV-based percentage returns and risk-free rate of return. Excess returns on market index are similarly estimated as difference between index values based percentage returns on BSE 500 Index and risk-free rate of return.

For constructing size and value factors, methodology suggested by Fama and French (1993) is adopted. First, BSE 500 Index stocks are ranked as per MCAP (at the end of March of year t) and median MCAP is used to divide them into two groups where the top 50 per cent are classified as big (B) and bottom 50 per cent are classified as small (S). Next, they are ranked on P/B ratio (at the end of March of year t), and median P/B ratio is used to divide them in two groups where the top 50 per cent are classified as high (H) and bottom 50 per cent are classified as low (L). The intersection of the two MCAP and two P/B groups leads to the formation of four equally weighted, double-sorted portfolios,⁹ namely, BIG-HIGH (B/H), BIG-LOW (B/L), SMALL-HIGH (S/H) and SMALL-LOW (S/L), where B/H represents large cap and high P/B stocks and so on. Daily returns are computed on each portfolio for the period April of year t to the March of year t + 1. Using fresh information on MCAP and P/B for sample stocks, at the end of March of every year, the portfolios are rebalanced starting from March 2007 to March 2012.

Size factor is expressed as Small Minus Big (SMB) and is computed as:

S M B = (S / L + S / H) / 2 - (B / L + B / H) / 2

(7)

This size factor is independent of value factor by construction. Value factor is expressed as Low Minus High (LMH) and similarly estimated, which is free from any size effects by construction as given below:

L M H = (S / L + B / L) / 2 - (S / H + B / H) / 2

(8)

In the present study, P/B is used in place of BE/ME. Hence, LMH (low minus high P/B) factor is constructed in place of HML (high minus low BE/ME) factor. Accordingly, the interpretation of value factor will be inverse.

For constructing Carhart (1997) momentum factor, sample stocks are ranked as per average daily returns calculated from April of year t − 1 to March of year t, that is, a year immediately prior to portfolio formation. These ranked stocks are then grouped into five equally weighted portfolios—P1 to P5—which are held for next 1 year, that is, from April of year t till March of year t + 1. The top 20 per cent past performers are denoted as P5 or winners portfolio, and the bottom 20 per cent based on past returns are denoted as P1 or losers portfolio. Portfolios are rebalanced annually starting from 2007 to 2012. The momentum factor, WML (winners minus losers), is estimated as the difference between average returns on winners (P5) and losers portfolios (P1).

The multiple time series regression analysis is conducted using the ordinary least squares (OLS) method for estimating the model. Also, an intercept dummy variable is incorporated in the model to account for global financial crisis. Before August 9, 2007, dummy takes the value ‘0’, and on and after this date, dummy assumes the value ‘1’. The statistical significance of the parameters is tested at 5 per cent level (two-tailed basis).

Wherever necessary, White (1980) heteroscedasticity consistent standard errors and Newey and West (1987) heteroscedasticity and autocorrelation consistent standard errors are computed. Corrections are made for multicollinearity¹⁰ following the approach suggested by Giliberto (1985) and Lance (1988). The transformed variables are used for further estimation purposes.

Conditional Carhart model is estimated for 6 individual years starting from April 2007 to March 2013 for sample schemes. Alphas, interpreted as a measure of risk-adjusted performance of mutual fund managers, are used as dependent variable in panel data regression analysis.

A panel of 237 sample schemes is created for 6 years. It is a short panel with T = 6 and N = 237, where T being time period and N being number of schemes. The study at the outset carries out pooled regression and conducts a Breusch and Pagan Lagrangian multiplier test for random effects (chi-square statistic is 0.01 and p value is 0.46). Further, the study conducts a Hausman specification test to decide if individual effects are random or fixed. Hausman test results in favour of fixed effects model (chi-square statistic is 246.53 and p value is 0.00; Table 1). Results of panel regression in the study are, therefore, based on fixed effects estimator.

For panel data regression, the problem of multicollinearity among explanatory variables is examined through pair-wise correlation and variance inflation factor (VIF) between them (Table 2). Correlations show the linear association between the fund characteristics. Correlation coefficient is found to be high (greater than 0.50) in case of fund age and NAV (0.65). High correlation implies older funds tend to give higher NAVs which may actually be an outcome of compounding effects owing to reinvestment clause in most funds schemes. High correlation between the fund characteristics may be driven by multico-llinearity between them. However, VIF is found to be below 2 for all explanatory variables assuring that multicollinearity is not a serious problem in the present case (Gujarati, 2004). To correct heteroscedasticity across individuals and autocorrelation over time within individuals, cluster-robust standard errors of fixed effects model are computed that cluster on individual.

Table 1.

Panel Data Analysis: Fixed Effects Estimator

Fund Characteristics	Coefficient	t-statistic
AUM	−0.00017*	−4.98
GRAUM	−0.00008*	−2.00
EXP	0.00002	0.25
PTR	0.00000	0.86
NAV	−0.00002*	−5.50
FUNDAGE	0.00007*	6.51
INTERCEPT	0.00093*	2.87
F test	Statistic	19.01*
	Prob.	0.00
Breusch and Pagan Lagrangian Multiplier Test for random effects	X² statistic	0.01
	Prob.	0.46
Hausman Specification Test	X² statistic	246.53*
	Prob.	0.00

Source: Authors’ calculations.

Notes: This table shows the estimates of the coefficients obtained from estimating equation number (6) for a panel of sample schemes using fixed effects estimator. The statistics for F test, Breusch and Pagan Lagrangian Multiplier Test and Hausman Specification Test are also provided. Heteroscedasticity and Autocorrelation consistent t-statistics are reported. AUM is size of fund, GRAUM is growth in size of fund, EXP is expense ratio, PTR is portfolio turnover ratio, NAV is net asset value and FUNDAGE is age of fund. * denotes statistical significance at 5% level.

Table 2.

Mean Correlation Matrix and Variance Inflation Factor Matrix for Fund Characteristics

Panel A: Mean Correlation Matrix
Fund Characteristic	AUM	GRAUM	EXP	PTR	NAV	FUNDAGE
AUM	1.00
GRAUM	0.19	1.00
EXP	−0.49	−0.09	1.00
PTR	−0.17	0.08	0.06	1.00
NAV	0.23	0.28	−0.16	−0.11	1.00
FUNDAGE	0.17	0.24	−0.15	−0.08	0.65	1.00
Panel B: Variance Inflation Factor matrix (VIF)
Fund Characteristic					VIF
AUM					1.53
GRAUM					1.11
EXP					1.43
PTR					1.05
NAV					1.88
FUNDAGE					1.75

Source: Authors’ calculations.

Notes: Panel A provides correlation coefficients computed as the averages of annual pair-wise correlations of fund characteristics for the period April 2006 to March 2012. Panel B gives the variance inflation factor for each fund characteristic. AUM is size of fund, GRAUM is growth in size of fund, EXP is expense ratio, PTR is portfolio turnover ratio, NAV is net asset value and FUNDAGE is age of fund.

Empirical Results

Descriptive Statistics

Panel A of Table 3 presents the average descriptive statistics for sample mutual fund schemes and various benchmark portfolios used in the study over the period April 2007 to March 2013. The mutual funds schemes exhibit average excess returns of −0.00522 per cent (annualized¹¹ excess returns of 1.30 per cent) during the sample period. The mutual fund schemes exhibit negative excess returns since the global financial crisis period. Only 81 out of 237 schemes provide positive excess returns, that is, returns over and above risk-free rate of return. 156 schemes, however, observe negative excess returns, while 153 out of those schemes report higher negative excess returns than that of market which generates an average annualized excess return of −0.11 per cent. For the sample period, particularly, Sahara Banking and Financial Services Fund, launched in 2008–2009, exhibits the highest daily average excess return of 0.06550 per cent (annualized 16.37 per cent), whereas Escorts Infrastructure Fund reports the lowest daily average excess return of −0.08830 per cent (annualized −22.07 per cent).¹²

The annualized average standard deviation of returns of mutual fund schemes remains around 17 per cent for the sample period. The highest volatility is observed in case of JM Hi Fi Fund and the least volatile scheme is Principal Resurgent India Equity Fund.

The average skewness for sample schemes is 0.16 ranging from −5.87 (Sahara Taxgain fund) to 10.45 (Sahara Infrastructure Fund) through April 2007 to March 2013 period. Approximately 55 per cent of the 237 schemes exhibit negative skewness. Return distributions of all the mutual fund schemes are leptokurtic. Jarque-Bera (JB) normality test is applied for testing whether the series is normally distributed. JB statistic is statistically significant at 5 per cent level for all mutual fund schemes, confirming that their returns distributions are non-normal in nature.

The average excess returns for market index (S&P BSE 500 index) are negative (annualized −0.11 per cent). In contrast, the annualized standard deviation is 26.55 per cent, indicating that high risk associated with the market index is not fetching adequate returns for the investors. The market index exhibits little skewness but strong leptokurtosis over the study period. JB statistic is 4580.05, suggesting that the distribution of the market index is not normal owing to leptokurtosis.

The annualized average returns for SMB associated with size factor are 11.17 per cent. The positive SMB, referred to as the size premium, indicates that stocks of firms with smaller MCAP outperform stocks of firms with larger MCAP during the study period in the Indian market. On the other hand, the average standard deviation associated with size factor is around 8 per cent. The SMB factor exhibits negative skewness and leptokurtosis during the sample period. JB statistic of 699.37 for the sample period confirms the non-normal distribution of the returns from size factor.

Table 3.

Panel A: Descriptive Statistics for Sample Schemes and Factor Portfolios

	Mean (%)	Standard Deviation (%)	Skewness	Kurtosis	Jarque-Bera	p value
MF Schemes (237)	−0.00522	1.09935	0.16563	19.29899	132281.58672	0.00000
MKT	−0.00045	1.67970	0.13155	11.69979	4580.05400	0.00000
SMB	0.04470	0.53510	−0.08142	6.39726	699.37530	0.00000
LMH	0.00308	0.50270	0.18736	4.28283	107.98230	0.00000
WML	0.02190	0.85050	−0.69386	8.94654	2254.31900	0.00000

Source: Authors’ calculations.

Notes: Average descriptive statistics for 237 mutual fund schemes and various benchmark portfolios for the sample period using daily data are shown. MKT is excess returns on market index (BSE 500 Index), SMB is size factor, LMH represents value factor, WML is stock momentum factor. Descriptive statistics of sample schemes are available from the authors on request.

The average annualized returns for LMH representing the value factor are 0.77 per cent. However, the volatility in returns measured by standard deviation is almost 8 per cent. The positive average returns for LMH, referred to as value premium, indicates that stocks of firms with low P/B ratio (value stocks) tend to outperform stocks of firms with high P/B ratio (growth stocks) during the study period in the Indian market. The positively skewed and leptokurtic distribution of returns is observed for LMH factor and high value of JB statistic (107.98) for the total period suggests that the series is not normally distributed.

The mean returns for WML associated with momentum in stock returns also are positive for the sample period. This implies that the winner stocks (over past 12 months) on an average have generated larger returns than loser stocks (over past 12 months) signifying the presence of stock momentum effect in the Indian market for the sample period studied. The non-normality of distribution of returns is confirmed through JB statistic.

Panel B of Table 3 presents the description of fund characteristics over the period April 2006 to March 2012. The number of mutual fund schemes shows a 73 per cent increase, from 756 schemes in 2006–2007 to 1,309 schemes in 2011–2012. Average size of fund during the sample period is ₹6,258.76 million (USD 96.29 million) with a maximum of ₹7,228.75 million (USD 111.21 million) in 2010–2011. On individual analysis of schemes, HDFC Equity Fund shows the highest average AUM (₹55,524.9 million) and Escorts Leading Fund reports the lowest average AUM (₹9.52 million) during the sample period. Average growth rate of AUM amounts to 2 per cent over total sample period, indicating inflows of money to the total corpus of mutual fund houses. Maximum average growth rate observed is 76.05 per cent in case of Sahara Banking and Financial Services Fund and a minimum average growth rate is –54.74 per cent in case of BNP Paribas Mid Cap Fund. Average expense ratio over the years is about 2.25 per cent, which remains similar throughout the sample period indicating that different funds may charge differently, but the ceiling rate set by SEBI does not allow funds to deviate much while charging its annual recurring expenses.¹³ When analyzed on individual basis, it is observed that Birla Sun Life Small & Midcap Fund reports the highest average expense ratio (3.69 per cent) and LIC Nomura MF Growth Fund has the least average expense ratio (1.71 per cent) through the sample period. The average turnover ratio for the sample period is 89.34 per cent, with a maximum of 101.07 per cent observed in 2008–2009 and a minimum of 75.03 per cent in 2011–2012. For the total sample period, on an average, Religare Invesco Equity Fund and Franklin Pharma Fund report the highest (263.11 per cent) and lowest (4.1 per cent) portfolio turnover ratios respectively. Average NAV for sample schemes is about ₹32.90. Average age of fund is about 6.56 years. The oldest mutual fund scheme in our sample is UTI Mastershare, and the youngest mutual fund scheme is UTI Top 100 Fund.

Time Series Regression

The time series regressions for 237 mutual fund schemes using conditional Carhart (1997) four factor model is estimated for 6 individual years starting from April 2007 to March 2013 for sample schemes. Table 4 reports annual alpha of 237 mutual fund schemes estimated using conditional Carhart (1997) four factor model in a time series regression framework. These alphas interpreted as a measure of risk-adjusted performance of mutual fund managers are employed as dependent variable in panel data regression analysis.

Table 3.

Panel B: Annual Averages of the Fund Characteristics

Year	2006–2007	2007–2008	2008–2009	2009–2010	2010–2011	2011–2012	Average
No. of mutual fund schemes (at the end of March of respective year)	756	956	1001	882	1131	1309	1005.83
Mean AUM (₹ in millions)	5376.32295	6925.50487	4739.20802	6258.32847	7228.75682	7024.46198	6258.76380
Mean GRAUM	0.13451	0.17318	−0.38874	0.23440	0.09019	−0.12343	0.02002
Mean EXP (%)	2.22224	2.25178	2.27301	2.28277	2.26351	2.25669	2.25833
Mean PTR (%)	78.88484	96.63686	101.07817	93.00188	91.43131	75.03065	89.34395
Mean NAV (₹)	32.71298	32.91299	24.11134	34.76779	36.45240	36.45458	32.90201
Mean FUNDAGE (Years)	4.83978	5.12368	5.87519	6.84640	7.85932	8.82932	6.56228

Source: Authors’ calculations.

Notes: The table reports values for fund characteristics over April 2006 through March 2012 period. All variables are reported on annual basis. Number of mutual fund schemes at the end of March of respective year is reported. AUM is assets under management representing the size of fund expressed in ₹ millions, GRAUM is growth in size of the fund, EXP is Expense ratio of the fund expressed in percentage, PTR is portfolio turnover ratio of the fund expressed in percentage, NAV is Net Asset value expressed in ₹, FUNDAGE is age of the fund expressed in years. Average is the mean value of each variable over the entire April 2006 to March 2012 period.

Table 4.

Alpha (risk-adjusted returns) of 237 Mutual Fund Schemes Estimated Using Conditional Carhart (1997) Four Factor Asset Pricing Model in a Time Series Regression Framework

YearsNAMES OF SCHEMES	2007–2008	2008–2009	2009–2010	2010–2011	2011–2012	2012–2013	Average Alpha
YearsNAMES OF SCHEMES	α	α	α	α	α	α
Baroda Pioneer Growth Fund	−0.00001	0.00014	−0.00007	−0.00006	−0.00028	−0.00006	−0.00006
Birla Sun Life Advantage Fund	−0.00058	−0.00050	−0.00032	−0.00018	−0.00027	−0.00012	−0.00033
Birla Sun Life Basic Industries	−0.00035	−0.00066	−0.00011	−0.00033	−0.00030	−0.00004	−0.00030
Birla Sun Life Buy India Fund	−0.00051	−0.00030	0.00023	−0.00001	−0.00016	0.00016	−0.00010
Birla Sun Life Dividend Yield Plus	−0.00032	−0.00028	0.00021	−0.00001	−0.00016	−0.00023	−0.00013
Birla Sun Life Equity Fund	−0.00039	−0.00038	0.00010	−0.00021	−0.00023	−0.00011	−0.00020
Birla Sun Life Frontline Equity Fund	−0.00025	−0.00014	−0.00004	−0.00009	−0.00022	−0.00010	−0.00014
Birla Sun Life India GenNext Fund	−0.00059	−0.00028	0.00009	0.00006	0.00002	0.00009	−0.00010
Birla Sun Life India Opportunities Fund	−0.00076	−0.00082	0.00054	−0.00019	−0.00026	−0.00041	−0.00032
Birla Sun Life Infrastructure Fund	−0.00077	−0.00038	−0.00007	−0.00042	−0.00029	−0.00013	−0.00034
Birla Sun Life Long-term Advantage Fund	−0.00095	−0.00016	0.00033	−0.00001	−0.00008	−0.00003	−0.00015
Birla Sun Life Mid-cap Fund	0.00003	−0.00067	0.00022	−0.00032	−0.00024	−0.00025	−0.00020
Birla Sun Life MNC Fund	−0.00064	−0.00041	0.00090	0.00009	0.00007	−0.00023	−0.00004
Birla Sun Life New Millennium	−0.00078	−0.00071	0.00087	0.00004	−0.00020	−0.00063	−0.00023
Birla Sun Life Pure Value Fund	NA	−0.00011	0.00076	0.00001	−0.00026	0.00002	0.00008
Birla Sun Life Small & Mid-cap Fund	−0.00078	−0.00149	0.00083	−0.00030	−0.00004	−0.00010	−0.00031
Birla Sun Life Special Situations Fund	−0.00090	−0.00024	−0.00019	−0.00030	−0.00012	0.00007	−0.00028
Birla Sun Life Tax Plan	−0.00054	−0.00033	−0.00014	−0.00008	−0.00021	−0.00010	−0.00023
Birla Sun Life Tax Relief 96	−0.00044	−0.00047	−0.00010	−0.00025	−0.00030	−0.00022	−0.00030
Birla Sun Life Top 100 Fund	−0.00073	−0.00001	0.00002	0.00009	−0.00010	−0.00004	−0.00013
BNP Paribas Dividend Yield Fund	−0.00072	−0.00021	0.00062	0.00010	−0.00004	−0.00030	−0.00009
BNP Paribas Equity Fund	−0.00030	−0.00035	−0.00032	−0.00009	−0.00017	−0.00022	−0.00024
BNP Paribas Mid-cap Fund	−0.00113	−0.00255	0.00040	−0.00006	0.00026	0.00024	−0.00047
BNP Paribas Opportunities Fund	−0.00089	−0.00090	−0.00028	−0.00021	−0.00002	0.00009	−0.00037
BNP Paribas Tax Advantage Plan	−0.00048	−0.00078	−0.00002	−0.00013	0.00015	−0.00014	−0.00023
Canara Robeco Emerging Equities	−0.00055	−0.00139	0.00065	−0.00004	0.00010	0.00006	−0.00019
Canara Robeco Equity Taxsaver	−0.00032	−0.00016	0.00007	−0.00011	−0.00018	−0.00025	−0.00016
Canara Robeco Infrastructure Fund	0.00029	−0.00008	−0.00022	−0.00005	−0.00014	−0.00012	−0.00005
DSP BlackRock Equity Fund	−0.00007	−0.00030	0.00004	−0.00003	−0.00019	−0.00023	−0.00013
DSP BlackRock India Tiger Fund	−0.00021	−0.00031	−0.00031	−0.00026	−0.00031	−0.00016	−0.00026
DSP BlackRock Natural Resources & New Energy Fund	NA	0.00018	0.00025	0.00015	−0.00041	−0.00028	−0.00002
DSP BlackRock Opportunities Fund	−0.00040	−0.00031	0.00007	0.00002	−0.00032	−0.00037	−0.00022
DSP BlackRock Small and Mid-cap Fund	−0.00051	−0.00082	0.00099	0.00006	−0.00011	0.00009	−0.00005
DSP BlackRock Tax Saver Fund	−0.00013	−0.00046	0.00037	−0.00007	−0.00012	0.00006	−0.00006
DSP BlackRock Technology.com Fund	0.00019	−0.00048	0.00115	−0.00012	−0.00031	−0.00047	−0.00001
DSP BlackRock Top 100 Equity Fund	−0.00015	−0.00014	−0.00015	−0.00007	−0.00015	−0.00023	−0.00015
DWS Alpha Equity Fund	0.00004	−0.00015	−0.00014	−0.00014	−0.00035	−0.00025	−0.00016
DWS Investment Opportunity Fund	0.00043	−0.00016	0.00026	−0.00029	−0.00036	−0.00012	−0.00004
DWS Tax Saving Fund	0.00020	−0.00031	0.00018	−0.00027	−0.00037	−0.00004	−0.00010
Escorts Growth Plan	−0.00057	−0.00082	−0.00006	−0.00026	−0.00041	−0.00014	−0.00038
Escorts High Yield Equity Plan	−0.00014	−0.00119	0.00011	0.00119	−0.00052	0.00005	−0.00008
Escorts Infrastructure Fund	0.00005	−0.00213	0.00004	−0.00084	−0.00083	−0.00025	−0.00066
Escorts Leading Sectors Fund	NA	0.00010	0.00010	0.00010	0.00010	0.00010	0.00010
Escorts Tax Plan	−0.00059	−0.00076	−0.00009	−0.00013	−0.00039	−0.00028	−0.00037
Franklin Asian Equity Fund	−0.00067	0.00139	0.00091	0.00024	0.00051	−0.00019	0.00036
Franklin FMCG Fund	−0.00046	−0.00039	0.00094	0.00035	0.00090	0.00034	0.00028
Franklin India Bluechip	−0.00036	−0.00005	−0.00001	−0.00006	−0.00019	−0.00034	−0.00017
Franklin India Flexi-cap Fund	−0.00069	−0.00015	0.00006	−0.00003	−0.00018	−0.00017	−0.00019
Franklin India Opportunities Fund	−0.00033	−0.00036	−0.00030	−0.00010	−0.00021	−0.00026	−0.00026
Franklin India Prima Fund	−0.00063	−0.00069	0.00023	−0.00027	−0.00020	−0.00013	−0.00028
Franklin India Prima Plus	−0.00034	−0.00024	−0.00013	−0.00012	−0.00019	−0.00024	−0.00021
Franklin India Smaller Companies Fund	−0.00105	−0.00103	0.00058	−0.00021	−0.00013	0.00048	−0.00023
Franklin India Taxshield	−0.00020	−0.00020	0.00002	−0.00006	−0.00015	−0.00025	−0.00014
Franklin Infotech Fund	−0.00028	0.00017	0.00134	0.00021	−0.00043	−0.00076	0.00004
Franklin Pharma Fund	−0.00049	−0.00005	0.00147	0.00004	0.00035	0.00022	0.00026
HDFC Capital Builder Fund	−0.00022	−0.00043	0.00027	−0.00001	−0.00019	−0.00016	−0.00012
HDFC Core & Satellite Fund	−0.00044	−0.00027	0.00055	0.00002	−0.00031	−0.00020	−0.00011
HDFC Equity Fund	−0.00048	−0.00020	0.00025	0.00012	−0.00016	−0.00010	−0.00010
HDFC Focused Large-cap Fund	−0.00055	−0.00034	0.00059	0.00030	−0.00005	−0.00011	−0.00003
HDFC Growth Fund	0.00007	−0.00032	0.00018	0.00012	−0.00003	−0.00015	−0.00002
HDFC Infrastructure Fund	NA	−0.00057	0.00067	−0.00012	−0.00014	0.00024	0.00002
HDFC Long-term Advantage Fund	−0.00061	−0.00041	0.00021	0.00006	−0.00017	−0.00019	−0.00018
HDFC Premier Multi-cap Fund	−0.00072	−0.00025	0.00046	−0.00002	−0.00019	−0.00031	−0.00017
HDFC Taxsaver	−0.00060	−0.00028	0.00026	−0.00007	−0.00022	−0.00024	−0.00019
HDFC Top 200	−0.00039	−0.00001	0.00007	0.00005	−0.00012	−0.00010	−0.00008
HSBC Dynamic Fund	−0.00073	−0.00026	−0.00022	0.00016	−0.00016	−0.00026	−0.00024
HSBC Equity Fund	0.00008	−0.00016	−0.00023	−0.00006	−0.00031	−0.00030	−0.00016
HSBC India Opportunities Fund	−0.00022	−0.00031	−0.00022	0.00000	−0.00020	−0.00026	−0.00020
HSBC Mid-cap Equity Fund	−0.00061	−0.00116	0.00011	−0.00050	−0.00008	−0.00009	−0.00039
HSBC Progressive Themes Fund	−0.00012	−0.00047	−0.00045	−0.00069	0.00023	−0.00001	−0.00025
HSBC Tax Saver Equity Fund	−0.00043	−0.00010	0.00016	−0.00008	−0.00001	0.00000	−0.00008
HSBC Unique Opportunities Fund	−0.00075	−0.00107	−0.00036	0.00013	−0.00017	−0.00005	−0.00038
ICICI Prudential Banking and Financial Services Fund–Retail	NA	−0.00051	0.00049	0.00041	0.00016	0.00080	0.00027
ICICI Prudential Discovery Fund	−0.00060	−0.00042	0.00098	−0.00002	−0.00002	0.00004	−0.00001
ICICI Prudential Dynamic Plan	−0.00048	−0.00004	0.00034	0.00004	−0.00005	−0.00017	−0.00006
ICICI Prudential Exports and Other Services Fund	−0.00052	−0.00066	0.00036	−0.00003	−0.00004	0.00013	−0.00013
ICICI Prudential FMCG	0.00001	−0.00074	0.00091	0.00034	0.00064	0.00001	0.00020
ICICI Prudential Focused Bluechip Equity Fund – Ret	NA	0.00140	0.00046	0.00045	0.00016	−0.00011	0.00047
ICICI Prudential Infrastructure Fund	0.00088	−0.00013	−0.00009	−0.00021	−0.00037	−0.00019	−0.00002
ICICI Prudential Mid-cap Fund	−0.00029	−0.00114	0.00050	−0.00040	−0.00029	−0.00014	−0.00029
ICICI Prudential Taxplan	−0.00022	−0.00039	0.00031	−0.00011	−0.00021	−0.00019	−0.00013
ICICI Prudential Technology Fund	−0.00193	−0.00110	0.00340	0.00082	0.00031	−0.00037	0.00019
ICICI Prudential Top 100 Fund	−0.00036	−0.00006	−0.00012	−0.00012	−0.00015	−0.00025	−0.00018
ICICI Prudential Top 200 Fund	−0.00050	−0.00021	−0.00001	−0.00007	−0.00020	−0.00021	−0.00020
IDFC Classic Equity Fund	−0.00011	−0.00010	−0.00010	−0.00010	−0.00010	−0.00010	−0.00010
IDFC Imperial Equity Fund	0.00044	0.00070	−0.00017	0.00006	−0.00045	−0.00033	0.00004
IDFC Premier Equity Fund	0.00105	−0.00074	0.00066	0.00013	−0.00004	0.00011	0.00020
IDFC Strategic Sector (50−50) Equity Fund	NA	0.00035	−0.00049	0.00029	−0.00015	−0.00013	−0.00003
ING C.U.B. Fund	−0.00029	−0.00125	0.00068	−0.00007	−0.00040	0.00008	−0.00021
ING Contra Fund	−0.00027	−0.00005	−0.00023	−0.00019	−0.00008	−0.00002	−0.00014
ING Core Equity Fund	−0.00003	−0.00021	−0.00003	0.00005	−0.00018	−0.00011	−0.00009
ING Dividend Yield Fund	0.00016	−0.00007	0.00104	0.00034	−0.00007	0.00002	0.00023
ING Domestic Opportunities Fund	−0.00028	−0.00027	−0.00014	−0.00016	−0.00025	0.00012	−0.00016
ING Large-cap Equity Fund	−0.00018	0.00016	−0.00037	0.00008	−0.00005	−0.00007	−0.00007
ING Mid-cap Fund	−0.00053	−0.00098	0.00046	−0.00004	−0.00003	0.00006	−0.00018
ING Multi Manager Equity Fund	−0.00040	−0.00023	−0.00006	−0.00028	0.00021	−0.00025	−0.00017
ING Tax Saving Fund	−0.00077	−0.00064	0.00011	0.00004	−0.00029	−0.00031	−0.00031
JM Basic Fund	−0.00021	−0.00097	−0.00026	−0.00049	−0.00028	−0.00024	−0.00041
JM Emerging Leaders Fund	0.00084	−0.00303	0.00054	−0.00067	0.00037	−0.00073	−0.00045
JM Equity	−0.00017	−0.00054	−0.00051	−0.00021	−0.00030	−0.00030	−0.00034
JM Hi Fi Fund	−0.00007	−0.00210	−0.00147	−0.00070	−0.00096	−0.00096	−0.00104
JM Large-cap Fund	−0.00026	−0.00023	−0.00057	0.00011	−0.00018	−0.00018	−0.00022
JM Mid-cap Fund	−0.00093	−0.00054	0.00027	−0.00061	−0.00005	−0.00005	−0.00032
JM Multi Strategy Fund	NA	0.00330	−0.00087	−0.00039	−0.00019	−0.00009	0.00035
JM Tax Gain Fund	NA	−0.00174	0.00002	−0.00046	0.00004	−0.00022	−0.00047
JPMorgan India Equity Fund	−0.00019	−0.00009	0.00001	0.00020	−0.00007	−0.00019	−0.00006
JPMorgan India Smaller Companies Fund	−0.00242	−0.00177	0.00071	0.00004	−0.00004	0.00021	−0.00054
Kotak 50	−0.00004	−0.00017	−0.00012	−0.00007	−0.00025	−0.00019	−0.00014
Kotak Classic Equity	−0.00045	0.00000	0.00011	−0.00013	0.00018	0.00013	−0.00003
Kotak Emerging Equity Scheme	−0.00031	−0.00132	0.00069	−0.00038	0.00026	0.00022	−0.00014
Kotak Lifestyle Fund	−0.00088	−0.00071	0.00018	0.00016	0.00048	−0.00002	−0.00013
Kotak Mid-cap Fund	−0.00095	−0.00094	0.00021	−0.00014	−0.00005	0.00013	−0.00029
Kotak Opportunities Fund	0.00042	−0.00032	−0.00014	−0.00016	−0.00023	−0.00022	−0.00011
Kotak Taxsaver	−0.00035	−0.00053	−0.00014	−0.00009	−0.00010	−0.00009	−0.00022
L&T Contra Fund	−0.00098	−0.00105	−0.00024	−0.00012	0.00011	−0.00007	−0.00039
L&T Equity Fund	−0.00050	−0.00010	0.00015	0.00021	−0.00016	−0.00031	−0.00012
L&T Global Advantage Fund	−0.00078	−0.00097	−0.00002	−0.00030	0.00025	−0.00033	−0.00036
L&T Growth Fund	−0.00023	−0.00038	−0.00027	−0.00005	−0.00019	−0.00024	−0.00023
L&T Hedged Equity Fund	0.00066	−0.00043	−0.00043	−0.00003	−0.00012	−0.00026	−0.00010
L&T India Large-cap Fund	−0.00068	0.00016	0.00042	0.00039	−0.00006	−0.00031	−0.00001
L&T India Special Situations Fund	−0.00080	−0.00017	0.00052	0.00014	0.00000	0.00012	−0.00003
L&T Mid-cap Fund	−0.00040	−0.00076	0.00046	−0.00020	−0.00017	−0.00019	−0.00021
L&T Multi-cap Fund	−0.00090	−0.00052	−0.00019	−0.00023	−0.00022	−0.00021	−0.00038
L&T Opportunities Fund	0.00035	−0.00046	−0.00007	−0.00020	−0.00023	−0.00027	−0.00015
L&T Tax Advantage Fund	−0.00031	0.00000	0.00033	0.00037	−0.00010	−0.00024	0.00001
L&T Taxsaver Fund	−0.00069	−0.00037	−0.00008	−0.00005	−0.00023	−0.00026	−0.00028
LIC Nomura Equity Fund	0.00011	−0.00021	−0.00041	−0.00007	−0.00024	−0.00029	−0.00018
LIC Nomura MF Growth Fund	−0.00091	0.00025	−0.00055	0.00013	0.00000	−0.00024	−0.00022
LIC Nomura MF Infrastructure Fund	NA	0.00008	−0.00091	−0.00032	−0.00028	0.00002	−0.00028
LIC Nomura MF Opportunities Fund	−0.00059	−0.00026	−0.00050	−0.00002	−0.00024	−0.00029	−0.00032
LIC Nomura MF Top 100 Fund	0.00038	0.00004	−0.00068	−0.00016	0.00007	0.00000	−0.00006
LIC Nomura Tax Plan	−0.00032	−0.00030	−0.00051	−0.00008	−0.00024	−0.00030	−0.00029
Mirae Asset India Opportunities Fund	NA	0.00040	0.00063	0.00022	0.00016	−0.00006	0.00027
Morgan Stanley A.C.E Fund	NA	−0.00007	0.00057	0.00008	−0.00006	0.00003	0.00011
PineBridge India Equity Fund	−0.00061	−0.00007	0.00066	−0.00031	0.00028	−0.00036	−0.00007
PineBridge Infrastructure and Economic Reform Fund	NA	−0.00096	0.00053	−0.00037	−0.00027	−0.00050	−0.00031
Principal Dividend Yield Fund	0.00014	−0.00050	0.00031	0.00023	0.00000	0.00007	0.00004
Principal Growth Fund	−0.00038	−0.00056	0.00006	−0.00021	−0.00006	0.00008	−0.00018
Principal Large-cap Fund	−0.00006	0.00001	0.00009	0.00007	−0.00012	−0.00015	−0.00003
Principal Resurgent India Equity Fund	−0.00045	−0.00041	−0.00008	−0.00018	0.00013	0.00013	−0.00014
Principal Services Industries Fund	−0.00039	−0.00019	0.00044	0.00009	−0.00030	−0.00073	−0.00018
Quantum Long-term Equity Fund	−0.00012	0.00009	0.00104	0.00046	0.00019	0.00021	0.00031
Reliance Banking Fund	−0.00046	−0.00002	0.00010	0.00047	−0.00020	0.00037	0.00004
Reliance Diversified Power Sector Fund	0.00121	−0.00037	0.00008	−0.00064	−0.00053	−0.00025	−0.00008
Reliance Equity Fund	−0.00022	−0.00007	−0.00021	−0.00045	−0.00005	0.00031	−0.00011
Reliance Equity Opportunities Fund	−0.00042	−0.00036	0.00093	0.00016	0.00010	0.00009	0.00008
Reliance Growth	−0.00013	−0.00041	0.00013	−0.00021	−0.00019	−0.00021	−0.00017
Reliance Long-term Equity Fund	0.00001	−0.00081	0.00068	0.00010	−0.00028	0.00019	−0.00002
Reliance Media & Entert Fund	−0.00079	−0.00145	0.00019	−0.00029	0.00038	0.00062	−0.00022
Reliance Natural Resources Fund	0.00956	−0.00036	0.00012	0.00009	0.00006	−0.00059	0.00148
Reliance NRI Equity Fund	−0.00066	0.00005	0.00022	0.00009	−0.00008	0.00001	−0.00006
Reliance Pharma Fund	−0.00088	0.00060	0.00185	−0.00003	0.00000	0.00009	0.00027
Reliance Quant Plus Fund	NA	0.00077	−0.00023	0.00044	−0.00001	−0.00021	0.00015
Reliance Tax Saver (ELSS) Fund	−0.00017	−0.00033	0.00035	0.00006	0.00017	−0.00007	0.00000
Reliance Top 200 Fund	−0.00027	0.00051	−0.00040	0.00023	0.00021	0.00007	0.00006
Reliance Vision	−0.00057	−0.00034	−0.00010	−0.00019	−0.00020	−0.00031	−0.00028
Religare Invesco AGILE Fund	0.00089	0.00028	−0.00067	0.00014	0.00001	−0.00024	0.00007
Religare Invesco Banking Fund	NA	0.00022	0.00038	0.00029	−0.00008	0.00037	0.00024
Religare Invesco Contra Fund	−0.00082	0.00026	0.00073	−0.00001	−0.00015	−0.00005	−0.00001
Religare Invesco Equity Fund	−0.00079	0.00029	0.00069	−0.00002	−0.00023	0.00009	0.00001
Religare Invesco Growth Fund	−0.00047	−0.00003	0.00023	0.00036	−0.00002	−0.00014	−0.00001
Religare Invesco Infrastructure Fund	−0.00163	−0.00054	0.00002	−0.00022	−0.00032	−0.00023	−0.00049
Religare Invesco Mid-cap Fund	−0.00059	−0.00171	0.00122	0.00012	0.00014	0.00019	−0.00010
Religare Invesco Mid-n-Small-cap Fund	NA	−0.00135	0.00149	0.00009	0.00023	0.00025	0.00014
Religare Invesco Tax Plan	0.00015	−0.00008	0.00046	0.00008	−0.00002	−0.00012	0.00008
Sahara Banking and Financial Services Fund	NA	0.00034	0.00002	0.00023	−0.00024	0.00016	0.00010
Sahara Growth Fund	−0.00021	0.00002	−0.00031	−0.00009	−0.00020	−0.00013	−0.00015
Sahara Infrastructure Fund–Fixed Pricing	0.00057	−0.00053	−0.00036	−0.00065	−0.00023	−0.00160	−0.00047
Sahara Infrastructure Fund– Variable Pricing	0.00059	−0.00051	−0.00034	−0.00063	−0.00020	−0.00161	−0.00045
Sahara Mid-cap Fund	−0.00042	−0.00085	0.00034	−0.00015	−0.00027	−0.00023	−0.00026
Sahara Power & Natural Resources Fund	NA	−0.00028	−0.00035	−0.00028	−0.00029	−0.00024	−0.00029
Sahara Taxgain	−0.00079	−0.00030	−0.00027	−0.00029	−0.00025	−0.00038	−0.00038
Sahara Wealth Plus Fund–Fixed Pricing	−0.00010	−0.00074	0.00027	0.00023	−0.00020	0.00011	−0.00007
Sahara Wealth Plus Fund–Variable Pricing	−0.00007	−0.00071	0.00030	0.00026	−0.00018	0.00014	−0.00004
SBI Contra Fund	−0.00014	−0.00029	−0.00010	−0.00024	−0.00025	−0.00024	−0.00021
SBI Emerg Buss Fund	−0.00040	−0.00113	0.00049	0.00002	0.00001	0.00015	−0.00014
SBI FMCG Fund	−0.00052	−0.00019	0.00092	0.00068	0.00083	0.00037	0.00035
SBI IT Fund	−0.00089	−0.00122	0.00117	0.00047	0.00004	−0.00069	−0.00019
SBI Magnum Bluechip Fund	−0.00025	−0.00023	−0.00012	−0.00013	−0.00007	0.00003	−0.00013
SBI Magnum COMMA Fund	0.00098	−0.00024	0.00011	−0.00025	−0.00057	−0.00043	−0.00007
SBI Magnum Equity Fund	−0.00030	−0.00030	−0.00003	−0.00016	−0.00006	−0.00029	−0.00019
SBI Magnum Global Fund 94	−0.00035	−0.00115	0.00060	−0.00017	−0.00009	−0.00015	−0.00022
SBI Magnum Mid-cap Fund	0.00015	−0.00149	−0.00014	−0.00031	−0.00010	0.00015	−0.00029
SBI Magnum Multi-cap Fund	−0.00038	−0.00032	0.00004	−0.00030	0.00003	−0.00017	−0.00018
SBI Magnum Multiplier Plus 93	−0.00015	−0.00046	0.00013	−0.00012	−0.00009	−0.00014	−0.00014
SBI Magnum Tax Gain Scheme 93	−0.00020	−0.00038	−0.00009	−0.00023	−0.00021	−0.00026	−0.00023
SBI One India Fund	−0.00073	−0.00056	0.00030	−0.00028	−0.00001	−0.00028	−0.00026
SBI Pharma Fund	−0.00165	−0.00042	0.00088	0.00015	0.00012	0.00014	−0.00013
Sundaram CAPEX Opportunities Fund	0.00025	−0.00146	−0.00007	−0.00060	−0.00057	−0.00048	−0.00049
Sundaram Energy Opportunities Fund	0.00088	−0.00059	−0.00065	−0.00055	0.00000	−0.00039	−0.00022
Sundaram Entertainment Opportunities Fund	NA	−0.00041	−0.00044	−0.00051	−0.00079	0.00072	−0.00029
Sundaram Equity Multiplier Fund	0.00070	−0.00074	−0.00012	−0.00026	−0.00016	−0.00001	−0.00010
Sundaram Financial Services Opportunities Fund	NA	−0.00054	−0.00027	0.00034	−0.00025	0.00022	−0.00010
Sundaram Growth Fund	0.00015	−0.00046	−0.00016	−0.00016	−0.00027	−0.00026	−0.00019
Sundaram India Leadership Fund	0.00013	−0.00041	−0.00018	−0.00005	−0.00023	−0.00031	−0.00017
Sundaram Rural India Fund	0.00030	−0.00102	−0.00005	0.00017	−0.00015	0.00005	−0.00012
Sundaram Select Focus	0.00018	−0.00039	−0.00034	−0.00019	−0.00034	−0.00030	−0.00023
Sundaram Select Midcap	−0.00019	−0.00066	0.00006	−0.00017	−0.00029	−0.00018	−0.00024
Sundaram SMILE Fund	0.00052	−0.00047	0.00024	−0.00034	−0.00024	−0.00021	−0.00008
Sundaram Taxsaver	−0.00005	−0.00039	−0.00013	−0.00020	−0.00027	−0.00023	−0.00021
Tata Contra Fund	0.00042	−0.00041	0.00027	0.00011	0.00012	−0.00037	0.00003
Tata Dividend Yield Fund	0.00052	−0.00054	0.00048	0.00019	−0.00004	−0.00033	0.00005
Tata Equity Opportunities Fund	0.00003	−0.00052	0.00004	−0.00027	−0.00012	−0.00035	−0.00020
Tata Equity P/E Fund	0.00028	−0.00034	0.00062	0.00003	0.00006	−0.00037	0.00005
Tata Ethical Fund	−0.00026	−0.00087	0.00045	−0.00016	−0.00012	−0.00042	−0.00023
Tata Infrastructure Fund	0.00034	−0.00037	−0.00038	−0.00036	−0.00034	−0.00040	−0.00025
Tata Life Sciences and Technology Fund	−0.00053	−0.00023	0.00080	−0.00007	−0.00017	−0.00056	−0.00012
Tata Mid-cap Growth Fund	−0.00013	−0.00073	0.00004	−0.00021	−0.00013	−0.00032	−0.00025
Tata Midcap Fund	0.00044	−0.00126	0.00043	−0.00035	−0.00020	−0.00012	−0.00018
Tata Pure Equity Fund	−0.00025	−0.00019	−0.00006	−0.00010	−0.00008	−0.00023	−0.00015
Tata Service Industries Fund	−0.00060	−0.00055	0.00023	−0.00040	−0.00003	−0.00025	−0.00027
Taurus Bonanza Fund	NA	−0.00083	−0.00084	0.00021	−0.00021	0.00001	−0.00033
Taurus Infrastructure Fund	−0.00039	−0.00063	−0.00017	−0.00028	−0.00018	0.00004	−0.00027
Taurus Taxshield	NA	−0.00043	−0.00054	0.00009	−0.00019	−0.00008	−0.00023
Templeton India Equity Income Fund	0.00022	0.00017	0.00075	0.00011	−0.00016	0.00000	0.00018
Templeton India Growth Fund	0.00011	−0.00021	0.00052	−0.00017	−0.00016	−0.00008	0.00000
UTI Banking Sector Fund	−0.00086	0.00018	−0.00006	0.00052	−0.00019	0.00020	−0.00004
UTI Contra Fund	−0.00021	0.00039	0.00021	−0.00043	−0.00011	0.00008	−0.00001
UTI Dividend Yield Fund	0.00016	−0.00001	0.00029	0.00014	−0.00015	−0.00019	0.00004
UTI Energy Fund	−0.00117	−0.00039	−0.00020	−0.00031	−0.00035	−0.00032	−0.00045
UTI Equity Fund	−0.00011	−0.00028	0.00027	0.00017	0.00005	−0.00006	0.00001
UTI Equity Tax Savings Plan	−0.00022	−0.00039	−0.00010	−0.00017	−0.00027	−0.00023	−0.00023
UTI Index Select Fund	−0.00013	−0.00013	0.00079	0.00002	0.00002	0.00002	0.00010
UTI India Lifestyle Fund	−0.00132	−0.00041	−0.00003	0.00025	0.00011	0.00009	−0.00022
UTI Infrastructure Fund	−0.00029	−0.00055	−0.00038	−0.00048	−0.00040	−0.00017	−0.00038
UTI Leadership Equity Fund	−0.00055	0.00000	−0.00030	−0.00002	−0.00006	0.00002	−0.00015
UTI Master Value Fund	0.00003	−0.00052	0.00033	−0.00013	−0.00026	−0.00021	−0.00013
UTI Masterplus Unit Scheme 91	−0.00060	−0.00023	−0.00031	0.00008	−0.00006	−0.00009	−0.00020
UTI Mastershare	−0.00035	−0.00017	−0.00015	−0.00011	−0.00033	−0.00037	−0.00025
UTI Mid-cap Fund	−0.00037	−0.00088	0.00067	−0.00013	−0.00015	−0.00003	−0.00015
UTI MNC Fund	−0.00049	−0.00029	0.00058	0.00013	0.00026	−0.00035	−0.00003
UTI Opportunities Fund	0.00001	−0.00006	0.00000	0.00005	0.00012	−0.00024	−0.00002
UTI Pharma and Healthcare Fund	−0.00031	0.00033	0.00123	0.00011	0.00004	−0.00021	0.00020
UTI Services Industries Fund	−0.00070	−0.00032	0.00016	−0.00012	−0.00019	−0.00013	−0.00022
UTI Top 100 Fund	−0.00030	−0.00025	0.00161	−0.00015	−0.00012	−0.00025	0.00009
UTI Transportation and Logistics Fund	−0.00094	−0.00040	0.00100	−0.00002	0.00019	−0.00032	−0.00008
UTI Variable Investment Scheme	−0.00049	−0.00006	−0.00004	−0.00002	−0.00021	−0.00021	−0.00017

Source: Authors’ calculations.

Notes: The table shows the annual alpha (intercepts) values and the average alpha values for sample mutual fund schemes. Conditional alpha is a measure of stock selection ability or risk adjusted performance of mutual fund portfolio estimated using conditional Carhart (1997) four factor model employing equation (5).

Panel Data Regression

Table 1 reports results of panel data regression using fixed effects estimator employing equation (6) for the sample period spanning from April 2007 to March 2013.

F-statistics of fixed effects model is 19.01, implying goodness of fit of the model. Thus, fund characteristics pertaining to a period jointly affect one-period-ahead risk-adjusted performance.

Analysis of individual fund characteristics is as follows:

Size of fund: AUM is statistically negatively significant at 5 per cent level (coefficient = −0.00017 and t statistic = −4.98). A negative relationship suggests that smaller funds tend to result in higher future performance in case of the Indian mutual fund industry subject to open-ended Indian equity-based schemes with growth as their objective. Since, small funds are managing a small corpus of funds, they may be in a better position to employ these funds in meaningful investment avenues. Moreover, the negative impact of fund size on fund performance is an evidence of diseconomies of scale on part of the large-sized funds in India. These diseconomies of scale could be attributed to organizational design, such as a fund with long hierarchy may not be viable in the industry or inability of large funds to trade without sending signals to other investors. Our results are consistent with the findings of Grinblatt and Titman (1989) who find performance of US mutual funds to be inversely related to size on using gross returns. According to them, small funds may be in a beneficial position because their size lets them trade securities with less trouble without altering prices of securities. Also, Dahlquist et al. (2000), in case of Swedish mutual funds, report negative relationship. Small is beautiful as per Beckers and Vaughan (2001) in context of portfolio of 250 Australian stocks. According to them, the large size of funds brings diminishing returns to scale and makes the trading by portfolio managers inflexible. Chen, Hong, Huang and Kubik (2004) use a predictive model of relationship between fund characteristics and fund performance and report negative significant relationship. Yan (2008) also reports a negative relationship between fund size and performance. Yap and Pierce (2008) examining Australian funds state that diseconomies of scale result in drop in performance of large funds. A similar argument is provided by Ferreira et al. (2012) in case of US funds, stating that small funds deal with a small number of investment opportunities which allow them to concentrate better, while large funds have to employ their energy and skills on many investment opportunities as a result their concentration weakens implying diseconomies of scale. Low (2012) finds a negative relation between size and performance for Malaysian mutual funds, suggesting that larger funds, due to their increasing size, face the challenge to locate meaningful and sensible investments which ultimately hampers the selectivity ability of managers. The results from Lobao and Gomes (2015) and Pastor et al. (2015) also suggest that bigger funds have performed less well than smaller funds. In respect to the Indian mutual fund industry, our results also ascribe to diseconomies of scale indicating that small funds tend to outperform large-sized funds and are in consistency with Sondhi and Jain (2010).

Growth in size of fund: GRAUM is statistically negatively significant (coefficient = −0.00008 and t statistic = −2.00). It implies that if the rate of change in AUM is high, it may pose problems for mutual fund managers to capably handle and allocate these increasing flows, thus, resulting in lower future performance for Indian mutual funds. As per Ciccotello and Grant (1996), management problems emerge with increasing size of funds. Fund managers and teams have to strive hard to locate meaningful investment avenues. Growing funds become so large in size that their trading moves have a noticeable impact on the market. Their stepping in and out of the market become visible and sends signals to other investors. Large-sized funds become so huge that they themselves act as a surrogate for the market, and it becomes complicated to beat the market index. Mutual funds may become enormous in size, but the optimal performance takes place in smaller funds. A similar assertion is presented by Low (2012), stating that funds with high growth rates may face certain hassles that make them ineffective in operating and managing funds. Low (2010) finds statistically significant negative coefficient of growth in fund size. Similar results are observed in the present study for Indian data where excessive growth in size of fund may serve as a deterrent for fund to perform well in future.

Expense ratio: There is a positive but statistically insignificant relationship between expense ratio and fund performance. It appears that the fund expenses tend to have no relation with its performance. Our results are in contrast with some studies, such as Droms and Walker (1996), Bauer et al. (2006), Prather et al. (2004) and Vijayakumar et al. (2012), that report a positive significant relationship suggesting that the fund houses undertake operating or administrative expenses with the assumption that these charges would adequately support research, managerial expertise and advertising of funds and, therefore, a positive influence is expected on fund performance. However, Ippolito (1989) studies the performance of US mutual funds and finds an insignificant relationship between expense ratios and fund performance. Since the risk-adjusted return is measured net of expenses, according to him, coefficients on the expense ratio, fees are expected to be insignificant as it indicates the efficient utilization of money by mutual funds. Also, funds with higher expenses and fees, apparently, earn risk-adjusted returns sufficient to even out the higher charges. Furthermore, Murcia (2011) reports an insignificant relationship between management fees and yield on funds, stating that the higher sum of fees (management and custody fees) charged by the fund houses do result in higher gross yields, but both increase with similar magnitude, thus, cancelling out the effect of any change in performance. Other studies that find statistically insignificant coefficient for expense ratio are Christensen (2003), Fortin and Michelson (2005), Karlsson and Persson (2005) and Low (2010). The insignificant relationship reported in the present case is aptly observing the notion presented by Ippolito (1989). Since the returns are computed net of expenses, the insignificant relationship suggests that an increase in expenses is matched with an equal amount of increase in performance of the fund. In other words, it indicates that the superior performance by the Indian mutual funds is just sufficient to cover up the additional charges and expenses levied by them. Also, this insignificant effect of expense ratio on performance of fund may be attributed to the constraint set by SEBI on mutual funds in India to charge their annual recurring expenses subject to a ceiling rate of 2.5 per cent to an investor.

Portfolio turnover: PTR is found to be positive but statistically insignificant, implying that actively managed funds are not resulting into significant superior risk-adjusted performance in case of mutual funds in India, particularly open-ended Indian equity-based schemes with growth as their objective. Also, the returns, in the present case, are estimated net of all expenses and transactions costs, the positive insignificant relationship so observed suggests that the higher returns generated by mutual funds may be as much as necessary to offset the higher transaction or brokerage costs charged by them. This finding is again consistent with Ippolito (1989) who finds an insignificant relationship between turnover and fund performance, stating that the fund houses actively trading their portfolio generate enough incremental returns sufficient to offset higher transactions costs. Alternatively, positive effect of PTR may provide that the Indian mutual funds tend to create value by actively trading their portfolio; however, the effect is statistically insignificant, perhaps due to the constraint on expense ratio set by SEBI in India. Further, it is observed that both expense ratio and PTR exhibit positive but statistically insignificant relationships with performance of fund indicating that the cap on expense ratio possibly has some bearing on the trading movements of the mutual funds. Our results are similar to Droms and Walker (1996), Prather et al. (2004), Karlsson and Persson (2005), Yan (2008) and Low (2010) who report statistically insignificant relationship between PTR and risk-adjusted performance.

Net asset value: NAV is found to be statistically negatively significant at 5 per cent level (coefficient = −0.00002 and t statistic = −5.50), suggesting that lower NAV tends to generate superior risk-adjusted performance for Indian equity mutual funds. NAV represents price per unit of mutual fund calculated at the end of each trading day. Investors seem to prefer lower NAVs, possibly because they assume market is undervalued and they tend to buy mutual funds units below their intrinsic value. In other words, a fund with a high NAV may be viewed as more expensive by the investors in relation to a fund that has a low NAV. Findings of the present study are in conformity with the argument provided by Belgacem and Hellara (2011) stating that it is more likely to attract investors if lower NAV prevails, and this may result in greater market share for the mutual fund.

Age of fund: FUNDAGE is a statistically positively significant (coefficient = 0.00007 and t statistic = 6.51). Positive value suggests that as funds grow with age, their risk-adjusted performance becomes superior, rewarding them with a premium for their experience. Funds with a long history are expected to result in higher performance, possibly because of the recognition and brand name they enjoy in the market. Moreover, old funds are more experienced to have survived a full market cycle, thus, coping with bearish and bullish phases. So, funds that have tasted the flavour of both upturns and downturns in the market and manage to survive seem to attract investors and perform better. In the Indian market, matured funds tend to be well-informed, resourceful and proficient in positioning a portfolio that may fetch abnormal returns for them. Ferreira et al. (2012) state that newer funds have to face higher start-up costs and challenges which may put them at the disadvantage in relation to older funds. Similar positive relationship for UK mutual funds is found, indicating existence of economies of experience by Blake and Timmerman (1998). Babalos et al. (2009) and Belgacem and Hellara (2011) also report a positive coefficient of fund age. In a recent study, Lobao and Gomes (2015) find a positive relationship between age and performance of Portuguese mutual funds suggesting that younger funds tend to underperform funds that have been in business for a longer time.

The statistically significant constant suggests that there may be some unobserved factors which are not captured through the present regression model. These unobserved factors may be managerial attributes such as manager’s experience, education, age, etc. Such factors could not be included in the model because of non-availability of data.

Broadly, AUM, GRAUM, NAV and FUNDAGE are found to be statistically significant characteristics in predicting the performance of mutual funds in India, whereas EXP and PTR share insignificant relationship with performance of fund.

Thus, results indicate that funds with small size, low growth in AUM, low NAVs and old by age provide higher one-period-ahead risk-adjusted performance. Expense and portfolio turnover ratios of fund are not found to have any marked effect on fund performance.

Summary and Concluding Observations

The study examines a range of fund characteristics, namely, the size of fund, growth in size of fund, expense ratio, portfolio turnover, NAV of fund and age of fund that may determine future performance of mutual funds in India. Using a sample of 237 open-ended Indian equity (growth) schemes from April 2007 to March 2013, a two-stage analysis is performed to capture this relationship. Firstly, alpha, risk-adjusted performance of funds, is generated using the conditional version of Carhart (1997) four factor model in a time series regression framework. Annual risk-adjusted performance for 237 schemes become the input for the second stage analysis, where panel data regression is estimated using fixed effects estimator.

The results suggest that size of fund, growth in size of fund and NAV negatively affect performance of fund, while age of fund has a positive impact. Besides, the portfolio turnover and expenses incurred by fund houses in respect of operations, management, brokerage or commission do not show any significant impact on performance of the fund. The findings of this study are consistent with Ippolito (1989), Dahlquist et al. (2000), Yap and Pierce (2008), Low (2010, 2012), Ferreira et al. (2012) and Lobao and Gomes (2015).

These findings have implications for mutual fund investors, fund managers and the academic community. While selecting mutual fund schemes, investors require being conversant with investment actions of fund managers; however, such actions are not directly observable by them. In such a scenario, findings exhibiting the relationship between fund characteristics and performance offer valuable insights to investors in making judicious investment decisions. The study suggests that it may be better for investors to route their investments in small-sized funds, low growth in AUM funds, low NAV funds and older funds having past track record. The funds with these attributes have the potential of generating high risk-adjusted returns. Further, expenses charged and active turnover of portfolio by mutual funds might be overlooked as these characteristics exhibit no significant influence on future performance of fund.

From a fund manager’s perspective, the study highlights those fund characteristics that may have bearing on performance of funds which they manage.

These findings are definitely advantageous to academicians and researchers as the literature on relationship between fund characteristics and fund performance is limited with respect to the Indian mutual fund industry. In an Indian context, this study has been the first attempt to apply predictive relationship model in a panel data framework. The present study fills the important research gap and contributes to the mutual funds literature by identifying significant fund characteristics determining investment performance.

Footnotes

Notes

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