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On Stock Selection Skills and Market Timing Abilities of ...

  1. 1. International Research Journal of Finance and Economics ISSN 1450-2887 Issue 15 (2008) © EuroJournals Publishing, Inc. 2008 http://www.eurojournals.com/finance.htm On Stock Selection Skills and Market Timing Abilities of Mutual Fund Managers in India Sanjay Sehgal Professor of Finance, Department of Financial Studies University of Delhi, South Campus, ESC-Pau,France E-mail: sanjayfin15@yahoo.co.in Tel. 0091-11-27130579 Manoj Jhanwar Research Student, IIT Delhi E-mail: manoj_jhanwar@rediffmail.com Abstract In this paper, we evaluate the performance of selected equity-based mutual funds in India. We argue that multi-factor benchmarks provide better selectivity and timing measures compared to one-factor CAPM as they control for style characteristics such as size, value and momentum. The results timing ability, and to some extent stock selectivity improve when we use daily instead of monthly data. We feel that higher observation frequency captures the trading skills of more active fund managers in a better fashion. We show that timing should be examined in a multi-dimensional framework with additional measures for timing of style characteristics. Further our timing results are not an outcome of any spurious statistical phenomenon. Keywords: Market timing, Selectivity skills, Alpha, Managed portfolios, Net asset value. JEL Classification Codes: C12, G11. 1. Introduction Mutual fund is one of the most preferred investment route by small investor, who allocate part of their funds to capital market. In India, the mutual funds industry has been in existence for more than four decades. Unit Trust of India (UTI) was the first mutual fund to be set up in India under the UTI Act 1963. In 1987, the Government allowed mutual funds to be promoted by public sector banks and other financial institutions and in 1993 the doors were opened for private sector mutual funds. As on June 3, 2006, Association of Mutual Funds of India (AMFI) reported that there are 29 mutual funds in India offering 592 schemes with an investment in assets of about 60 billion US dollars. In view of large investor interest, the performance of mutual fund managers needs continuously evaluated. The performance studies deal mainly with two aspects (1) evaluating stock selection skills and (2) examining the market timing abilities of the fund managers. Studies of stock selection date back to Jensen (1969) who finds that managers deliver negative abnormal returns. Using more recent data Ippolito (1989) finds evidence of positive abnormal returns. However, Elton et al (1992) show that the benchmark chosen by Ippolito causes this result. Using, multi-factor model, they find that abnormal fund returns are on average negative.
  2. 2. International Research Journal of Finance and Economics - Issue 15 (2008) 308 A series of empirical studies also deal with the market timing skills of mutual fund managers. Most of the previous work finds little evidence that fund managers possess market timing ability. Treynor and Mazuy (1966), hereafter referred to as TM develop test of market timing and find significant timing ability in only one out of 67 funds in their sample. Henriksson (1984) uses the test of Henriksson and Merton (1981), hereafter referred to as HM, finds that only 3 out of 116 funds exhibit significant positive timing ability. However, Bollen and Busse (2001) point out that statistical tests used in previous studies are weak as they are based on monthly data. Using daily data, they find evidence of market timing ability in a significant number of funds in their sample. Chance and Hemler (2001) use daily data to track the allocation strategies of 30 professional fund market timers. They also find significant number of market timers. Ferson and Schadt (1996) state that standard measures of performance designated to detect security selectivity and market timing ability suffer from a number of biases. Most previous work employs traditional performance measures that use unconditional expected returns as a baseline. However, if expected returns and risks vary over time such an unconditional approach is not desirable. Common time variations in returns and risk premia will be confused with average performance. Previous studies show that returns and risk on stocks are predictable over time using dividend yields, earnings yields, company size and other variables. Carhart (1997) develops a four-factor model, comprising of the market factor of Sharpe's (1964) CAPM, the size and book to market factors provided by Fama and French (1993) and a momentum factor to account for continuation patterns in stock returns diagnosed by Jegadeesh and Titman (1993). A growing body of empirical literature shows that the four-factor model captures the major anomalies of Sharpe's one-factor CAPM. The three additional risk factors may also capture a part of time-varying nature of returns and risk premia as suggested by Ferson and Schadt. Despite the use of high-frequency data as well as a more comprehensive benchmark, the performance results, especially those of market timing, may still suffer from Jagannathan and Korajczyk (1986) bias provided the fund returns are more option like compared to the market returns. The correction mechanism involves comparing the timing measures of actual funds with those of synthetic funds where the latter should have no explicit timing ability by construction [See Bollen and Busse (2001)]. In this paper, we demonstrate how the performance results related to both selectivity and market timing skills get modified when we use multi-factor benchmark instead of standard one-factor benchmark. We also show the impact of observation frequency on the size and significance of performance measures. We deal with two issues regarding market timing measures. We attempt to measure market timing abilities of fund managers not only with regard to the market factor, as in previous work, but also for the three additional Carhart factors. We also try to rectify Jagannathan and Korajczyk (1986) bias by constructing synthetic funds using an estimation procedure different from Bollen and Busse (2001). 2. The Models of Mutual Fund Performance Prior studies of mutual funds performance focus on stock selection or market timing ability. In this section, we describe the models for evaluating both the managerial skills along with innovations applied in recent literature. 2.1. Stock Selection A traditional approach to measure selectivity is to regress the excess returns of a portfolio on the market factor. Assuming that market beta (or slope coefficient) is constant, then the unconditional alpha (or intercept) is a measure of average performance, as in Jensen (1968), i.e., RPt - RFt = + RMt - RFt + et (1) where
  3. 3. 309 International Research Journal of Finance and Economics - Issue 15 (2008) RPt - RFt and RMt - RFt are excess portfolio and market returns. and are the intercept and slope coefficients. et is an error term. We can also measure selectivity using the Carhart (1997) four-factor model 4 RPt - RFt = + K FKt + et (2) K=1 where RPt - RFt is the excess portfolio return FK = returns on the factors including the excess market return, the Fama-French (1993) size and book to market factors and Carhart's momentum factor. K = sensitivity coefficient = intercept term Previous research shows that the last three factor capture most of the anomalies of Sharpe's (1964) single-factor CAPM. We include these additional factors to avoid rewarding managers for simply exploiting these anomalies. 2.2. Market Timing Market timing refers to the dynamic allocation of capital between broad asset classes. Treynor and Mazuy (1966) use the following regression to test for market timing: RPt - RFt = + Rmt - RFt + (RMt - RFt)2 + et (3) Where Rpt - RFt is the excess return on a portfolio at time t, RMt - RFt is the excess return on the market, and is a measure of timing ability. If a mutual fund manager increases (decreases) the portfolio's market exposure prior to a market increase (decrease) then the portfolio's return will be a convex function of the market's return, and will be positive. Henriksson and Merton (1981) develop a different test of market timing. In their model, the mutual fund manager allocates capital between risk free assets and equities based on forecasts of the future excess market return, as we test a model with two target betas via the following regression: RPt - RFt = + Rmt - RFt + rDt + et (4) Where r = 1 when RMt - RFt > 0, equal to 0 otherwise is used as a market timing measure in HM framework. We use both timing models to measure timing ability in our sample of mutual funds. Grinblatt and Titman (1994) show that the tests of market performance are quite sensitive to choice of benchmark. For this reason, we use the four-factor versions of equation (3) and (4) based on Carhart (1997) model and those that control for prominent CAPM anomalies as stated in the previous sub-section. The modified market timing models are used in Bollen and Busse [(2001), (2005)] and look like 4 RPt - RFt = + k FKt + rF21t + et (5) K=1 For the TM model where F21t is the square value of factor 1, i.e., the excess market return while additional factors in the summation represent size, value and momentum factors; and 4 RPt - RFt = + k FKt + Dt1 + et (6) K=1 For the HM model, where Dt = 1 for F1 (RM - RF) to be positive and Dt = 0 for F1 (RM - RF) to be zero or negative. However, timing measures in (5) and (6) do not provide a complete picture. In these versions, market timing simply means increasing (decreasing) the sensitivity (or slope coefficient) of fund returns to market returns using market upturns (downturns). The presence of additional factors that control for investment style characteristics may warrant a multi-dimensional market timing ability. For
  4. 4. International Research Journal of Finance and Economics - Issue 15 (2008) 310 instance, besides timing the market factor, the fund managers may be timing the size, value as well as the momentum factors. Thus, market timing also implies increasing (decreasing) the sensitivity of fund returns to size, value and momentum factors during the periods when small firms are expected to outperform (underperform) big firms, high book to market equity or BE/ME firms are expected to outperform (underperform) low BE/ME firms and stocks with high short-term past returns (past winners) are expected to outperform (underperform) stocks with low short-term past returns (past losers). This requires measuring of additional timing coefficients in the multi-factor regressions. This will lead to the following versions of the abovesaid models: 4 4 RPt - RFt = + k FKt + rKt F2Kt + et (7) K=1 K=1 for TM model where rs represent timing measures for each factor, and 4 4 RPt - RFt = + k FKt + K Dkt + et (8) K=1 K=1 where DKs are dummy variables for the slope coefficient of each factor, such that 1 = 1 if RM - RF is > 0 = 0 otherwise 2 = 1 if SMB > 0 = 0 otherwise 3 = 1 if HML > 0 = 0 otherwise 4 = 1 if WML > 0 = 0 otherwise The RM - RF, SMB, HML and WML depict market size, value and momentum factors respectively whose construction is explained in a later section. We believe that our model versions given in (7) and (8) shall provide a more comprehensive inference about market timing abilities of fund managers. 3. Data The data comprises of dividend-adjusted Net Asset Values (NAVs) for 59 natural fund schemes from January 2000 to December 2004. 57 of the sample schemes have a re-investment option and hence we use their re-investment based NAVs. All the sample schemes are open-ended in nature and are predominantly equity-based with growth and growth-income as their objectives. The NAV values are used to estimate percentage daily and monthly returns for the sample schemes. We also collect information about entry load and management expenses for the sample funds. The data source is ICRA Mutual Funds Software. The data set is limited due to non-availability of regular NAV and expenses information both cross-sectionally as well as over long periods of time. We also use individual securities data for the construction of size, value and momentum factors in returns both on daily and monthly basis. The data includes daily share prices for 452 companies that form part of BSE-500 index from January 1999 to December 2003. The sample securities account for more than 90% of market capitalisation and market trading activity. Hence, the sample set is fairly representative of market performance. The share prices are adjusted for capitalisation changes, such as bonus, rights and stock splits and are used to compute percentage returns on the sample securities. The share prices are obtained from Smart Investor, a technical software.
  5. 5. 311 International Research Journal of Finance and Economics - Issue 15 (2008) The Bombay Stock Exchange (BSE)-500 index is used as the surrogate for aggregate economic wealth. The BSE-500 series is available from January 2000 onwards. We therefore splice this series with the BSE-100 series for the year 1999 as the correlation between the two indices is 0.93 for the 2000-2004 period. BSE-500 is a broad-based and value weighted market proxy constructed on lines of Standard & Poor, USA. The data source is BSE website. The 91-day treasury-bills are used as a risk- free proxy and are compiled from the Reserve Bank of India (RBI) website. We also collect information for company characteristics such as market capitalisation (price times number of shares outstanding) and book equity to market equity (BE/ME) ratios for the sample companies. The annual figures for market capitalisation and BE/ME are obtained for December-end and March-end respectively from CMIE Provess, a financial software. 4. Estimation Procedure We estimate one measure of stock selectivity, i.e., the Jensen's model and two measures of market timing, i.e., TM and HM models. We deal with two important issues in the estimation process: (1) model selection and rectification of inherent biases and (2) observation frequencies and the related correction procedure. We estimate our performance measures of selectivity and market timing abilities using both one-factor as well as multi-factor benchmarks which are described in Section 2. The latter shall provide a selectivity measure (or alpha) that is net of compensations to investment style characteristics. It shall also provide a matrix of market timing measures for TM and HM models, where in addition to the market timing coefficient, we obtain timing measurers for characteristic-based portfolios. A comparison of results between one-factor (traditional) and four-factor (modified) versions of performance models shall bring to light how the style characteristics affect fund performance. The four-factor model needs construction of three additional factors, besides the excess market return factor provided by standard CAPM. Two of the additional factors, i.e., size and value factors are computed based on Fama-French (1993) methodology. Using the market capitalisation at the end of calendar year t-1, we sort the 452 sample stocks that form part of the BSE-500 index into two groups using median break point: small or S (bottom 50%) and big or B (top 50%). We re-rank the stocks on the basis of book equity to market equity (BE/ME) ratio observed in March t-1 and form three groups: low (bottom 33.3%), medium (between 33.3% - 66.6%) and high (above 66.6%). The BE/ME information is available only in March each-year as India has a April-March financial year. Combining the two size and three BE/ME groups, we generate six portfolios, i.e., S/L, S/M, S/H, B/L, B/M and B/H. While S/L represents small cap and low BE/ME stocks, B/H comprises of big cap high BE/ME firms. The equally-weighted monthly (daily) returns are calculated on each of the six portfolios for the year t. The portfolios are rebalanced at the end of t based on fresh information on company size and BE/ME for the sample stocks. We use the six sample portfolios to construct the size (SMB) factor. SMB (small minus big) is measured as a difference between the average monthly (daily) returns on the small stock portfolios and the average returns on the big stock portfolios. The construction procedure ensures that the size factor is neutral of value effect. Similarly, we construct value (HML) factor in returns. The HML (High minus Low) factor is the monthly (daily) difference in the average returns on high BE/ME portfolios and the average returns on low BE/ME portfolios. The value effect is free of size effect by construction. We also construct the Carhart (1997) momentum factor. We rank the sample stocks based on their average monthly (daily) returns for one-calendar year prior to portfolio formation, i.e., year t - 1 and form five groups. The bottom 20% based on past returns are referred to as losers portfolio, while top 20% past performers form the winners portfolio. The momentum factor is defined as the difference between the returns on equally-weighted winners and losers portfolios or WML (Winners minus Losers). The portfolios are re-balanced at the end of t on one-year prior return criterion. The momentum (WML) factor has been estimated on monthly as well as daily basis.
  6. 6. International Research Journal of Finance and Economics - Issue 15 (2008) 312 We identify Jagannathan and Korajczyk (1986) bias in our mutual funds data that may distort the results of our market timing measures. We adopt the bias correction procedure suggested by Bollen and Busse (2001). We specifically construct synthetic funds. Synthetic funds are boggy portfolios that mimic the style characteristics of actual mutual funds but are expected to possess no market timing ability by construction. Since we don't have daily portfolio compositions, we follow a construction methodology different from Bollen and Busse. We regress the excess returns on a sample fund on the six size-BE/ME portfolios and the momentum (WML) portfolio. The regression is estimated through the origin and is constrained to have non-negative betas (slope) which add to one. In this way, we can interpret these betas as weights or relative exposures to the style characteristics. We then estimate weighted characteristics return on period to period basis, i.e., k Rkt where k is the characteristic weights and Rkt is the characteristic return in period t. We refer to this weighted characteristic portfolio as synthetic fund, as its mimics the characteristic properties of the actual mutual fund but without any active market timing ability. We construct a synthetic fund as a shadow for every sample fund. We estimate the timing coefficients for the synthetic funds using one-factor and four-factor versions of TM and HM models. The acid test is to demonstrate that the timing coefficients for the sample funds significantly exceed those of synthetic funds. In most of the studies conducted so far, observations of mutual fund returns are recorded monthly or annually. As discussed by Goetzmann, Ingersoll, and Ivkovic (2000), hereafter referred to as (GII), a monthly frequency might fail to capture the contribution of a manager's timing activities to fund returns, because decisions regarding market exposure are made more frequently than monthly for most of the funds. We have daily observations of mutual fund returns. This allows us to directly overcome the problem investigated by Goetzmann et al. To determine whether observation frequency matters, we use both daily and monthly data to test the selectivity and timing skills of fund managers. We shall use in the next section that the tests using daily data are more powerful than those based on monthly data. Scholes and Williams (1977) point out that when estimating the parameters of a factor model of daily stock returns, infrequent trading can result in biased estimates of variance, serial correlation, and contemporaneous correlation between assets. This holds for portfolios of infrequently traded assets as well because variance of a portfolio is largely determined by the average covariance of the individual assets in the portfolio. While using daily data, we employ Dimson's (1979) correction and include lagged values of the factors as additional independent variables in the regression to accommodate infrequent trading. 5. Empirical Results We discuss the results on performance evaluation in two parts: (1) for stock selection skills and (2) for timing abilities. 5.1. Tests of Stock Selection Table 1 provides the results for one-factor and four-factor Jensen's selectivity measure, using both monthly and daily data. We estimate mean alpha values for the sample funds and provide the number of significantly positive alpha values at 5% level (on one-tail basis). 25% of the sample schemes (15 out of 60) exhibit statistically significant selectivity skills on the basis of one-factor benchamrk, when we use monthly data. The mean alpha decline marginally from .0040 to .0036 on monthly basis, once one accounts for investment style characteristics by shifting from one-factor to four-factor model. The number of significantly positive alpha values increase from 15 to 17 based on four factor benchmark as one employs daily instead of monthly data. Further, the mean alpha improves from 4.32% to 7.5% on annualised basis when we use daily instead of monthly return data. The mean alpha values have been annualised on the assumption of 250 trading days in a year. Thus, we observe that after controlling for style characteristics (by employing four-factor benchmark), the evidence on stock selectivity improves,
  7. 7. 313 International Research Journal of Finance and Economics - Issue 15 (2008) though not substantially, with higher observation frequency. This probably reflects that weak results on stock selectivity reported by most previous studies, may partially be an outcome of large data interval say use of monthly or annual data. Table 1: Tests of selectivity based on Jensen Model Panel-A- Monthly Data One factor Four factor Mean +ve significant values Mean +ve significant values 0.0040712 15 0.0036 15 +ve- positive alpha values Panel-B- Daily Data One factor Four factor Mean +ve values Mean +ve significant values 0. 0.00036 23 0.0003 17 +ve- positive alpha values 5.2. Tests of Timing Abilities Next, we explore the timing abilities of Indian mutual fund managers. Table 2 contains the results for one-factor and four-factor versions of the TM model. The market timing coefficient (r1) changes from a negative value to a strong positive value when we shift from monthly to daily return file. Only 1 out of 60 sample funds provides that significantly positive gamma (r), on monthly basis. Using daily data we find that superior market timing skills are exhibited by 27 fund managers (45% of the sample). Thus fund managers seem to be executing more active market timing strategies which are better captured by use of daily data. The r2, r3 and r4 reflect the abilities of the fund managers to time additional factors and hence are measures of timing in a multi-dimensional evaluation framework. The fund managers exhibit negligible skills in timing the size and value factors. However, 12.5% of the fund managers seem to be effectively timing the momentum factor. Table 2: Tests of market timing based on Treynor - Mazuy Model Panel-A- Monthly Data One factor Four factor Mean +ve 1 +ve 1 2 +ve 2 3 +ve 3 4 +ve 4 -0.32 1 -0.16 1 -0.22 3 -0.184 0 -0.83 5 +ve- positive gama values Panel-B- Daily data One factor Four factor Mean +ve 1 +ve 1 2 +ve 2 3 +ve 3 4 +ve 4 0.253 28 0.377 27 2.86 1 0.73 1 0.393 5 +ve- positive gama values We now analyze the results for the HM model, an alternative timing tool, by focussing on Table 3. The results of market timing again improve as we shift from monthly to daily data as shown by 1 (the market timing coefficient) and the fact that the significant market timers increase from 7 to 17 on four-factor basis. The fund managers do not seem to exhibit any distinguishable ability in timing of style characteristics, such as size and value. Further, on four-factor basis only 10% and 3.3% of fund managers seem to be timing the momentum characteristics when we use monthly and daily data respectively.
  8. 8. International Research Journal of Finance and Economics - Issue 15 (2008) 314 Table 3: Tests of Market Timing based on Hendrikson-Merton Model Panel-A- Monthly Data One factor Four factor Mean +ve 1 +ve 1 2 +ve 2 3 +ve 3 4 +ve 4 0.0083 7 0.0294 1 0.352 2 -0.0075 0 -0.70 5 +ve- positive delta values Panel-B- Daily Data One factor Four factor Mean +ve 1 +ve 1 2 +ve 2 3 +ve 3 4 +ve 4 0.00088 15 0.00083 17 -0.00023 1 0.000112 0 -0.00026 2 +ve- positive delta values Summing up, the results on market timing ability improve substantially as we use a higher observation frequency as suggested by Bollen and Busse (2001). Hence, negative evidence on market timing reported in the literature can generally be attributed to use of higher observation frequency (monthly and larger intervals). High frequency data, such as daily observations, does capture market timing skills in a better way. In addition to timing the market factor, a few fund managers also seem to be timing the momentum characteristics. Therefore, timing skills should be evaluated in a multi- dimensional framework. This requires inclusion of additional timing coefficients for style characteristics in our factor regressions. 5.3. The Jagannathan and Korajczyk Bias and its correction We next examine if our timing results are caused by any spurious statistical phenomenon. A possible source of spurious timing is provided by Jagannathan and Korajczyk (1986), who argue that spurious timing ability can be generated when portfolios hold stocks with payoffs that are more option-like than the market proxy. In particular, if the average stock in a mutual fund is more option-like than the average stock in the market proxy, a timing regression will result in a positive timing coefficient and a negative intercept, (which is usually interpreted as measuring the stock-selection ability of the fund manager). We might expect states in which mutual fund returns and market returns are both positive, due to their correlation, and where the mutual fund return is more positive than the market return, due to its larger positive skewness. These states would generate a positive timing coefficient even in the absence of market timing activity. We find an inverse relation between the timing coefficients and intercepts in the timing regressions as predicted by Jagannathan and Korajcyzck (JK). To test the relation more formally, we regress intercepts on timing coefficients cross-sectionally for TM model. Using daily data, the slope is negative and significant for TM model, indicating that estimates of stock selection and market timing are significantly negatively related. This result suggests that some of the positive timing coefficients in our sample could be spurious. In an effort to control for the JK source of spurious timing ability, we run the timing tests on a sample of synthetic funds that match the actual fund characteristics but have no timing ability of construction, as described in Section IV. If the synthetic funds exhibit timing ability of the same frequency and magnitude as the actual funds, then the estimated timing coefficients are likely to be spurious rather than being an evidence of ability. Table 4 provides the average descriptive statistics for the sample funds, and the market index. There is not much difference in the mean skewness and kurtosis figures for both of them on monthly basis. However, mean higher order moments are significantly greater for the sample funds compared to the market index when we use daily data. It seems that the Jaganathhan and Korajcyzck (1986) bias affects the daily results more than the monthly results in our case.
  9. 9. 315 International Research Journal of Finance and Economics - Issue 15 (2008) Table 4: Descriptive statistic for sample mutual fund schemes and the market index Portfolio Mean Sigma Skewness ( 1) Skewness ( 1) Excess Kurotosis Kurotosis Daily Fund Schemes 0.00123 0.017352 0.000872 3.858437 18.40438 21.40438 Daily BSE 500 0.0007 0.017094 -0.56165 0.315451 3.686799 6.686799 Monthly Fund Schemes 0.022266 0.08357 -0.32379 0.269727 0.899073 3.899073 Monthly BSE 500 0.013572 0.087812 -0.52643 0.277133 0.049493 3.049493 We concentrate on the differences between timing coefficients for the sample funds provided by the TM Model and the timing coefficients for the corresponding synthetic funds. The mean market timing differentials and the number of sample funds whose r1s are significantly higher than the synthetic counterparts are given in Table 5. The r differentials appear to be negative with only 5 sample funds exhibiting significantly better timing than the synthetic funds on maturity basis. However, on daily basis, our results are quite encouraging. The mean r differential is large and positive and about 88% (53 out of 60) sample funds time significantly better than synthetic funds when we use four-factor performance benchmark. Table 5: Comparing market timing results for sample funds and synthetic funds Panel-A- Monthly Data One factor Four factor Mean differentials +ve Mean differentials +ve -0.5931 5 -0.6913 5 +ve- positive gama values Panel-B- Daily Data One factor Four factor Mean differentials +ve Mean differentials +ve 0.5043 43 1.624 53 +ve- positive gama values Thus, the results on timing abilities are unimpressive on monthly basis as is found by prior research. The daily data based timing results are positive and not affected by Jaganathan-Korajcyzck bias. Moreover, timing abilities need to be evaluated in a multi-dimensional framework in light of the fact that the fund managers can gain by timing the style characteristics (especially the momentum factor) in addition to conventionally timing the market factor. 6. Summary and Concluding Remarks In this paper, we evaluate the performance of 60 growth and growth income mutual fund schemes in India. We examine both the stock selection skills and the timing abilities of the sample fund managers. We find that the evidence on selectivity improves marginally when we use higher frequency data such as daily returns instead of monthly returns. On daily basis, about 28% of the sample funds exhibit significantly positive alpha (selectivity coefficient) based on Jenson's four factor version, which controls for style characteristics such as size, value and momentum. The impact of observation frequency seems to be more pronounced on the results of market timing as demonstrated by Bollen and Busse (2001) for the US mutual funds industry. The fund managers in India do not seem to possess significant market timing ability when we use monthly data. However, 45% and 28% of the sample funds demonstrate significantly positive market timing coefficients for multi-factor versions of Treynor-Muazy and Hendrikson-Merton models respectively on daily files. Further, the strong market timing results are not generated by Jaganathan-Korajcyzck
  10. 10. International Research Journal of Finance and Economics - Issue 15 (2008) 316 (1986) bias. The timing ability seems to be multi-dimensional as about 10% of the fund managers time the momentum factor besides the market factor. Our study has strong implications for practitioners as well as researchers. From the practitioners' point of view our performance results especially for market timing are more positive than documented by previous empirical work. From the researchers' point of view, we observe that performance results become more positive when we use high-frequency data such as daily returns instead of monthly or annual returns. We also recommend use of multi-factor benchmarks for performance evaluation as shown in Carhart (1997), as they provide selectivity and timing measures after controlling for any spurious effects of style characteristics. We also advocate evaluation of multi- dimensional timing attributes, as fund managers besides timing the market factor, may also gain from timing style characteristics. Finally, our empirical tests involve use of emerging market data. We therefore prove robustness of new-generation performance measures which thus far have been applied mainly to mature market setting.
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