managerial charteristics of mutual fund managers affecting returns

1

Do the Managerial Characteristics, especially,the Financial Educational Qualification of the

Mutual Fund Managers matter in India?

Shreyo Mallik∗

December 26, 2014

Abstract

We study how the managerial characteristics, especially the financial educa-tional qualification of the mutual fund managers affect the mutual fund returns inIndia for the period 2010-13. We divide the financial educational qualification ofthe mutual fund managers into five categories- Master of Business Administration(MBA), Chartered Financial Analyst (CFA), Chartered Accountant (CA), MS (Fi-nance) and Financial Risk Management (FRM), and certifications other than CA,CFA or FRM respectively. We investigate how these qualification buckets affectthe returns as well as the various financial parameters that reflect the performanceof the mutual funds. We come to the conclusion that financial qualification sig-nificantly affects the returns as well as the various financial parameters governingthe performance of the mutual funds. They are also affected significantly by theage, the years of work-experience and the number of schemes managed by the fundmanagers. We observe that a fund manager who is younger but has more years ofwork-experience yields a higher latest return for her fund. We also observe thatfor a older fund manager with lesser years of work experience, the latest returnincreases significantly when we control for the previous return (lagged latest re-turn) and that the risk-adjusted return (jensensalpha) increases for a older fundmanager with more years of work-experience when we control for the market risk(beta). Moreover, the previous return (lagged latest return) has a higher explana-tory power in explaining the returns compared to the managerial characteristics.

Keywords: Mutual Fund, Return, Managerial Characteristics, Financial Parameters

∗Research Associate Trainee, Finance Lab, Indian Institute of Management Calcutta

2

1 IntroductionIn our current work, we are interested in studying how the managerial characteristics,especially the financial educational qualification of the mutual fund managers affect themutual fund returns together with the various financial parameters determining the per-formance of the mutual funds in India for the period 2010-13. We are also interestedin finding out how the age, the years of work experience and the number of schemesmanaged by the mutual fund managers affect the returns as well as the various financialparameters governing the performance of the mutual funds in India for the aforesaidperiod. We have divided the financial educational qualification of the mutual fund man-agers into five buckets namely Master of Business Administration (MBA), CharteredFinancial Analyst (CFA), Chartered Accountant (CA), MS (Finance) and Financial RiskManagement (FRM), and certifications other than CA, CFA of FRM respectively. Aftersufficient investigation, we came to the conclusion that financial qualification signifi-cantly affects the returns as well as the various financial parameters determining the per-formance of the mutual funds. They are also affected significantly by the age, the yearsof work-experience and the number of schemes managed by the mutual fund managers.We found that a fund manager who is younger but has more years of work-experienceyields a higher latest return for her fund. We also found that the latest return increasessignificantly for a older fund manager with lesser years of work experience when wecontrol for the previous return (lagged latest return) and that the risk-adjusted return(jensensalpha) increases for a older fund manager with more years of work-experiencewhen we control for the market risk (beta). Furtherthemore, we observed that the previ-ous return (lagged latest return) has a higher explanatory power in explaining the returnscompared to the managerial characteristics.

2 Literature ReviewGolec (1996) considered a sample of 530 mutual funds that have different fund objec-tives in order to inspect the relationship between managerial characteristics and fundperformance. He found that risk-adjusted performance of the mutual funds is directlycorrelated to the age, the educational qualification and the tenure of the fund manager.He also added that the return is not reduced by a higher management fee. This is in linewith the fact that managers with better management ability get higher fees from fundinvestors.

Kallberg (2000) reinforced Golec (1996) on the significance of management. For asample of 44 Real Estate Mutual Funds (REMF), Kallberg pointed out that the sampledfunds have positive average abnormal returns for the period 1987-98. Similarly, fundmanagers outperformed the benchmarks during down markets as compared to rising

3

markets. Baks (2002) examined the performance of the mutual fund managers takinginto account the sample of 2086 managers of equity mutual funds. He came to the con-clusion that the performance of the mutual funds and their fund managers could neverbe observed in isolation. Furtherthemore, he pointed out that about 50% of the perfor-mance of the mutual funds is dependent on the performance of their fund managers.

Prather & Middleton (2002) observed that superior managerial decision making at-tributes to the differential and persistent performance of the mutual funds. Ding &Wermers (2005) observed that fund managers of large mutual funds with more experi-ence outperformed their less experienced peers. Chevalier & Ellison (1999) found thatthe managers who are younger invest in unadventurous portfolios reducing market riskcompared to the older ones. They too exhibit better performance relative to their el-derly peers. Another interesting finding was that the performance of fund managers ispositively related to the quality of the educational institution they attended during theiracademic carrier. Berkowitz & Kotowitz (2002) examined the relationship between thefees being charged by the mutual fund managers and their performance. They observedthat for managers with better quality, there is a positive correlation between fees andperformance. On the contrary, for managers with worse qualities, there is a negativecorrelation between fees and performance.

Philpot & Peterson (2006) used a sample of 63 REMFs for the period 2001-03 in orderto study the effect of the individual managerial characteristics on the performance ofthe mutual funds. Philpot & Peterson (2006) examined the effects of managerial char-acteristics and fund characteristics on mutual funds’ risk-adjusted returns, market riskand management fees by estimating three equations. They estimated the equations byregressing risk-adjusted return, market risk and management fees on manager’s tenure,financial educational qualification, work-experience, and whether the fund is managedby a team or not. Medium evidence is found for the relationship between team-managedfunds and risk-adjusted return. They found that team managed funds have lower risk-adjusted returns than the mutual funds managed by single managers. Furtherthemore,the findings reflect the fact that the managers with longer tenure tend to attract highermarket risk levels and exhibited no relationship between managerial characteristics andmanagement fees.

Switzer & Huang (2007) examined the performance of the small and mid-cap mutualfunds in relation to the human capital characteristics such as gender,financial educa-tional qualification, tenure, and work-experience of the mutual fund managers. Thefindings gave evidence in favour of the funds’ performance that can be attributed to thedifferentiation in human capital characteristics of the mutual fund managers.

4

In contrast to the above evidences, Treynor & Mazuy (1966) presented market timingmodel which depicted the stock picking ability of the fund managers. They assumedthat the fund managers can speculate the market and change their portfolios accord-ingly. They studied a sample of 57 mutual funds and found that only one fund in theirsample has shown a characteristic line deviating from the linear characteristic line whichsuggests that no investor, professional or amateur, could, in potential outguess the mar-ket.

Daniel et al (1997) suggest that the characteristic-based benchmark performance dividesthe gross returns into three factors: CS (Characteristic Select), CT (Characteristic Tim-ing) and AS (Average Style) respectively. They studied a sample of 2500 equity fundsfor the period 1975-1994 and created a characteristic based benchmark which pooled125 portfolios from the stocks in the syock exchanges namely NYSE, AMEX, NAS-DAQ rexpectively. The observed that among the various categories of mutual funds, theaggressive-growth funds show some selectivity skill, but no characteristic timing skill.They came to the conclusion that the mutual funds can outperform the characteristic-based benchmark by about 1%, but it is nothing other than the management fee itself.

3 DataWe use the data from the ACE Mutual Fund (ACEMF) database. The ACEMF databaseprovides a comprehensive overview of the Indian mutual funds. The data used in ouranalysis covers the period from 2010-13. The data is updated until November 20, 2014.We collect the data on the latest return (latestaumcr), previous return(previousaumcr),best return for one month(best1month), best return for six months(best6months), bestreturn for one year(best1year), best return for three years(best3years), best return forfive years(best5years), worst return for one month(worst1month), worst return for sixmonths(worst6- months), worst return for one year(worst1year), worst return for threeyears (worst3years), worst return for five years(worst5years), average return for onemonth(average1month), average return for six months(average6months), average re-turn for one year(average1year), average return for three years(averag- e3years) andaverage return for five years(av- erage5years). We also collect the data on the numberof schemes managed by the mutual fund managers, their age and years of work expe-rience. We further collect data on the financial educational qualification of the mutualfund managers. Based on the educational qualification of the mutual fund managers,we form five dummies, namely mba(Master of Business Administration), ca(CharteredAccountant), cfa(Chartered Financial Analyst), msfinancefrm(Master in Finance and/orFinancial Risk Management) and othercertifications(Other Certifications). The mbadummy is equal to 1 for a manager who has an MBA, 0 otherwise. The ca dummytakes value 1 for a manager who has a Charered Accountancy certification, 0 other-

5

wise. The cfa dummy is equal to 1 for a manager who has a CFA certification, 0 oth-erwise. The msfinancefrm dummy takes value 1 for a manager who has a MS(Finance)and/or FRM certification, 0 otherwise. If a manager has certifications other than CA,CFA or FRM, then the othercertifications dummy is equal to 1 , 0 otherwise. We con-struct another dummy education in the closed interval [0,1]. This dummy takes value1 for a manager with MBA, 0.80 for a manager who has a CFA certification but notan MBA, 0.60 for a manager who has certifications other than CA, CFA or FRM butnot an MBA or CFA, 0.40 for a manager who has a CA certification but not an MBA,CFA or certifications other than CA, CFA or FRM, and finally 0.20 for a manager whohas an MS (Finance) and/or FRM certification but not an MBA, CA, CFA or certifica-tions other than CA, CFA or FRM. This dummy EDUCATION is constructed on thebasis of the frequency of the occurrence of a particular category of financial educa-tional qualification of the mutual fund manager. It takes a higher value in the closedinterval [0,1] if the particular educational qualification bucket has a higher frequency ofoccurrence in our data set. There are five categories of educational qualification- MBA,CFA, certifications other than CA, CFA or FRM, CA, and MS(finance) and/or FRM,arranged in descending order according to the frequency of their occurrence in our dataset. Accordingly, we assign the respective values 1, 0.80, 0.60, 0.40 and 0.20 respec-tively to these educational qualification buckets to our dummy education. The purposeof constructing this dummy is to identify if there is any propagation of informationamongst the mutual fund managers belonging to a particular financial educational qual-ification bucket. We further collect the data on the various financial parameters thatenable us to judge the performance of the mutual funds. This include the simple av-erage of returns (average), standard deviation of returns (standarddeviation), downsidescheme return(semistandarddeviation), slope(beta), corelation, beta correlation (beta-correlation), treynor, fama, sharpe, Jensen’s alpha (jensensalpha), sortino, return due toimproper diversification(returnduetoimproper), return due to selectivity (returnduetos-electivity), downside probability(downsideprobability), R-squared (rsquared), trackingerror(trackingerror), downsiderisk(downsiderisk), sdannulised and information ratio(informationratio)respectively.

4 MethodologyWe consider the following econometric models for each mutual fund manager i for ourgiven study as follows:

latestreturni = constant+schemesi+agei+experiencei+educationalqualificationi

+ previousreturni + controlsi + errori,∀i = 1(1)n

We judge the performance of the above model by adding controls in sequence- best

6

returns, worst returns and average returns respectively.

7

(1) (2) (3) (4)VARIABLES latestaumcr latestaumcr latestaumcr latestaumcr

previousaumcr 1.069*** 1.070*** 1.070*** 1.071***(0.00159) (0.00165) (0.00166) (0.00191)

bestreturn1month 14.29*** 16.76*** 10.72*(4.412) (4.790) (5.965)

bestreturn6months 1.366* 1.255 2.997**(0.752) (0.842) (1.277)

bestreturn1year 0.223 2.003** 1.032(0.702) (0.875) (1.090)

bestreturn3years -17.49*** -16.59*** -15.71***(3.563) (3.734) (3.399)

bestreturn5years 10.41*** -0.960 -11.58***(3.395) (3.601) (3.688)

worstreturn1month -40.49*** -43.95***(5.360) (8.577)

worstreturn6months 10.44*** 12.89***(2.632) (3.132)

worstreturn1year 0.105 -1.149(0.935) (1.269)

worstreturn3years -2.805* 0.417(1.619) (3.362)

worstreturn5years 17.33*** 1.035(2.215) (4.842)

averagereturn1month 21.92*(13.27)

averagereturn6months -8.736***(3.056)

averagereturn1year 2.660(2.148)

averagereturn3years -11.86(7.743)

averagereturn5years 40.21***(10.31)

schemes -11.16*** -11.40*** -11.56*** -11.77***(0.452) (0.538) (0.550) (0.601)

age 32.15*** 30.47*** 30.97*** 33.22***(3.423) (3.727) (3.831) (3.969)

experience -37.61*** -34.96*** -32.72*** -35.76***(4.171) (4.312) (4.196) (4.592)

mba -68.80** -82.38** -84.62** -73.37**(29.72) (33.77) (33.87) (34.57)

cfa -188.6*** -172.8*** -190.2*** -195.9***(22.23) (23.66) (24.07) (24.31)

othercertifications -25.29 -0.0348 28.06 33.85(21.82) (21.23) (22.76) (24.25)

ca 186.3*** 201.0*** 205.0*** 204.8***(41.14) (44.00) (45.58) (44.54)

msfinancefrm 170.1*** 165.2*** 163.4*** 156.1***(27.28) (28.39) (29.49) (31.61)

Constant -499.0*** -410.9*** -511.7*** -591.5***(66.57) (77.04) (75.03) (74.34)

Observations 2,362 2,217 2,217 2,217R-squared 0.998 0.998 0.998 0.998

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

8

We consider an extension of the above mentioned model by considering the averagereturn per scheme as follows:

latestreturnmi = constant+ agei + experiencei + educationalqualificationi

+ previousreturnmi + controlsi + errori, ∀i = 1(1)n

We also judge the performance of this model in similar lines by adding controls in se-quence - best return per scheme, worst return per scheme and average return per schemerespectively. We continue by taking into account that variablem denotes the averageof the variable per scheme and that the previous return is nothing but the lagged latestreturn.

9

(1) (2) (3) (4)VARIABLES latestaumcrm latestaumcrm latestaumcrm latestaumcrm

previousaumcrm 1.057*** 1.055*** 1.055*** 1.055***(0.00140) (0.00122) (0.00126) (0.00128)

bestreturn1monthm -3.620*** -0.315 -14.69***(1.384) (1.948) (4.711)

bestreturn6monthsm -4.693*** -5.863*** -5.435***(1.329) (1.594) (1.757)

bestreturn1yearm 1.045** 2.383*** 4.314***(0.420) (0.636) (1.431)

bestreturn3yearsm 3.869*** 2.868 -5.262*(1.300) (1.826) (2.716)

bestreturn5yearsm -0.751 -5.578** 2.901(2.636) (2.524) (2.827)

worstreturn1monthm -6.391* -19.40***(3.385) (7.385)

worstreturn6monthsm 4.660*** 2.643(1.416) (2.495)

worstreturn1yearm -1.320*** 0.746(0.335) (1.070)

worstreturn3yearsm -3.679*** -10.66***(0.957) (2.519)

worstreturn5yearsm 9.320*** 23.12***(1.717) (3.789)

averagereturn1monthm 27.00**(10.56)

averagereturn6monthsm 1.437(3.018)

averagereturn1yearm -4.235*(2.170)

averagereturn3yearsm 18.49***(5.024)

averagereturn5yearsm -26.30***(6.135)

age 3.652*** 4.044*** 4.106*** 4.086***(0.618) (0.625) (0.628) (0.634)

experience -4.539*** -4.394*** -4.282*** -4.367***(0.714) (0.673) (0.646) (0.659)

mba -19.90*** -22.40*** -23.88*** -25.24***(6.215) (6.425) (6.855) (7.019)

cfa -5.714* -2.323 -3.465 -5.778**(2.927) (2.967) (2.875) (2.568)

othercertifications 7.994* 12.16** 14.29*** 15.36***(4.554) (4.864) (5.287) (5.577)

ca -4.264 -2.285 -2.511 -2.073(5.178) (4.993) (5.018) (4.998)

msfinancefrm -5.936* -7.364* -9.104** -8.813**(3.064) (3.790) (3.996) (3.991)

Constant -54.43*** -69.99*** -73.45*** -69.58***(10.30) (9.869) (10.10) (9.800)

Observations 2,362 2,217 2,217 2,217R-squared 0.996 0.997 0.997 0.997


10

We also consider the following econometric specifications to have a elaborate overviewof how the number of schemes affects the returns:

latestreturni = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

previousreturni = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

bestreturni = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

worstreturni = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

averagereturni = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

11

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)VARIABLES latestaumcr previousaumcr bestreturn1month bestreturn6months bestreturn1year bestreturn3years bestreturn5years worstreturn1month worstreturn6months worstreturn1year worstreturn3years worstreturn5years averagereturn1month averagereturn6months averagereturn1year averagereturn3years averagereturn5years

schemes 192.2*** 190.2*** -0.0428*** -0.117*** -0.248*** -0.0810*** -0.0384*** -0.0319*** -0.0759*** -0.167*** -0.0537*** -0.0103*** -0.0459*** -0.111*** -0.239*** -0.0723*** -0.0249***(4.746) (4.410) (0.00273) (0.00825) (0.0180) (0.00614) (0.00338) (0.00195) (0.00527) (0.0110) (0.00490) (0.00204) (0.00171) (0.00473) (0.0101) (0.00395) (0.00204)

age -20.58 -49.32 0.136*** 0.166* -0.000731 -0.0508 -0.0411 0.0154 -0.00811 0.217 0.0785 -0.000804 0.0270 0.136** 0.199 0.0384 -0.0353(43.39) (40.57) (0.0279) (0.0950) (0.210) (0.0672) (0.0379) (0.0280) (0.0751) (0.173) (0.0641) (0.0266) (0.0228) (0.0626) (0.147) (0.0488) (0.0239)

experience 185.6*** 208.8*** 0.00191 0.283*** 1.153*** 0.274*** 0.153*** 0.0750** 0.124 0.266 0.127* -0.00190 0.148*** 0.227*** 0.790*** 0.208*** 0.0906***(53.93) (50.80) (0.0304) (0.104) (0.235) (0.0728) (0.0424) (0.0316) (0.0833) (0.190) (0.0728) (0.0285) (0.0253) (0.0687) (0.162) (0.0533) (0.0266)

mba -913.6** -790.1** -1.450*** -4.039*** -9.510*** -2.978*** -1.077*** -0.156 -0.632 -3.212*** 0.420 0.419** -0.786*** -1.874*** -4.549*** -1.274*** -0.705***(361.7) (334.7) (0.192) (0.694) (1.351) (0.428) (0.272) (0.171) (0.420) (0.963) (0.396) (0.181) (0.142) (0.413) (0.890) (0.316) (0.175)

cfa 1,266** 1,361*** -0.220 -0.379 5.108*** 1.732*** 1.291*** 0.272 2.092*** 4.408*** 1.827*** 0.782*** 0.668*** 1.973*** 6.025*** 2.416*** 1.100***(546.8) (514.1) (0.206) (0.685) (1.478) (0.495) (0.295) (0.185) (0.449) (0.993) (0.444) (0.200) (0.157) (0.422) (0.909) (0.360) (0.185)

othercertifications -4,919*** -4,577*** -0.839*** -2.509*** -3.679** 0.0679 -0.232 1.178*** 1.836*** 3.864*** 3.396*** 0.773*** -0.0290 0.386 1.425 1.389*** 0.348*(370.9) (342.3) (0.201) (0.782) (1.614) (0.517) (0.306) (0.152) (0.434) (1.145) (0.378) (0.208) (0.154) (0.460) (1.078) (0.379) (0.211)

ca -756.1 -881.4* -0.926*** -3.878*** -5.360*** -2.399*** -2.266*** -1.080*** -3.417*** -6.741*** -0.178 -1.206*** -0.521*** -1.730*** -3.354*** -1.226*** -1.548***(528.5) (481.0) (0.262) (0.799) (1.769) (0.597) (0.338) (0.207) (0.564) (1.218) (0.556) (0.256) (0.175) (0.486) (1.045) (0.416) (0.205)

msfinancefrm 8,553*** 7,840*** -1.952*** -7.986*** -11.93*** -3.542*** -2.181*** -0.919*** -3.081*** -8.829*** -1.359*** 0.0481 -1.805*** -5.233*** -10.74*** -3.093*** -1.207***(964.7) (883.4) (0.277) (0.903) (2.200) (0.712) (0.436) (0.118) (0.390) (0.895) (0.300) (0.190) (0.173) (0.381) (0.988) (0.351) (0.239)

Constant 1,677 2,035* 5.586*** 20.69*** 44.54*** 23.57*** 15.31*** 0.668 5.552*** 5.505 4.174*** 7.139*** 3.205*** 8.587*** 17.13*** 12.71*** 11.70***(1,256) (1,174) (0.722) (2.364) (5.111) (1.657) (0.929) (0.631) (1.722) (3.875) (1.495) (0.648) (0.522) (1.446) (3.269) (1.129) (0.577)

Observations 2,362 2,362 2,368 2,368 2,367 2,340 2,223 2,368 2,368 2,367 2,340 2,223 2,368 2,368 2,367 2,340 2,223R-squared 0.453 0.476 0.172 0.131 0.188 0.139 0.108 0.163 0.119 0.149 0.117 0.038 0.368 0.283 0.320 0.229 0.117


12

Similarly as before, we consider the extensions of the above mentioned specifica-tions by taking into account the average return per scheme as follows:

latestreturnmi = constant+agei+experiencei+educationalqualificationi+errori,∀i =1(1)n

previousreturnmi = constant + agei + experiencei + educationalqualificationi +errori,∀i = 1(1)n

bestreturnmi = constant+agei+experiencei+educationalqualificationi+errori,∀i =1(1)n

worstreturnmi = constant+agei+experiencei+educationalqualificationi+errorii,∀i =1(1)n

averagereturnmi = constant + agei + experiencei + educationalqualificationi +errori,∀i = 1(1)n

13

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)VARIABLES latestaumcrm previousaumcrm bestreturn1monthm bestreturn6monthsm bestreturn1yearm bestreturn3yearsm bestreturn5yearsm worstreturn1monthm worstreturn6monthsm worstreturn1yearm worstreturn3yearsm worstreturn5yearsm averagereturn1monthm averagereturn6monthsm averagereturn1yearm averagereturn3yearsm averagereturn5yearsm

age -40.94*** -42.19*** -0.0522*** -0.148*** -0.373*** -0.151*** -0.0902*** -0.0282*** -0.0925*** -0.185*** -0.0867*** -0.0419*** -0.0458*** -0.117*** -0.280*** -0.120*** -0.0680***(6.558) (6.151) (0.0130) (0.0336) (0.0801) (0.0303) (0.0176) (0.00973) (0.0252) (0.0619) (0.0252) (0.0134) (0.0111) (0.0277) (0.0683) (0.0269) (0.0150)

experience 59.17*** 60.28*** 0.118*** 0.334*** 0.794*** 0.287*** 0.177*** 0.0696*** 0.185*** 0.411*** 0.178*** 0.0796*** 0.104*** 0.253*** 0.602*** 0.233*** 0.129***(8.336) (7.863) (0.0161) (0.0387) (0.0945) (0.0345) (0.0203) (0.0119) (0.0307) (0.0725) (0.0294) (0.0145) (0.0138) (0.0330) (0.0809) (0.0312) (0.0168)

mba 144.4*** 155.5*** 0.0695 -0.408 -0.299 -0.210 -0.137 0.146** 0.464** 0.748* 0.160 0.0318 0.112 0.0546 0.328 -0.0168 -0.108(49.34) (46.05) (0.0949) (0.295) (0.591) (0.229) (0.145) (0.0702) (0.186) (0.404) (0.179) (0.0987) (0.0806) (0.223) (0.471) (0.198) (0.118)

cfa 606.8*** 579.6*** 0.238** 0.817*** 1.985*** 0.634*** 0.501*** 0.137 0.578** 1.220** 0.387* 0.364*** 0.262*** 0.822*** 1.943*** 0.605*** 0.428***(105.3) (100.2) (0.0957) (0.278) (0.564) (0.236) (0.147) (0.0894) (0.262) (0.497) (0.229) (0.131) (0.0900) (0.263) (0.514) (0.227) (0.136)

othercertifications 50.22 39.95 0.626*** 2.416*** 4.754*** 1.901*** 1.149*** 0.421*** 1.125*** 2.922*** 1.274*** 0.605*** 0.511*** 1.679*** 3.775*** 1.553*** 0.885***(43.15) (40.42) (0.107) (0.377) (0.750) (0.286) (0.179) (0.0770) (0.211) (0.559) (0.210) (0.120) (0.0897) (0.266) (0.612) (0.239) (0.143)

ca -90.04 -81.16 0.0206 0.0397 0.0617 0.0715 -0.189 -0.00400 0.116 0.163 0.175 -0.0920 0.0110 0.118 0.0850 0.0748 -0.138(63.02) (58.84) (0.0938) (0.318) (0.606) (0.260) (0.155) (0.0790) (0.238) (0.425) (0.215) (0.120) (0.0838) (0.266) (0.488) (0.231) (0.134)

msfinancefrm -37.78 -30.13 -0.845*** -2.579*** -5.189*** -1.789*** -1.009*** -0.535*** -1.478*** -3.788*** -1.229*** -0.452*** -0.684*** -1.950*** -4.326*** -1.518*** -0.740***(94.72) (89.04) (0.0948) (0.305) (0.656) (0.258) (0.172) (0.0624) (0.145) (0.343) (0.178) (0.121) (0.0774) (0.196) (0.441) (0.206) (0.137)

Constant 1,102*** 1,094*** 1.509*** 4.658*** 10.31*** 4.989*** 3.012*** 0.496** 1.945*** 3.753*** 2.400*** 1.599*** 1.055*** 3.122*** 6.769*** 3.705*** 2.396***(173.7) (164.1) (0.297) (0.860) (1.879) (0.754) (0.459) (0.222) (0.577) (1.336) (0.596) (0.335) (0.253) (0.667) (1.517) (0.650) (0.383)



14

Next, we consider the clustering of the different buckets of financial educationalqualification by bringing in the dummy education into our regression model as follows:

latestreturni = constant+schemesi+agei+experiencei+educationi+errori, ∀i =1(1)n

previousreturni = constant+schemesi+agei+experiencei+educationi+errori,∀i =1(1)n

bestreturni = constant+ schemesi+ agei+ experiencei+ educationi+ errori, ∀i =1(1)n

worstreturni = constant+schemesi+agei+experiencei+educationi+errori,∀i =1(1)n

averagereturni = constant+schemesi+agei+experiencei+educationi+errori,∀i =1(1)n

15

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)VARIABLES latestaumcr previousaumcr bestreturn1month bestreturn6months bestreturn1year bestreturn3years bestreturn5years worstreturn1month worstreturn6months worstreturn1year worstreturn3years worstreturn5years averagereturn1month averagereturn6months averagereturn1year averagereturn3years averagereturn5years

education 201.4 692.1 -0.0461 -1.135 -7.652* -0.113 1.742*** -0.549 -2.698*** -8.083*** -4.227*** 0.422 -0.437 -1.929* -3.868* -1.775* 0.427(727.0) (644.8) (0.519) (1.875) (4.012) (1.248) (0.649) (0.382) (0.997) (2.010) (0.874) (0.452) (0.371) (1.151) (2.281) (0.905) (0.418)

schemes 183.2*** 180.7*** -0.0551*** -0.144*** -0.305*** -0.0979*** -0.0449*** -0.0402*** -0.0898*** -0.206*** -0.0627*** -0.00462* -0.0582*** -0.139*** -0.310*** -0.0891*** -0.0284***(8.540) (7.877) (0.00389) (0.0122) (0.0281) (0.00959) (0.00532) (0.00274) (0.00770) (0.0165) (0.00669) (0.00269) (0.00255) (0.00688) (0.0156) (0.00599) (0.00310)

age -116.0** -141.3*** 0.154*** 0.152 0.111 0.0404 0.00520 0.0474 -0.0504 0.153 0.0924 0.0449 0.0659** 0.214*** 0.425** 0.112* 0.0195(48.34) (44.92) (0.0329) (0.107) (0.253) (0.0813) (0.0457) (0.0300) (0.0825) (0.182) (0.0679) (0.0279) (0.0266) (0.0719) (0.165) (0.0581) (0.0279)

experience 353.5*** 373.0*** -0.00731 0.357*** 1.193*** 0.239*** 0.149*** 0.123*** 0.379*** 0.779*** 0.314*** -0.0439 0.129*** 0.226*** 0.799*** 0.234*** 0.0625**(61.75) (57.59) (0.0331) (0.110) (0.266) (0.0817) (0.0471) (0.0333) (0.0893) (0.197) (0.0753) (0.0302) (0.0285) (0.0763) (0.178) (0.0605) (0.0292)

Constant 2,148 2,083 3.637*** 16.99*** 38.90*** 18.25*** 10.91*** -0.532 6.258*** 7.167 6.639*** 5.857*** 1.892*** 6.295*** 9.926** 10.83*** 9.025***(1,623) (1,493) (0.989) (3.341) (7.790) (2.439) (1.311) (0.728) (2.122) (4.553) (1.725) (0.701) (0.715) (2.038) (4.423) (1.668) (0.796)



16

Similarly as before, we consider the extensions of the above mentioned specifica-tions by taking into account the average return per scheme as follows:

latestreturnmi = constant+ agei + experiencei + educationi + errori, ∀i = 1(1)n

previousreturnmi = constant+agei+ experiencei+ educationi+ errori, ∀i = 1(1)n

bestreturnmi = constant+ agei + experiencei + educationi + errori,∀i = 1(1)n

worstreturnmi = constant+ agei + experiencei + educationi + errori,∀i = 1(1)n

averagereturnmi = constant+ agei + experiencei + educationi + errori,∀i = 1(1)n

17

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)VARIABLES latestaumcrm previousaumcrm bestreturn1monthm bestreturn6monthsm bestreturn1yearm bestreturn3yearsm bestreturn5yearsm worstreturn1monthm worstreturn6monthsm worstreturn1yearm worstreturn3yearsm worstreturn5yearsm averagereturn1monthm averagereturn6monthsm averagereturn1yearm averagereturn3yearsm averagereturn5yearsm

education 57.98 120.1 -1.135*** -5.449*** -8.962*** -4.057*** -2.217*** -0.576*** -2.026*** -4.188*** -2.818*** -1.365*** -0.826*** -3.518*** -5.951*** -3.359*** -1.935***(90.68) (83.59) (0.254) (0.960) (1.650) (0.736) (0.451) (0.213) (0.704) (1.080) (0.598) (0.344) (0.231) (0.793) (1.308) (0.655) (0.394)

age -46.26*** -47.53*** -0.0676*** -0.163*** -0.414*** -0.173*** -0.0986*** -0.0294*** -0.104*** -0.199*** -0.0969*** -0.0491*** -0.0541*** -0.129*** -0.304*** -0.136*** -0.0765***(6.147) (5.750) (0.0148) (0.0368) (0.0862) (0.0329) (0.0193) (0.0108) (0.0275) (0.0630) (0.0268) (0.0141) (0.0125) (0.0303) (0.0720) (0.0289) (0.0161)

experience 67.36*** 68.33*** 0.141*** 0.375*** 0.904*** 0.329*** 0.195*** 0.0807*** 0.218*** 0.481*** 0.211*** 0.0880*** 0.121*** 0.285*** 0.688*** 0.269*** 0.143***(9.209) (8.678) (0.0181) (0.0423) (0.103) (0.0377) (0.0216) (0.0133) (0.0347) (0.0763) (0.0323) (0.0157) (0.0155) (0.0367) (0.0872) (0.0342) (0.0181)

Constant 1,364*** 1,304*** 3.068*** 10.01*** 19.75*** 9.362*** 5.358*** 1.155*** 4.539*** 8.615*** 5.456*** 3.246*** 2.155*** 6.946*** 13.42*** 7.368*** 4.501***(168.1) (159.7) (0.401) (1.375) (2.593) (1.115) (0.684) (0.300) (0.946) (1.676) (0.856) (0.483) (0.343) (1.091) (2.020) (0.959) (0.574)



18

Furtherthemore, we intend to study how the different financial parameters reflect-ing the performance of the mutual fund get affected by the managerial characteristics,especially the financial educational qualification of the mutual fund managers by con-sidering the following specification:

financialparameteri = constant+schemesi+agei+experiencei+educationalqualificationi+errori,∀i = 1(1)n

Here, n denotes the total number of Indian mutual fund managers in our data set asof November 20, 2014.

19

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)VARIABLES average standarddeviation semistandarddeviation beta correlation betacorrelation treynor fama sharpe jensensalpha sortino returnduetoimproper returnduetoselectivity downsideprobability rsquared trackingerror downsiderisk sdannualised informationratio

schemes -0.000743*** -0.00469*** -0.00265*** -0.00536*** -0.00229*** -0.00528*** -0.0334** -0.000359*** 0.00370*** -0.000219*** 0.00706*** -0.000121*** -8.78e-05** -0.00218*** -0.00274*** -0.00245*** -0.00248*** -0.0895*** -0.00117***(4.15e-05) (0.000244) (0.000139) (0.000280) (0.000196) (0.000277) (0.0149) (3.68e-05) (0.000907) (2.54e-05) (0.000431) (3.46e-05) (4.25e-05) (0.000113) (0.000178) (0.000217) (0.000131) (0.00466) (7.12e-05)

age 0.000284 -0.00494 -0.00239 -0.0129*** -0.00313 -0.0124*** 0.158* 0.00117** 0.0106** 0.000850** 0.0166*** -0.00112** 0.00187*** -0.00508*** -0.00357 -0.00258 -0.00275 -0.0939 -0.00208**(0.000592) (0.00335) (0.00188) (0.00425) (0.00246) (0.00424) (0.0892) (0.000506) (0.00468) (0.000361) (0.00413) (0.000469) (0.000572) (0.00126) (0.00249) (0.00252) (0.00176) (0.0638) (0.000914)

experience 0.00371*** 0.0248*** 0.0133*** 0.0330*** 0.0168*** 0.0331*** -0.301** 0.00129** -0.0249*** 0.000257 -0.0296*** 0.00111** -0.000731 0.00938*** 0.0190*** 0.00689** 0.0126*** 0.473*** 0.00656***(0.000665) (0.00370) (0.00209) (0.00468) (0.00272) (0.00468) (0.141) (0.000576) (0.00541) (0.000404) (0.00435) (0.000545) (0.000654) (0.00132) (0.00277) (0.00286) (0.00194) (0.0707) (0.00100)

mba -0.0140*** -0.0808*** -0.0461*** -0.0218 0.00143 -0.0223 0.0568 -0.00735** 0.0640** -0.00672** 0.110*** -0.00463 -0.00293 -0.0380*** -0.0109 -0.0390** -0.0422*** -1.547*** -0.0283***(0.00404) (0.0204) (0.0116) (0.0267) (0.0173) (0.0267) (0.276) (0.00372) (0.0315) (0.00271) (0.0219) (0.00334) (0.00420) (0.00645) (0.0175) (0.0169) (0.0108) (0.389) (0.00631)

cfa 0.0181*** 0.0739*** 0.0412*** -0.0169 0.0285* -0.0148 -1.459** 0.0143*** 0.0173 0.0109*** -0.0796*** -0.00162 0.0123*** 0.00198 0.0281* 0.0323* 0.0366*** 1.407*** 0.0297***(0.00383) (0.0198) (0.0111) (0.0263) (0.0161) (0.0262) (0.723) (0.00343) (0.0623) (0.00240) (0.0264) (0.00320) (0.00378) (0.00763) (0.0163) (0.0167) (0.0104) (0.379) (0.00635)

othercertifications -0.00305 -0.00851 -0.00156 0.00303 -0.0130 0.00134 -1.469* -0.00154 -0.0343 0.000613 -0.00125 0.00160 -0.000940 0.00144 -0.0260 0.0467** -0.00100 -0.170 -0.0124*(0.00420) (0.0215) (0.0124) (0.0265) (0.0161) (0.0264) (0.883) (0.00359) (0.0303) (0.00295) (0.0271) (0.00271) (0.00368) (0.00787) (0.0164) (0.0184) (0.0116) (0.410) (0.00652)

ca -0.00457 0.0616** 0.0345** 0.0799** 0.0373* 0.0827** -1.621* -0.0136*** -0.104** -0.00948*** -0.0539 0.0117** -0.0219*** 0.0246** 0.0489** 0.0334 0.0339** 1.174** -0.00486(0.00489) (0.0268) (0.0150) (0.0392) (0.0223) (0.0391) (0.878) (0.00495) (0.0498) (0.00319) (0.0360) (0.00530) (0.00624) (0.0107) (0.0226) (0.0235) (0.0142) (0.511) (0.00819)

msfinancefrm -0.0320*** -0.142*** -0.0768*** -0.144*** -0.0195 -0.153*** 0.185 -0.0179*** 0.0257 -0.0153*** 0.0733* -0.0108*** -0.00401 -0.0317** -0.0460** -0.0726*** -0.0672*** -2.712*** -0.0433***(0.00462) (0.0297) (0.0170) (0.0354) (0.0212) (0.0347) (0.848) (0.00401) (0.0477) (0.00305) (0.0418) (0.00285) (0.00423) (0.0128) (0.0218) (0.0240) (0.0164) (0.567) (0.00765)

Constant 0.0589*** 0.453*** 0.247*** 0.807*** 0.481*** 0.784*** 1.264 -0.0144 0.293*** 0.00472 0.238** 0.0671*** -0.0596*** 0.416*** 0.324*** 0.411*** 0.241*** 8.643*** 0.0286(0.0138) (0.0750) (0.0423) (0.103) (0.0591) (0.103) (1.053) (0.0127) (0.0975) (0.00855) (0.105) (0.0115) (0.0145) (0.0313) (0.0590) (0.0571) (0.0399) (1.431) (0.0225)

Observations 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368 2,368R-squared 0.231 0.256 0.249 0.174 0.141 0.176 0.002 0.101 0.037 0.069 0.211 0.021 0.026 0.224 0.184 0.086 0.243 0.256 0.177


20

We also regress Jensen’s alpha on the number of schemes managed by the mutualfund manager and the managerial characteristics and we control for the market risk byusing beta. The specification of this regression is given by:

jensensalphai = constant+agei+experiencei+schemesi+educationalqualificationi+betai + errori, ∀i = 1(1)n

(1)VARIABLES jensensalpha

age 0.000850**(0.000361)

experience 0.000257(0.000404)

schemes -0.000219***(2.54e-05)

mba -0.00672**(0.00271)

cfa 0.0109***(0.00240)

othercertifications 0.000613(0.00295)

ca -0.00948***(0.00319)

msfinancefrm -0.0153***(0.00305)

Constant 0.00472(0.00855)

Observations 2,368R-squared 0.069Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

21

5 Results & AnalysisRegressing the latest return on the previous return, number of schemes managed by thefund manager and the managerial characteristics (age, years of work-experience andfinancial educational qualification) of the mutual fund manager, we get R-squared ofaround 100%. Obviously, we get no improvement by adding subsequent controls inour model. The variables that significantly affect the latest return are the previous re-turn, the number of schemes managed by the mutual fund manager, her age, her yearsof work-experience and some of his educational qualification buckets respectively. Wefind that the latest return increases significantly with the previous return, decreases sig-nificantly with the number of schemes managed by the mutual fund manager, increasessignificantly with her age, decreases significantly with her years of work-experience,decreases significantly if she has an MBA or a CFA certification and increases signifi-cantly if she has a CA certification or MS(Finance) and/or FRM certification. The latestreturn is also affected significantly by the best, worst and average returns.

Considering the average return per scheme and the same regression models as above,we find similar results except that a manager having a CA certification is no longersignificant and that a manager having an MS (Finance) and/or a FRM certification sig-nificantly decreases the latest return. Surprisingly, in both the model and its extension,the latest return increases significantly with the age but decreases significantly with theyears of work-experience of the mutual fund manager. The fact that the latest returndecreases significantly with the years of work-experience of the mutual fund managerpoints out that she has no learning over her tenure as a mutual fund manager.

Next, we regress the latest return, previous return, best return, worst return and averagereturn on the number of schemes managed and managerial characteristics (age, years ofwork-experience and financial educational qualification) of the mutual fund managers.We find that the return decreases significantly with the number of schemes managedby the fund managers, increases significantly with his age and experience, decreasessignificantly if he has an MBA, increases significantly if she has a CFA certification,decreases significantly if she has a CA certification, may increase or decrease signif-icantly if she has an MS (Finance) and/or an FRM certification or certifications otherthan CA, CFA or CA, CFA and FRM. Considering the extension of this model withthe average return per scheme we find that the return decreases significantly with theage of the fund manager, increases significantly with the years of her work-experience,increases significantly if she has an MBA or a CFA certification, decreases significantlyif she has an MS (Finance) and/or an FRM certification and increases significantly withcertifications other than CA, CFA and FRM. We notice a significant decrease in thevalue of the R-squared when we consider the average return per scheme which reflectsthe fact that the original model has a better explanatory power than the extended model.

22

Regressing the return on the dummy education, the number of schemes managed, theage and years of work-experience of the mutual fund managers we find that the re-turn may increase or decrease significantly with the number of schemes managed andthe age of the mutual fund managers but increase significantly with her years of work-experience. The return may increase or decrease significantly with the dummy edu-cation. However, the return mostly decreases significantly with the dummy educationreflecting that there is more or less no propagation of information among the mutualfund managers belonging to the similar the financial educational qualification bucket.Considering the extension of this model by taking into account the average return perscheme, we find that the return decreases significantly with the dummy education andthe age of the mutual fund manager whilst it increases significantly with her years ofwork-experience. However, the original model has a significantly higher R-squared thanthe extended one.

Further, we regress the various financial parameters reflecting the performance of themutual funds on the number of schemes managed by the mutual fund manager, herage, her years of work-experience and her financial educational qualification. We ob-serve that these financial parameters decrease significantly with the number of schemesmanaged by the mutual fund manager and her age but may increase or decrease sig-nificantly with her years of work experience. However, these parameters mainly in-crease with the years of work-experience of the mutual fund manager except for treynor,sharpe and sortino respectively. With an MBA qualification of the manager, the param-eters decrease significantly except for sortino. For CFA certification, the parametersdecrease significantly except for sharpe and jensensalpha. For CA certification, theparameters beta, correlation, betacorrelation, returnduetoimproper, downsideprobabil-ity, rsquared, downsiderisk and sdannulised significantly increase while the parametersaverage, treynor, fama, sharpe, jensensalpha, sortino, returnduetoselectivity and infor-mationratio decrease significantly. For a manager with an MS (Finance) and an FRMcertification, none of the parameters increase significantly excepting sortino. For man-agers with certifications other than CA, CFA and FRM, the parameters treynor andinformationratio decrease significantly while trackingerror increases significantly.

Regressing the risk-adjusted return (jensensalpha) on the number of schemes managedby the mutual fund manager, her age and years of work-experience while using beta asthe control for taking into care the market risk, we find that all the variables except theyears of work-experience are significant. jensensalpha decreases significantly with thenumber of schemes managed by the mutual fund manager, increases significantly withher age and decreases significantly with most of the financial educational qualificationbuckets except CFA certification with which it increases significantly.

23

We also find interesting correlations among the managerial characteristics and returns.The corelation matrix between the managerial characteristics and returns is given below:

24

Table 1: Cross-correlation TableVariables Age MBA CFA OtherCertifications CA MSFinanceFRM education Experience Schemes latestaumcr previousaumcr bestreturn1month bestreturn6months bestreturn1year bestreturn3years bestreturn5years worstreturn1month worstreturn6months worstreturn1year worstreturn3years worstreturn5years averagereturn1month averagereturn6months averagereturn1year averagereturn3years averagereturn5years latestaumcrm previousaumcrm bestreturn1monthm bestreturn6monthsm bestreturn1yearm bestreturn3yearsm bestreturn5yearsm worstreturn1monthm worstreturn6monthsm worstreturn1yearm worstreturn3yearsm worstreturn5yearsm averagereturn1monthm averagereturn6monthsm averagereturn1yearm averagereturn3yearsm averagereturn5yearsm

Age 1.000MBA -0.082 1.000CFA -0.014 -0.180 1.000OtherCertifications 0.216 -0.110 -0.081 1.000CA -0.152 -0.409 0.223 -0.069 1.000MSFinanceFRM 0.080 -0.148 0.179 0.157 -0.099 1.000education -0.053 0.895 -0.075 -0.234 -0.567 -0.045 1.000Experience 0.891 -0.145 -0.022 0.063 -0.194 0.076 -0.073 1.000Schemes -0.244 0.230 -0.197 -0.137 -0.157 -0.118 0.229 0.120 1.000latestaumcr -0.046 0.216 -0.145 -0.186 -0.139 -0.045 0.202 0.153 0.897 1.000previousaumcr -0.051 0.217 -0.144 -0.186 -0.143 -0.048 0.208 0.153 0.902 1.000 1.000bestreturn1month 0.258 -0.141 0.014 -0.043 -0.006 -0.063 -0.069 0.270 -0.140 -0.167 -0.168 1.000bestreturn6months 0.226 -0.091 -0.005 0.000 0.014 -0.165 -0.081 0.177 -0.256 -0.260 -0.261 0.837 1.000bestreturn1year 0.269 -0.101 0.016 0.011 0.051 -0.209 -0.091 0.233 -0.226 -0.165 -0.165 0.721 0.764 1.000bestreturn3years 0.204 -0.103 0.009 0.051 0.037 -0.178 -0.050 0.146 -0.241 -0.194 -0.194 0.696 0.761 0.933 1.000bestreturn5years 0.181 -0.063 0.084 0.068 0.003 -0.133 -0.035 0.058 -0.373 -0.290 -0.291 0.558 0.722 0.830 0.903 1.000worstreturn1month 0.272 0.041 -0.087 0.157 -0.068 -0.162 -0.080 0.182 -0.187 -0.149 -0.150 0.264 0.287 0.395 0.380 0.296 1.000worstreturn6months 0.176 0.083 -0.012 0.090 -0.059 -0.210 -0.091 0.121 -0.123 -0.086 -0.087 0.217 0.324 0.388 0.381 0.339 0.901 1.000worstreturn1year 0.241 -0.021 0.045 0.115 -0.021 -0.169 -0.151 0.169 -0.256 -0.203 -0.204 0.250 0.346 0.423 0.397 0.352 0.899 0.935 1.000worstreturn3years 0.261 0.085 -0.104 0.183 0.033 -0.256 -0.071 0.050 -0.170 -0.109 -0.111 0.130 0.238 0.346 0.375 0.327 0.826 0.825 0.762 1.000worstreturn5years 0.039 0.199 -0.143 0.125 -0.039 -0.259 0.078 -0.116 -0.118 -0.061 -0.063 -0.047 0.089 0.186 0.276 0.299 0.534 0.530 0.444 0.730 1.000averagereturn1month 0.396 -0.131 0.065 0.057 0.059 -0.171 -0.143 0.300 -0.432 -0.383 -0.385 0.726 0.705 0.806 0.772 0.677 0.677 0.596 0.658 0.521 0.318 1.000averagereturn6months 0.331 -0.091 0.064 0.067 0.046 -0.197 -0.134 0.243 -0.364 -0.310 -0.312 0.674 0.788 0.810 0.802 0.738 0.660 0.682 0.699 0.575 0.364 0.922 1.000averagereturn1year 0.370 -0.113 0.080 0.078 0.052 -0.198 -0.133 0.297 -0.348 -0.275 -0.276 0.614 0.658 0.858 0.810 0.707 0.690 0.664 0.739 0.581 0.350 0.935 0.931 1.000averagereturn3years 0.319 -0.072 0.061 0.126 0.055 -0.197 -0.114 0.172 -0.335 -0.256 -0.257 0.554 0.635 0.800 0.847 0.781 0.691 0.692 0.702 0.727 0.521 0.870 0.900 0.918 1.000averagereturn5years 0.172 -0.021 0.066 0.107 -0.009 -0.169 -0.035 0.035 -0.329 -0.254 -0.256 0.456 0.611 0.722 0.818 0.868 0.522 0.565 0.543 0.601 0.650 0.712 0.787 0.755 0.883 1.000latestaumcrm 0.061 0.017 0.191 0.013 0.009 -0.043 -0.020 0.055 -0.180 0.145 0.139 -0.016 0.026 0.148 0.134 0.195 0.205 0.226 0.242 0.230 0.211 0.178 0.196 0.237 0.246 0.248 1.000previousaumcrm 0.060 0.022 0.194 0.007 0.006 -0.042 -0.011 0.060 -0.175 0.147 0.143 -0.012 0.030 0.151 0.137 0.196 0.205 0.226 0.241 0.228 0.208 0.180 0.197 0.238 0.246 0.247 0.998 1.000bestreturn1monthm 0.192 -0.127 0.109 0.121 0.003 0.014 -0.185 0.166 -0.384 -0.351 -0.352 0.406 0.333 0.394 0.373 0.335 0.618 0.565 0.613 0.365 0.208 0.679 0.602 0.615 0.536 0.417 0.174 0.177 1.000bestreturn6monthsm 0.206 -0.151 0.086 0.183 0.031 -0.049 -0.239 0.164 -0.386 -0.349 -0.349 0.397 0.484 0.494 0.497 0.476 0.639 0.642 0.658 0.459 0.307 0.701 0.736 0.692 0.650 0.563 0.192 0.195 0.906 1.000bestreturn1yearm 0.210 -0.109 0.060 0.173 0.023 -0.090 -0.190 0.167 -0.372 -0.318 -0.318 0.388 0.418 0.559 0.521 0.464 0.684 0.656 0.691 0.505 0.341 0.733 0.717 0.750 0.678 0.556 0.237 0.240 0.930 0.948 1.000bestreturn3yearsm 0.174 -0.127 0.077 0.179 0.036 -0.054 -0.221 0.119 -0.419 -0.364 -0.364 0.286 0.350 0.449 0.459 0.425 0.646 0.637 0.663 0.502 0.358 0.660 0.662 0.671 0.646 0.539 0.257 0.259 0.907 0.947 0.952 1.000bestreturn5yearsm 0.183 -0.128 0.113 0.188 0.010 -0.019 -0.220 0.123 -0.442 -0.377 -0.378 0.233 0.330 0.396 0.413 0.438 0.591 0.595 0.611 0.455 0.340 0.602 0.620 0.607 0.598 0.535 0.300 0.302 0.875 0.929 0.906 0.975 1.000worstreturn1monthm 0.150 0.007 -0.029 0.104 -0.008 -0.122 -0.089 0.108 -0.206 -0.186 -0.185 0.206 0.188 0.276 0.275 0.223 0.753 0.681 0.669 0.564 0.428 0.570 0.519 0.532 0.516 0.404 0.153 0.156 0.877 0.809 0.849 0.837 0.802 1.000worstreturn6monthsm 0.125 0.001 0.009 0.099 0.008 -0.119 -0.115 0.087 -0.228 -0.202 -0.202 0.164 0.200 0.265 0.274 0.232 0.709 0.725 0.677 0.576 0.434 0.533 0.551 0.528 0.531 0.420 0.176 0.178 0.843 0.857 0.855 0.875 0.837 0.946 1.000worstreturn1yearm 0.152 -0.033 0.027 0.125 0.004 -0.105 -0.128 0.119 -0.251 -0.221 -0.221 0.200 0.208 0.294 0.285 0.226 0.739 0.716 0.750 0.561 0.385 0.574 0.560 0.589 0.543 0.400 0.191 0.193 0.880 0.849 0.891 0.880 0.830 0.951 0.953 1.000worstreturn3yearsm 0.140 0.006 -0.049 0.146 0.036 -0.185 -0.138 0.053 -0.269 -0.225 -0.226 0.108 0.185 0.280 0.294 0.251 0.711 0.708 0.673 0.699 0.560 0.524 0.538 0.539 0.587 0.476 0.248 0.248 0.762 0.793 0.821 0.880 0.842 0.891 0.931 0.900 1.000worstreturn5yearsm 0.110 -0.001 0.004 0.142 0.024 -0.133 -0.132 0.011 -0.325 -0.274 -0.276 0.053 0.146 0.221 0.253 0.238 0.615 0.622 0.580 0.602 0.594 0.466 0.479 0.465 0.518 0.477 0.266 0.266 0.719 0.764 0.759 0.864 0.867 0.820 0.867 0.818 0.936 1.000averagereturn1monthm 0.187 -0.062 0.049 0.115 0.003 -0.070 -0.140 0.154 -0.311 -0.279 -0.280 0.326 0.288 0.379 0.370 0.324 0.700 0.640 0.659 0.483 0.340 0.675 0.606 0.624 0.576 0.454 0.179 0.182 0.970 0.892 0.929 0.911 0.874 0.960 0.917 0.939 0.855 0.797 1.000averagereturn6monthsm 0.174 -0.090 0.068 0.146 0.026 -0.077 -0.188 0.136 -0.327 -0.289 -0.289 0.292 0.343 0.400 0.410 0.373 0.692 0.698 0.688 0.527 0.378 0.654 0.678 0.649 0.626 0.517 0.201 0.203 0.915 0.962 0.940 0.953 0.921 0.905 0.958 0.933 0.890 0.846 0.944 1.000averagereturn1yearm 0.192 -0.082 0.066 0.154 0.014 -0.087 -0.162 0.156 -0.329 -0.285 -0.286 0.311 0.321 0.437 0.418 0.358 0.718 0.689 0.728 0.531 0.364 0.685 0.663 0.700 0.635 0.495 0.223 0.225 0.938 0.923 0.973 0.946 0.895 0.917 0.921 0.964 0.876 0.805 0.964 0.964 1.000averagereturn3yearsm 0.163 -0.092 0.064 0.162 0.031 -0.070 -0.203 0.106 -0.375 -0.324 -0.325 0.216 0.272 0.362 0.372 0.341 0.673 0.670 0.680 0.558 0.408 0.615 0.616 0.624 0.622 0.503 0.260 0.261 0.888 0.911 0.920 0.980 0.953 0.881 0.925 0.918 0.943 0.909 0.921 0.959 0.948 1.000averagereturn5yearsm 0.155 -0.109 0.102 0.170 0.016 -0.027 -0.213 0.090 -0.415 -0.358 -0.359 0.169 0.252 0.315 0.336 0.341 0.608 0.616 0.617 0.498 0.409 0.555 0.572 0.558 0.567 0.507 0.287 0.288 0.852 0.894 0.872 0.962 0.978 0.829 0.874 0.853 0.896 0.939 0.872 0.923 0.888 0.969 1.000

25

6 ConclusionOverall, we find that the different financial education qualification buckets, namely,MBA, CA, CFA, MS (Finance) and FRM, and certifications other than CA, CFA orFRM have diverse impact on the returns. Some of them may cause the latest return,previous return (lagged latest return), best return, worst return and average return to risewhereas others may act the other way round. But it is indubitable that the return getsaffected significantly by the different categories of financial educational qualificationof the mutual fund managers. Also, the different financial parameters get affected bythese financial educational qualification buckets of the fund managers. Furtherthemore,the return as well as the financial parameters governing the performance of the mutualfunds are affected significantly by the age, years of work-experience and the numberof schemes managed by the mutual fund manager. We discover a interesting fact thatthe latest return increases significantly with the experience but not the age of the mutualfund manager, i.e. a manager who is younger but has more years of work-experienceyields a significant higher latest return for her fund. However, the latest return increasessignificantly with the age but decreases significantly with the years of work experienceof the fund manager when we control for the previous return (lagged latest return),ie., for a older fund manager with lesser years of work experience, the latest returnincreases significantly when we control for the previous return (lagged latest return).Moreover, the risk-adjusted return (jensensalpha) increases with both the age and thevears of work-experience of the mutual fund manager when we control for the marketrisk (beta), ie, the risk-adjusted return (jensensalpha) increases for a older fund managerwith more years of work-experience when we control for the market risk (beta).In thefirst two cases, the return is not devoid of the market risk induced in it. The returndecreases and becomes negligible as both the age and the years of work experience ofthe manager increases which is evident from the line diagrams of the latest returns versusthe age and years of work-experience of the mutual fund manager. Another interestingfinding is that the latest returns are better explained by the previous return (lagged latestreturn) than managerial characteristics as well as the best return, the worst return andthe average return. This is also evident from the line diagrams of the returns and thereturns per scheme against the previous return. To sum up, we may point out that themanagerial characteristics (age, years of work experience and educational qualification)play an important role in determining the returns and, on the whole, the performance ofthe mutual funds besides other factors as the previous return or the number of schemesmanaged by the mutual fund manager.

26

References[1] Kaplan S.N., Klebanov M.M., & Sorensen M., “Which CEO Characteristics and

Abilities Matter”,The Journal of Finance, forthcoming, 2014.

[2] Anand M., “Corporate Finance Practices in India: A Survey.”, Vikalpa: The Jour-nal for Decision Makers. vol. 27, no. 4., 2002.

[3] Berkowitz K.M.,& Kotowitz Y, “Managerial Quality and the Structure of Manage-ment Expenses in the US Mutual Fund Industry.”, International Review of Eco-nomics and Finance, 11(2002), 315âAS330, 2002.

[4] Chevalier, J. & Ellison G., “Are Some Mutual Fund Managers Better Than Oth-ers.”, The Journal of Finance, LIV (3), 875-899,1999.

[5] Daniel K., Grinblatt M., Titman S., Wermers M., “Measuring Mutual Fund Per-formance With Characteristic-based Benchmark.”, The Journal of Finance, LII (3),1035-1058, 1997.

[6] Baks K.P., “On the Performance of Mutual Fund Managers.”, Working paper, De-partment of Finance, The Wharton School, University of Pennsylvania, 2001.

[7] Ding B., & Wermers R., “Mutual Fund Performance and Governance Structure:The Role of Portfolio Managers and Boards of Directors”, Manuscript submit-ted for publication, Department of Finance, School of Business SUNY at Albany2005.

[8] Golec, Joseph H., “The Effects of Mutual Fund Managers’ Characteristics on TheirPortfolio Performance Risk and Fees.”, Financial Services Review, 5(2), 133-148,1996.

[9] Kallberg G.J., “The Value Added from Investment Managers: An Examinationof Funds of REITs.”, The Journal of Financial and Quantitative Analysis, 35(3),387-408, 2000.

[10] Philpot J. & Peterson C.A., “Manager Characteristics and Real Estate Mutual FundReturns, Risk and Fees.”, Managerial Finance, 32(12), 988-996, 2006.

[11] Prather L.J. & Middleton L.K., “Are N + 1 Heads Better Than One? The Caseof Mutual Fund Managers.”, Journal of Economic Behavior & Organization, 47,103-120, 2002.

[12] Switzer, L.N. & Huang, Y., “How Does Human Capital Affect the Performance ofSmall and Mid-cap Mutual Funds?”, Journal of Intellectual Capital, 8(4), 666-681,2007.

27

[13] Treynor J.L., &. Mazuy K.K, “Can Mutual Funds Outguess the Market”, HarvardBusiness Review, 44(4), 131-136, 1996.

Appendices

28

Figure 1: Educational Qualification of the Mutual Fund Managers

29

Figure 2: Experience of the Mutual Fund Managers

30

25 30 35 40 45 50Age

latestaumcr/averagereturn3years previousaumcr/averagereturn5yearsbestreturn1month bestreturn6monthsbestreturn1year bestreturn3yearsbestreturn5years worstreturn1monthworstreturn6months worstreturn1yearworstreturn3years worstreturn5yearsaveragereturn1month averagereturn6monthsaveragereturn1year

Data Source: ACEMF

Return vs Age of the Mutual Fund Managers

Figure 3: Return vs Age of the Mutual Fund Managers

0 10 20 30Experience

latestaumcr/averagereturn3years previousaumcr/averagereturn5yearsbestreturn1month bestreturn6monthsbestreturn1year bestreturn3yearsbestreturn5years worstreturn1monthworstreturn6months worstreturn1yearworstreturn3years worstreturn5yearsaveragereturn1month averagereturn6monthsaveragereturn1year

Data Source: ACEMF

Return vs Experience of the Mutual Fund Managers

Figure 4: Return vs Experience of the Mutual Fund Managers

31

25 30 35 40 45 50Age

latestaumcrm/averagereturn3yearsm previousaumcrm/averagereturn5yearsmbestreturn1monthm bestreturn6monthsmbestreturn1yearm bestreturn3yearsmbestreturn5yearsm worstreturn1monthmworstreturn6monthsm worstreturn1yearmworstreturn3yearsm worstreturn5yearsmaveragereturn1monthm averagereturn6monthsmaveragereturn1yearm

Data Source: ACEMF

Return per scheme vs Age of the Mutual Fund Managers

Figure 5: Return per scheme vs Age of the Mutual Fund Managers

0 10 20 30Experience

latestaumcrm/averagereturn3yearsm previousaumcrm/averagereturn5yearsmbestreturn1monthm bestreturn6monthsmbestreturn1yearm bestreturn3yearsmbestreturn5yearsm worstreturn1monthmworstreturn6monthsm worstreturn1yearmworstreturn3yearsm worstreturn5yearsmaveragereturn1monthm averagereturn6monthsmaveragereturn1yearm

Data Source: ACEMF

Return per scheme vs Experience of the Mutual Fund Managers

Figure 6: Return per scheme vs Experience of the Mutual Fund Managers

32

0 10000 20000 30000 40000Previous Return

latestaumcr/averagereturn5years bestreturn1monthbestreturn6months bestreturn1yearbestreturn3years bestreturn5yearsworstreturn1month worstreturn6monthsworstreturn1year worstreturn3yearsworstreturn5years averagereturn1monthaveragereturn6months averagereturn1yearaveragereturn3years

Data Source: ACEMF

Return vs Previous Return

Figure 7: Return vs Previous Return

0 2000 4000 6000 8000Previous Return per scheme

latestaumcrm/averagereturn5yearsm bestreturn1monthmbestreturn6monthsm bestreturn1yearmbestreturn3yearsm bestreturn5yearsmworstreturn1monthm worstreturn6monthsmworstreturn1yearm worstreturn3yearsmworstreturn5yearsm averagereturn1monthmaveragereturn6monthsm averagereturn1yearmaveragereturn3yearsm

Data Source: ACEMF

Return per scheme vs Previous Return vs per scheme

Figure 8: Return per scheme vs Previous Return per scheme

managerial charteristics of mutual fund managers affecting returns

Documents