Alpha analysis of some top performing multicap mutual funds

Normally, an investor prefers an asset that yields a higher alpha relative to others. Alpha or the active return is measured as the difference between the portfolio return and its benchmark return. Alpha is the barometer of a fund manager’s performance. Persistent generation of alpha is a reflection of the fund manager’s skill. This is in contrast to sporadic alphas which indicate luck. Everybody prefers persistent results rather than a few bouts of outperformance. It therefore becomes imperative to dig deep inside the performance of mutual funds in order to evaluate their alpha generating ability.

In the current study, we analysed the anatomy of a select few multicap funds. We did a parametric evaluation and found the top quartile funds in the group. The funds in the cohort include DSP BlackRock Opportunities Fund, Franklin India High Growth Companies Fund, SBI Magnum Multicap Fund and ICICI Prudential Value Discovery Fund. For all the funds, only growth schemes with regular options are taken into consideration.

The findings are as under

Based on the Carhart four-factor model, ICICI Prudential Value Discovery Fund – Growth generated an alpha of 80 basis points (bps) per month which translates to an Alpha of approximately 9.60 per cent per year for the last five years (study period: March 2012 to February 2017)

Fama French argued that it is better to compare funds based on t statistic and not on alpha estimate. This is because change in portfolio diversification of a fund during the study period impacts the reliability of the alpha estimate. t statistic is a measure of precision of an estimate and higher the value of t statistic, the higher is the reliability of the estimate.

For measuring alpha, we used the celebrated Fama French three-factor model and its extension, the Carhart four-factor model. Fama French model is actually an extension of the Capital Asset Pricing Model (CAPM) which propounded that expected returns of a portfolio are explained by systematic risk measured by Beta (β). The CAPM model is

R_p = α +R_f+ β (R_m – R_f) + Ɛ

Equals R_p - R_f = α +β (R_m – R_f) + Ɛ

In this equation, the LHS shows the excess return of the portfolio, beta is the risk of the portfolio with respect to the market, R_m is the market return, R_f is the risk free rate, α is the residual or the excess return not explained by the Beta.

Eugene Fama and Kenneth French, the proponents of the Fama French 3 Factor model found that the CAPM model cannot explain the variations in portfolio returns in totality. They contended that these return anomalies are captured by the sensitivities of the return to three factors namely Market risk premium (R_m – R_f), difference between the portfolio return of small cap stocks and portfolio return of large cap stocks, SMB (Small minus Big) and difference between the portfolio return of value stocks and portfolio return of growth stocks, HML (High Book Value to Price minus Low Book Value to Price). The model is

R_p - R_f = α +β_p (R_m – R_f) + s_p SMB + h_p HML + Ɛ


In this equation, (R_m – R_f), SMB and HML are the market risk premiums, size premium and value premiums respectively. β_p, s_p and h_p are the slopes of the factors in the time series. They figured out that the 3 factors can explain about 90 per cent of the variations in portfolio return. Carhart added a 4^th factor to the Fama French three-factor model called the Momentum factor. It is measured by the difference of the average return between the winner stocks and the loser stocks in a month (WML).

The Carhart 4 factor model is

R_p - R_f = α +β_p (R_m – R_f) + s_p SMB + h_p HML + M_p WML+ Ɛ

Regression results

We used the Fama French and momentum factors from the IIM (A) database and ran the regression. The time series considered for study is from March 2012 till February 2017 that is 60 months. The regression estimates are given as follows