{"id":914,"date":"2022-04-01T17:44:53","date_gmt":"2022-04-01T17:44:53","guid":{"rendered":"https:\/\/sharpely.in\/blogs\/active-passive-and-smart-beta-part-2-active-vs-passive-investing\/"},"modified":"2026-06-03T15:48:23","modified_gmt":"2026-06-03T15:48:23","slug":"active-passive-smart-beta-part-2-active-vs-passive-investing","status":"publish","type":"post","link":"https:\/\/sharpely.in\/blogs\/active-passive-smart-beta-part-2-active-vs-passive-investing\/","title":{"rendered":"Active, Passive and Smart Beta: Part 2 \u2013 Active vs Passive Investing"},"content":{"rendered":"

This is Part 2 of the 5-part series on Active, Passive and Smart Beta strategies.<\/p>\n

In Part 1<\/a>, we did an introduction and saw how they fit into the overall investment strategy landscape. We also compared them along 3 dimensions \u2013 Discretion, Cost and Capacity.<\/p>\n

In this blog, we will focus on Active and Passive style of management and do a detailed comparison. More specifically, we will try to answer the question \u2013 Should you invest in an actively-managed Mutual Fund or passive index funds? Or is there a middle ground?<\/p>\n

CAPM revisited<\/strong><\/h2>\n

We introduced the Capital Asset Pricing Model (CAPM) in Part 1. Since we will be using CAPM extensively in this analysis, let\u2019s do a quick refresher.<\/p>\n

CAPM states that expected excess return (excess of risk-free rate) on any security is proportional to excess return on the market (the equilibrium portfolio) and that proportion is the security\u2019s exposure to the market and is called beta.<\/p>\n

ER – Rf =\u00a0b x (ERm – Rf)<\/em><\/strong><\/p>\n

Very simply put, CAPM states that exposure to market is your one and only<\/strong> source of rewarded return \u2013 market is the ONLY risk factor that is rewarded.<\/p>\n

Therefore, as per CAPM, expected excess return (over and above beta-adjusted return) should be zero and hence active management cannot add any value.<\/p>\n

However, the entire goal of active management is to deliver excess return over beta-adjusted return i.e., the \u201calpha\u201d. By introducing \u201calpha\u201d fits into the above equation we get:<\/p>\n

ER – Rf = a + b x (ERm – Rf)<\/em><\/strong><\/p>\n

The role of active management is, in essence, to deliver positive alpha<\/strong>. Alpha is often misunderstood as excess returns over the benchmark and this is what is also used by the Mutual Fund industry \u2013 outperformance over the benchmark.<\/p>\n

In reality, alpha is the excess return over benchmark, adjusted for beta<\/strong>. A large-cap manager outperforming Nifty 50 index with a beta of 1.2 may not actually be delivering alpha.\u00a0<\/p>\n

In this analysis, we will use the correct definition of alpha to analyse the performance of actively managed Mutual Funds.<\/p>\n

Note 1: CAPM model is not perfect and has come for a lot of criticism over the years. At the same time, it still remains the cornerstone of quantitative finance and is widely used. And when used solely for the purpose of analysing actively managed funds, it\u2019s a simple and brilliant model.<\/p>\n

Note 2: Our analysis of Mutual Fund alpha is not new. It has been done exhaustively in the US markets. However, to the best of our knowledge, such an analysis for Indian Mutual Funds is not available.<\/p>\n

Data and methodology<\/strong><\/h2>\n

Our aim is to discover alpha (if any) generated by actively managed Mutual Funds.<\/p>\n

We rely on CAPM style linear regression (time series) of historical data for each fund to extract the alpha generated.<\/p>\n

First step is to fix the universe of funds. For our analysis, we include all types of Equity Mutual Funds except Hybrid Funds. Since we are interested in beta-adjusted returns, the analysis can be applied to any type of fund (large caps, small caps, thematic, sectoral). Therefore, we do not exclude any category.<\/p>\n

Next step is to define the benchmark or the market. Obvious choice is the Nifty 50 Index. However, we do not consider the returns of Nifty 50 TRI directly. Instead, we consider the lowest-cost investible proxy for Nifty 50 Index – Nippon India ETF Nifty BeES (NiftyBEES). A passive investor cannot directly hold the index and hence we use NiftyBEES as the benchmark.<\/p>\n

We could have also used Nifty 500 index as our market but instead we use Nifty 50 due to availability of liquid and low-cost investible proxy.<\/p>\n

Benchmark is same for all categories of funds since we are interested in beta-adjusted excess returns.<\/p>\n

Next, we take last 10 years of data from 1st Jan 2012 to 31st Dec 2021<\/strong> and split it into 2 equal buckets of 5 years each – Jan 2012 to Dec 2016 and Jan 2017 \u2013 Dec 2021.<\/p>\n

For each 5-year bucket, we select only those funds that have at least 4 years of data. For example, in Jan 2017 \u2013 Dec 2021 bucket, a fund that started in August 2017 will be included but a fund that started in April 2018 will not be included.<\/p>\n

For each bucket, and for each fund that qualifies in that bucket, we regress the excess monthly returns (over liquid returns) against excess monthly returns of NiftyBEES to get an estimate of alpha and beta.<\/p>\n

We also combine the two buckets to create a long-term bucket of last 10 years (since 1st Jan 2012) and repeat the regression exercise.<\/p>\n

The purpose of creating 2 5-year buckets is to assess the consistent of performance. The purpose of long-term bucket of last 10 years is to see if results change significantly if Mutual Funds are held for a long term.<\/p>\n

Finally, many analysis of Mutual Fund performance suffer from something called the \u201csurvivorship bias\u201d. These analysis only include the funds that exist today and does not include funds that were closed (likely due to underperformance). Research has shown that using only live funds can overstate returns by 1% to 2% compared to a universe that includes both live and dead funds (see On Persistence in Mutual Fund Performance, Mark M. Carhart<\/a>).<\/p>\n

We control for survivorship bias by including terminated funds in our analysis. However, due to data availability issues, our analysis may not have completely eliminated the survivorship bias.<\/p>\n

Summarising the result<\/strong><\/h2>\n

Let\u2019s first look at the table that summarizes our analysis<\/p>\n

<\/p>\n

The table above shows the total number of funds analysed in each period, average and median alphas, percentage of funds with positive and negative alpha.<\/p>\n

On the face of it, the results look mixed. While the period from Jan 2012 to Dec 2016 (henceforth called Period 1) looks amazing for Mutual Funds, an exact opposite happened in the next 5 years from Jan 2017 to Dec 2021 (henceforth called Period 2).<\/p>\n

Percentage of funds delivering positive alpha in Period 1 was a staggering 89%. But it fell dramatically to 43% in Period 2. If we consider the last 10 years (henceforth called Period 3), roughly 80% of funds delivered positive alpha.<\/p>\n

So is it okay to conclude that actively managed funds have outperformed the passive index?<\/p>\n

Not really.<\/p>\n

Most important data is the last column in the table above \u2013 Percentage of funds with \u201cStatistically significant positive alpha\u201d.<\/p>\n

Statistical significance is used in statistics to refute a null hypothesis. In our case, the null hypothesis is that alpha is 0 and hence a statistically significant positive alpha allows us to infer that the fund indeed has a positive alpha. If alpha estimate is statistically insignificant, we cannot infer that the fund has a positive alpha.<\/p>\n

Look at percentage of funds that have delivered statistically significant positive alpha. Even for Period 1, only 28% of funds delivered positive alpha.<\/p>\n

Looks like we need to explore further.<\/p>\n

A closer look at Jan 2012 to Dec 2016 period <\/strong><\/h2>\n

The period of Jan 2012 to Dec 2016 needs special attention here. The scale of outperformance is huge with median alpha of 4.4%. This is diametrically opposite to what happened in the next 5 years (Period 2) where median alpha is negative 0.4%.<\/p>\n

Let\u2019s for the time being keep statistical significance aside as we will not be trying to infer anything in this section.<\/p>\n

In fact, performance of last 10 years itself is dominated by huge outperformance in Period 1.<\/p>\n

So what explains this? We will discuss a few possible explanations (not exhaustive).<\/p>\n

1. Skill of the manager?<\/strong><\/h3>\n

One possible explanation could simply be skill. But 89% is a huge number.<\/p>\n