Category: Investing Insight

Investing insight to make you a better investor.

What is the right benchmark for funds owning US equities?

Some funds, the Parag Parikh Long Term Equity, for example, have a carve out for international (primarily US) equities. From a tax perspective, if a fund owns at least 65% of its portfolio in Indian stocks, it is treated as “Indian Equity Fund” for taxation – 15% short-term gains and 10% long-term gains (if held beyond one year). Otherwise, short-term gains (if held for less than 3-years) are added to your income and taxed at your marginal rate. So there is some advantage in packaging US stocks inside a an Indian equity fund. However, what is the appropriate benchmark in this case?

The PP-LTE Fund benchmarks against the NIFTY 500 TR index. But based on its portfolio, it should ideally be benchmarked against a 65/35 Indian Midcap/US Large Cap index. If you construct an Index with the M100 ETF making 65% of the portfolio and the rest allocated to the SPY ETF (tracking the S&P 500 index,) you will get an idea of the fund’s alpha/excess returns.

Parag Parikh Long Term Equity vs. 65/35 M100/SPY:

If you rebalance the 65/35 monthly, the LTE Fund’s annualized returns are 16.47% (Reg.) and 17.10% (Dir.) vs. the 65/35’s 15.30%. That’s excess returns of 1.8% for the direct plan, delivered to investors in a tax efficient manner, after all costs have been factored in. Another way to look at this is that even if the present management is replaced and investors do not have faith in the new one, they can just replace the fund with two ETFs and get almost to the same place.

Code and charts on github.

Most investors would be better off indexing

If you had invested in this fund in April 2006, would you still be invested in it?

Between 2006-04-03 and 2019-03-08 (13-years), SBI Magnum MIDCAP FUND – REGULAR PLAN – GROWTH has returned a cumulative 268.66% vs. NIFTY MIDCAP 100 TR’s cumulative return of 321.39%. Annualized returns are 10.96% and 11.96%, respectively.

A point of out-performance, a gallon of pain:

Between 2008-01-01 and 2013-01-01 (5-years), SBI Magnum MIDCAP FUND – REGULAR PLAN – GROWTH has returned a cumulative -27.09% vs. NIFTY MIDCAP 100 TR’s cumulative return of -1.54%. Annualized returns are -6.34% and -0.31%, respectively.

When it comes to discretionary active management, the problems are many:

  1. There are over 40 asset management companies. Almost all of them have a midcap fund. Almost all of them claim to be “value” investors.
  2. Value, as described in Graham And Dodd, cannot scale to the 10’s of thousands of crores that these funds collectively manage.
  3. So almost all funds are, at best, index plus a value and/or GARP and/or quality tilt.
  4. And occasionally, fund managers blow it. They hop over to other funds. Or retire.
  5. And occasionally, the investing style goes through a bad patch.
  6. And usually, the business of fund management (accumulating assets) wins over the profession of fund management (superior risk adjusted returns.)

There is no way that any investor can dodge all these minefields. So, the risk that a mutual fund investor takes = market risk + manager risk + style risk + capacity risk.

Investors should primarily allocate to index funds (take only market risk.) Actively managed discretionary mutual funds should be a niche.

Index Valuations, Part I

The NSE website has PE (Price to Earnings), PB (Price to Book) and Dividend Yields of indices going back a decade. Even though their methodology of calculating these has its drawbacks, they are consistently applied to all the indices. This makes an apples-to-apples comparison possible.

Here are the historical PE and PB ratios of the NIFTY 50 and NIFTY MIDCAP 50 indices:
NIFTY 50 Historical PE

NIFTY MIDCAP 50 Historical PE

And their relative ratios:
Relative NIFTY 50/MIDCAP 50 Historical PE

Relative NIFTY 50/MIDCAP 50 Historical PB

In the next part, we will explore if these can be used to time or switch between large-caps and mid-caps.

Code and charts on github.

Dual Momentum: NIFTY vs MIDCAP

In Global Equities Momentum, we looked at how toggling between US Equity Momentum and World ex-US Equity Momentum ETFs gave superior returns to buy-and-hold. Can the same framework be applied to toggle between NIFTY 50, MIDCAP 100 and bonds?

Relative Performance

For dual momentum to work, you need the excess returns of the two equity assets to be un-correlated (or very loosely correlated.) Here is the plot of rolling 200-day cumulative returns of the equity indices minus that of the 0-5 year bond total return index:

excess returns of NIFTY and MIDCAP
The line marked RELATIVE is the difference between MIDCAP 100 returns and NIFTY 50 returns.

What we see here is that there is a high degree of correlation between the two when it comes to excess returns over bonds. At the same time, however, the relative performance between the two equity indices tends to be sticky. So, a dual momentum model tuned to sniff out the “regime” should be able to give returns better than buy-and-hold.

For reference, here is how buy-and-hold performed:
buy-and-hold NIFTY/MIDCAP/bonds


Over different look-back periods, here is how the dual-momentum strategy worked:
NIFTY/MIDCAP dual momentum over different look-back periods

Over 3- and 4- month look-backs, the model does seem to show higher returns and lower drawdowns than buy-and-hold. But this is probably going to over-fit past data. But what happens if we specify the model to use “any” of the lookbacks? i.e., stay in equities if any of the look-backs signals the NIFTY 50 has out-performed bonds over the same period?

NIFTY 50/MIDCAP 100 dual momentum over any lookback

Here are its worst drawdowns:
drawdowns of NIFTY 50/MIDCAP 100 dual momentum over any lookback

A model setup this way has lower drawdowns and returns that better than NIFTY 50 but lower than that of MIDCAP 100 buy-and-hold. It really just boils down to how much pain you can bear – for those with a lot of testicular fortitude, buy-and-hold MIDCAPs are the best. But for the rest of us mere mortals, this strategy makes sense. And unlike an SMA model that checks for potential trades every day, this one checks only once a month. This keeps transaction costs low for long-term investors.

Code and more charts are on github.

Book Review: The Geometry of Wealth

In The Geometry of Wealth: How to shape a life of money and meaning (Amazon,) author Brian Portnoy starts with the difference between being rich and being wealthy and takes us through what it means to our investments and career choices.

While it may come across as a cross between a self-help and a personal-finance book, it is not as boring as those typically are. Besides, it is concise, running at around 150 pages.

What I like the most about this book is that it makes you take a step back and question why you are doing what you are doing. What is the whole point of saving and investing? It is a means to an end. Not an end in itself. Also, we are wired to think that something complex is better. But in investing, it is the simpler things that compound your portfolio. And simple doesn’t mean easy.

Recommendation: Worth a read.