Category: Investing Insight

Investing insight to make you a better investor.

Portfolio Churn

There is a famous scene in the “Wolf of Wall Street” where Matthew McConaughey (Mark Hanna) is explaining to Leonardo DiCaprio (Jordan Belfort) the concept of fugazi:

Mark Hanna: Number one rule of Wall Street. Nobody… and I don’t care if you’re Warren Buffet or if you’re Jimmy Buffet. Nobody knows if a stock is gonna go up, down, sideways or in f***ing circles. Least of all, stockbrokers, right? You know what a fugazi is?”

Jordan Belfort: *Fugayzi*, it’s a fake.

Mark Hanna: *Fugayzi*, fugazi. It’s a whazy. It’s a woozie. It’s fairy dust. it doesn’t exist. It’s never landed. It is no matter. It’s not on the elemental chart. It’s not f***ing real.


Gross returns of a high turnover portfolio is just that – fugazi.

Assume that there is an investment strategy that produces 12% in gross returns every year. Notionally, $1 should grow to $3.11 in 10 years. However, even if you assume brokerage charges are zero, demat charges don’t exist and there are no other taxes whatsoever, STT – Securities Transaction Tax – will take a slice of the portfolio at every churn.

A x600 churn, where 25% of the portfolio is replaced every month, will leave you only $2.94 in 10 years. A x1200 churn, where 50% of the portfolio is replaced every month – not uncommon with most momentum strategies – will leave you with only $2.79.

12% notional returns

And STT is not the only tax that is paid on a direct-equity portfolio. Capital gains tax of 10-15% also apply. These taxes have a non-linear impact on a portfolio’s compounded returns.

Investors should keep these in mind while comparing direct-equity portfolio returns.

Also, mutual fund NAVs are net returns. It is highly inappropriate to compare gross direct-equity returns with mutual fund NAVs.

Code for this analysis can be found on github. You can play around with it on pluto.

Mid-caps vs. Large-caps – A false choice?

It is generally believed that mid-caps give better returns than large-caps. But if you compare their historical returns, the difference is minuscule.

Is the pain worth the gain?

But mid-caps have often inflicted a lot of pain on their investors – spending most of their time in drawdowns. There is no diversification benefit because both of them play in the same circus. If you are a buy-and-holder, why bother with mid-caps at all?

Check out the notebook on pluto. You can play around with it once you login with your github account.

pluto: Your Research Velocity

pluto is our compute cloud for exploratory financial data analysis (intro). We built it, in part, to scratch our own itch and to offer an intuitive platform for financial market research that abstracts away most of the drudge work involved in data acquisition, storage and maintenance. The end goal is the increase the speed at which reproducible and shareable research occurs. Here’s a recent example.

VIX-Adjusted Momentum

On 10:13 AM · Jul 11, 2019, Darren (@ReformedTrader) tweeted out a link to CSSA that discussed a momentum strategy on the S&P 500 index. It divided the daily returns of the index by the day’s VIX – a poor man’s volatility adjustment, if you will. The back-test result was interesting and we wanted to reproduce it.

We started work on it at 2 PM. Using pluto’s Indices data-set, we could quickly setup the code and reproduce the results within the hour. See the github history of vix-adjusted-momentum-US.R notebook if you don’t believe it.

Next: if it worked for S&P 500, could it work for NIFTY 50? We fired up pluto again at 5:15 PM and quickly ran the strategy for different look-back periods ( before concluding that it doesn’t. Time taken: 15 minutes.

A quick glance at the annual return chart of the S&P 500 back-test showed that the out-performance occurs in periods of persistent high-volatility, like in 2008. But regimes change and signals fade. If you removed 2008 from the back-test, the strategy’s overall out-performance degrades considerably.

Next: does it beat a simple SMA system? vix-adjusted-momentum-and-SMA-INDIA.R answers that question in 20 minutes.

So basically, within an hour, anybody who had a passing interest in systems trading/investing could reproduce a strategy, check for applicability and extend it.

We will continue to add more data-sets to pluto and make it easier to use so that you can increase your research velocity.

Introducing pluto

A compute cloud for exploratory financial data analysis

We are proud to announce the launch of our open-source initiative to help data-scientists explore financial data-sets without having to go through the hassle of setting up data-bases, cleaning data and maintaining them on an ongoing basis.

pluto has both python and R libraries that you can use on to setup Jupyter notebooks. These notebooks are automatically backed-up on a repo created for you on github. To learn more and get started, check out the github page.

To get a glimpse of what is possible, have a look at some of the sample notebooks created by us on github. And to see how you can get started building on top of pluto, have a look at some of the code-snippets on goofy.

Questions, issues, pull-requests welcome!

Quant Model in Mutual Fund Wrapper

Most quant/smart-beta model based portfolios in India are built on direct-equity platforms – PMS, RIA, Themes and DIY. Their first major drawback is the 15% capital gains tax that needs to be paid the piper every year. The second one is the ability to track the “all-in” cost of maintaining the portfolio. This is where mutual funds have an advantage. Their pass-through status means that they don’t have to pay capital gains tax on portfolio sales and the end-of-day NAV gives investors the fully baked-in value of their portfolio. That said, mutual funds that wrap quantitative models have been few and far between. A new one has entered the fray: the DSP Quant Fund.

They were gracious enough to share their backtest. What follows is a 30,000 foot analysis.

Cumulative performance looks vs. a broad-market cap index looks good

cumulative performance vs. NIFTY 100 TR

However, excess returns seem to be tapering off…

excess returns over NIFTY 100 TR

Value factor seems to be a drag

If you regress the Quant Fund against the market-cap index and NIFTY strategy indices representing quality and value, you can see that returns have been primarily driven by the market (beta) and quality. Value seems to contribute negatively to overall returns. Part of the diminishing excess returns could be explained by the increasing influence of market beta to the fund’s returns.

drivers of returns

Why not just buy the NIFTY 200 Quality 30 Index Fund/ETF?

cumulative performance vs. NIFTY 200 Quality 30 TR

The SBI Quality ETF that tracks the NIFTY 200 Quality 30 Index has an expense ratio of 50bps. So while comparing the index against the Quant Fund, we need to haircut the index performance by that amount. Also, the Quant Fund comes out at 40bps for direct investors. The former is an ETF with minimal liquidity whereas the latter is an open-ended fund that can be redeemed at NAV – matters when you want to exit.

Qualitatively speaking…

DSP’s Quant Fund is a low-cost alternative to investors who want something more than market beta but not a full-fledged actively managed fund. It is tax efficient compared to other direct-equity platform solutions that over-weight the quality factor. And it is of comparable cost to most other quant/smart-beta funds/etfs for direct investors. Passive investors should definitely give it a strong look.

Code and charts are on github.