Author: shyam

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.

IMDB

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.

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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.

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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 ยท 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 (vix-adjusted-momentum-INDIA.R.md) 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.

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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 pluto.studio 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!

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Book Review: Alchemy

In Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life (Amazon,) Rory Sutherland makes a strong point that the pendulum has swung too far to the side of “rationality.” Businesses are so enthralled by scientific thinking that they have stopped taking risks.

The problem that bedevils organisations once they reach a certain size is that narrow, conventional logic is the natural mode of thinking for the risk-averse bureaucrat or executive. There is a simple reason for this: you can never be fired for being logical. If your reasoning is sound and unimaginative, even if you fail, it is unlikely you will attract much blame. It is much easier to be fired for being illogical than it is for being unimaginative.

The fatal issue is that logic always gets you to exactly the same place as your competitors. If you are wholly predictable, people learn to hack you.

For an investor, there are quite a few aha moments. To out-perform, you need to be different from everybody else. But if you are a professional money manager, then being different is very hard to defend if things don’t work out. So the larger your get, the lesser the risks you can take. If you think quantitative models will solve this problem, think again:

The risk with the growing use of cheap computational power is that it encourages us to take a simple, mathematically expressible part of a complicated question, solve it to a high degree of mathematical precision, and assume we have solved the whole problem.

We should also remember that all big data comes from the same place: the past. Yet a single change in context can change human behaviour significantly. For instance, all the behavioural data in 1993 would have predicted a great future for the fax machine.

The book is an insightful, yet easy read.

Recommendation: Must read!

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