Author: shyam

Buy and Hold probably works only for US stocks

Social science research is said to have a WAGS problem. Most of their research is based on White American Graduate Students and fail to replicate in the real world.

Finance has a similar problem where, thanks to the depth of the data available on the US markets, most investment research is based on American data.

And US data clearly demonstrates the superiority of Buy and Hold. Over a 25+ year period, the probability of ending up with a loss is less than 0.1%

MSCI USA

If future returns are in the same vein as their past returns, US investors would be fools not to buy and hold forever.

However, this does not mean that the rest of the world should do the same thing. Every market is different. The Japanese experience is a study in contrast.

MSCI JAPAN

The probability of a negative outcome is a whopping 21% for them. i.e., there is a one-in-five chance that investors will not make any money investing in Japanese equities. If past is indeed prelude.

Indian investors have been better off than their Japanese counterparts. There is only a 6% chance of not making any money investing in Indian equities.

MSCI INDIA

All this goes to show that the US is a statistical outlier. “Buy and Hold” working in the US is an outlier. In every other market, there is a non-trivial chance that you will not make any money buying-and-holding equities.

When you look at research based on US markets, keep in mind that the probability distribution of returns that it is based on are an outlier. Anything that is long US equities will “look good.”

All investing is forecasting. And these probabilities will change – we are talking about equity markets after all. But know this before you adopt the “buy and hold” mantra.

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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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