Author: shyam

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 (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.

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!

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!

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.

Be Wrong. But Be Wrong With Confidence

I recently listened to this a16z podcast, A Guide to Making Data-Based Decisions in Health, Parenting… and Life, that got me thinking on how common personal finance and investing is with diet, exercise and parenting.

They all defy attempts at formulating universal laws.

Take the case of breast-feeding, for example. A truly scientific study on its benefits cannot be done. The data-set of identical or fraternal twins where the mother decided to breast-feed one and not the other for no particular reason (i.e., randomly) is simply not big enough to draw meaningful conclusions. We cannot isolate effects. What we observe are correlations. But correlation is not causation and most of them end up being spurious.

In parenting, oftentimes, we only have a sample-size of one. Most parents are guided by what their parents did and them their’s and so on. Most parenting “rules” are driven by societal norms rather than data. When I was a kid, the doctor advised by parents to substitute water with Coke or Pepsi when they traveled, which was quite often. The rationale was that water could be contaminated whereas bottled drinks are safer. Now, as a new parent, I find myself spending an inordinate amount of time trying to keep my kid away from food with added sugar. So, were my parents or doctor wrong? Should they have rolled the dice on possibly dirty water rather than sugar-water? There is simply no way to know for sure – there is no counterfactual. They simply made the best choice they could with the data that they had. Just like how I am doing with mine.

Try changing your diet. It is possibly the most difficult thing to do. We all know that vegetables are good, processed food is bad, and that we should stay away from sugar and deep-fried food. And yet, most of us will fail at being 100% healthy eaters. Why? Because a diet is a habit. In order to change it, you have to first change the environment, get rid of triggers and replace old shopping and consumption patterns with entirely new ones. A challenge that many of us find insurmountable.

Personal finance and investing have the same problems: more data is not necessarily better, there is no easy way to isolate effects, there is no counterfactual and people have hard but unstated preferences.

Your savings rate is like your diet. An advice to “save more” is as useful as “eat healthier.”

It is easier to believe that vaccination causes autism – a false but bold claim, made authoritatively – than to dive in to the messy data and statistics that back the benefits of vaccination. Just like it is easier to believe in “risk-free” trading strategies than to study one’s probabilities and sequence of returns.

Leading a healthy lifestyle only means that there is less of a risk that you will develop diabetes or get cancer decades later in life. Not zero. And neither is that risk quantifiable. Just like it takes decades for even the strongest investment strategies to play out.

It is also a lot like parenting. You will never know for sure whether you are making the “best” choice for your kids. You are only kind of sure of it once they grow up and start making their own choices.

But thankfully, there is a spectrum of good choices that can be made. The rest is up to the complex-dynamic beast of a system that we call markets… and life.