Let’s talk returns
The tide has not gone out yet, though it has just started receding. We believe that now, therefore, is the right time to start this conversation.
Before we dive into the numbers and analysis, we want to make it abundantly clear that this is an analysis of investment strategies, past performance and what we believe led to the large differences in performance. In no way should this analysis be considered as advice to change your own portfolios, nor should it be considered a prediction of the future.
We would also like to reiterate what we keep saying — investors should remain focused on, and much more concerned about, reaching their goals on time and not on the exact returns on their portfolios. Unlike actual goals, one can’t wear or drive or eat the number called returns.
Three months ago, we talked about returns for the first time. The timing of that communication was chosen strategically. The markets were on an upswing with little bad news trickling through. The highly concentrated (and hence, riskier) portfolios of our competitors and even the Nifty had outperformed us by about 2–3% over that short period.
A few of the readers pointed out this under-performance and asked us if we were right in using ETFs as opposed to mutual funds (MFs) that have produced significant ‘alpha’. For every person that puts up their hand and asks the question, there are always 10 others who have the same question but choose to stay silent.
We’ve hardly come across a VC or an Angel that didn’t ask us this question. Our justifications were most likely brushed off as theoretical constructs with no marketing value — because only MFs and returns sell in India. The blind belief that the Indian investor only cares about alpha (excess returns) and that Indian MF managers are gods who are able to consistently deliver this alpha that eludes the best of the global asset managers, only reinforced these assumptions.
However, this is the right time for anyone who believes in these fallacies to reflect upon their assumptions about both, the ETF as a product and about the Indian customer’s blind quest for alpha.
What went down this November?
November was a tough month for the Indian investor. Two major events unfolded on the night of the 8th of November, when the US elected Donald Trump as their next President and simultaneously, the Indian government decided to withdraw the Rs. 500 and Rs. 1,000 notes in circulation that represented about 85% of all the cash in the economy.
While both these events surprised many, they certainly were not black swan events. This is clear from the fact that the initial market reaction was exaggerated and also turned out to be wrong as the markets quickly corrected in the opposite direction.
The initial reaction to the Donald Trump victory was of a selloff in US stocks and currency. However, the American markets made a dramatic recovery and both the currency and the markets have been on the rise. The Dow is currently at a lifetime high.
Back home in India, the initial reaction to the withdrawal of currency notes was positive, based on the hope that black money will be weeded out and that the deposits will benefit banks. The Nifty Bank index led the charge as markets ended higher on the 9th.
However, reality sunk in soon as the markets absorbed the impact of the move whose objective was to withdraw the working capital of the black market economy. Owing to poor planning, this move unintentionally wiped out most of the working capital of the real economy (specifically, the unorganised sector). The markets made a quick U-turn and we are now staring at a prolonged period of uncertainty.
The rupee was the biggest loser, with both the rise in the US dollar and the flight of FII capital, taking it to a lifetime low.
What about investment returns?
When the markets are down about 10% in a month and the full year returns are close to zero, expectations typically are very low. Instead of the greed that drove us to invest in the first place, fear now drives our emotions and decisions. The famous Indian retail down cycle of flight to safety (gold and FDs) typically takes off at a time like this.
With all this bad news, how did the Tavaga portfolios fare?
The following table shows the returns of the portfolios for each of our 5 investor attitudes, over the last 1 month, over the last 1 year and between 8th September and 21st November, when the Nifty saw the largest fall this year of 11.3% (Max. Drawdown). The returns on the Nifty 50 Index are also presented for benchmarking purposes.
In a year that the Nifty returned only 4.7%, the average Tavaga portfolio was up 16.2%
How did our competition fare?
The following table shows the returns, of the 5 most popular ‘best’ MFs recommended by our competition (using the ‘best’ algorithms developed by the ‘best’ IIT/IIM grads), over the last 1 month, over the last 1 year and between 8th September and 21st November, when the Nifty saw the largest fall this year of 11.3% (Max Drawdown). The returns on the Nifty 50 Index are also presented for benchmarking purposes.
The average ‘best’ MF didn’t manage to beat the Nifty over the last month or ever over the last year. The average return for these funds for the last 1 year is 5.14%, which is only marginally above the Nifty return of 4.7%.
Tavaga portfolios on an average returned 16.21% for the last 1 year as compared to the 4.7% returned by the Nifty and 5.1% returned by these best MFs.
As one would expect, thematic portfolios are doing far worse than even MFs. With heavily concentrated portfolios that are back-tested against performance during a bull run, it is no surprise that even themes that carry the word ‘safe’ in their names have failed to outperform the Nifty benchmark.
How did Tavaga manage to beat most of the competition on returns using just ETFs?
Simple — Tavaga is a real ‘robo’, while most of our competitors rely on human judgment to build their portfolios.
The accepted worldview has been that active MFs give you excess returns, that your MF distributor’s algorithm is the best at picking the best MFs and that ETFs are lame products that only lose money. Since this worldview has now been disturbed, we assume that you will have the patience to go through this rather long post that goes into the fundamentals of investing to explain this ‘unexpected’ outcome.
Although long, we promise you that this will not be very technical. :-)
What does a real ‘Robo’ do?
A real robo simply follows instructions.
What is the primary objective of a ‘Robo’?
The objective of the robo is to standardise output and minimise costs involved in the manufacturing of a product or delivery of a service by removing the element of human labour and requirement for continuous decision making.
To achieve its objective, the robo uses a standard set of instructions created by a human, in the form of an algorithm or a program to produce a standardised output. For the developer to write a robust program, it is essential that the input is standardised and well defined.
Is there a real ‘Robo’ that I already know of?
Yes, of course — you know many. Let’s take the example of a car assembly. The robotic arm at the plant fits the engine, windows, doors and many other parts seamlessly. This process will work as long as the input (windows, mirrors, etc.) is of the defined specifications that have been programmed into the robotic arm.
If the input parts are not of standard size and shape (i.e. subject to human discretion), the robo will fail to assemble a car properly.
The job of the human is to simply manage and tweak the program running the robotic arm. The only time you would do this actively is when the input specifications change. The programmer needs to know the exact changes to the input beforehand to successfully reprogram the robotic arm.
On ‘Robo’ advisors
From the example above, what are the necessary conditions for a robo advisor to achieve its objective?
- The characteristics of the input need to be predictable
- The input needs to be produced by a robo as well
- The robo needs to know of any changes to the input characteristics beforehand
- The robo should always do exactly the same unless reprogrammed to do something different
- For the results to remain consistent, the robo should not be reprogrammed very frequently
So, are MF distributors really ‘Robo’ advisors?
The diagram below shows how the traditional MF production and sales process works.
- The research analyst, a human, uses discretion to predict the future and come up with recommendations and targets for each stock she covers.
- The fund manager, a human, uses his discretion to predict the future independent of the research analyst’s recommendations and then use the analyst’s research to build a portfolio that he believes will outperform his benchmark.
- The MF distributor/advisor, again a human, predicts not only the future of the markets but also the future decisions of the fund manager, with no access to the rich data input that the former two people have.
With three humans involved in the chain, all exercising their own discretion, this model is no robo. The primary objective of minimising costs also is defeated as there are three very expensive humans involved in the process. Investors pay 2.5% a year in expense ratios to pay for these three humans.
How about start-ups that use algos to pick funds?
When we replace the distributor with an algo, the diagram changes to something like this.
Now, the algo that replaced the distributor doesn’t have a determinate input. This is because there are still 2 humans upstream of the algo. This too fails all the criteria we laid out for a robo advisor in the section above, including the most important one of minimising costs. This only gets rid of the least expensive of the 3 humans involved, saving very little for the investor.
How is Tavaga a ‘Robo’ then?
There are two key factors that make Tavaga a real robo.
- Tavaga uses ETFs which track market indices. Indices are calculated using a formula and any changes to the indices are announced in advance for us to reprogram our robo to reflect the changes in input.
- Tavaga assumes that the future is unpredictable. It assumes that it is equally likely that the next year or two will be as bad as 2008–2009 or as good as 2006–2007. However, it also assumes that overall the next 10–15 years are going to be very similar to the last 10–15 years (growth with hiccups).
The first factor ensures that we deliver on the core objective of robo investing of minimising costs. As you can see from the diagram below, all three humans have been replaced by two algorithms saving you 2% on the fee right off the bat.
ETFs ensure guaranteed savings of 2% on the fee, while active funds give you a 20–25% chance of making an additional 2% after 5–10 years.
The second factor is what helped us beat pretty much every competitor out there in these tough times.
Since we assume we don’t know the future, we need to ensure that we are always prepared to face any eventuality — and not just when we expect something to happen, like the MF algos do. That is exactly what we have programmed our robo for.
Our portfolios are spread across Indian large and mid cap equity, Indian government bonds, gold and international equity. If the Indian economy ends up doing very well, our Indian equity portfolio will do really well. The rest of it will lag and create a bit of a drag on the overall return. This is exactly what happened when we shared our returns about 3 months ago. When the market was up significantly, our portfolios lagged by 2–3%.
On the flip side, when surprises hit the market like they did this month and dragged the markets down and our Indian equity ETFs down by almost 10%, the other part of our portfolio rose to the occasion and negated the losses created by these Indian equity ETFs. This helped our portfolios outperform the same benchmark by a whopping 7% just this month.
This is the core reason why we don’t see either Donald Trump’s victory or the cancellation of currency notes as black swan events. Both these events were well within our model parameters and therefore, our portfolios were able to easily cope with these surprises. Indeed, we see no reason to rebalance our portfolio even now, unless data shows that one of these events has actually altered the fundamentals of the markets.
Asset allocation (spreading money across different assets like bonds, stocks, gold etc.) is responsible for most alpha, even in active MFs. Selection alpha (stock picking) in India and abroad is either zero or negative. When you allocate across more diverse assets (instead of just sectors as active MFs do), the chances of producing consistent alpha are greater. That is how Tavaga managed to perform better than MF distributors who rely mostly on the fund manager’s skill to produce selection returns by taking on greater concentration risk.
The incumbents and all the start-ups that are anchoring their customers to past returns or their own biases (thematics), instead of anchoring them to goals, are targeting the 9–10% who believe they are high risk takers or have great knowledge of the markets.
Tavaga is designed to cater to the needs of the 50–60% who are most concerned about reaching their financial goals and are willing to take on some risk to get there on time.
At the end of this tough month, we are confident of being able to call up our customers and have a conversation about increasing allocation to their Tavaga portfolios. I’m not sure if our competition would have the courage to do the same this month.
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