Tactical Allocation, An Introduction, Part II

We introduced tactical allocation in our Free Float newsletter last week. We saw how, by using a simple moving average to toggle between equities and bonds, one can reduce drawdowns in their portfolios. In the ensuing discussion, we mentioned how excess-returns found during back-tests could be an artifact of illiquidity and high transaction costs of the markets in the past. It is not like people who traded markets before us were dumb (or somehow, we suddenly added 50 IQ points in the last 20 years.) There has to be a reason why the money was left on the table.

Indian markets have seen significant changes over time. It has got more deeper and wider with better liquidity, lower transaction costs and higher levels of automation. One way to gauge the efficacy of strategies is to use a metric like Sharpe or Information Ratio over rolling-windows through time. Also, the drivers of total returns in allocation strategies will be different across different time-horizons leading to different tax liabilities. It is useful to decompose returns to handicap them from a tax angle.

200-day Tactical Strategy
50-day Tactical Strategy
  • There are quite a few time-periods where tactical allocations will under-perform buy-and-hold equities.
  • Over a 10-year horizon, on an annualized basis, bonds have contributed about 1-4% to over-all returns.
  • Sharpes have been falling through time. One should expect this strategy to attenuate further.
  • Bonds have a bigger say in determining over-all returns in low equity return environments. So, use both assets!
  • Bond returns have been less volatile that those of equities’. However, that doesn’t mean that have been constant through time.
With and without bonds

In high equity-returns environments, bonds are usually an after-thought. However, running these strategies “equities-only” is ill-advised. In the chart above, returns in the recent 10-year periods have been palatable only because of the returns contributed by bonds.

Our personal experience has been that when equities drawdown, investors switch over to tactical strategies, only to abandon them once stocks recover. Thus, leaving their downsides exposed during the next drawdown; ensuring that they end up with the worst of both worlds.

Excess returns aside, SMA strategies are also useful in managing risk. With lower risk, one can employ a bit of leverage to boost returns. We have done deep-dives into variations of these strategies in the past. Interested readers can have a look at our SMA Collection.

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Book Review: This Is Not Propaganda

In This Is Not Propaganda: Adventures in the War Against Reality (Amazon,) Peter Pomerantsev lays out how the very tools that “democratize” information has been turned against democracy itself.

We live in a post-fact world where we are caught in a social-media driven doom-loop:

Social media is a sort of mini-narcissism engine that can never quite be satisfied, leading us to take up more radical positions to get more attention. It really doesn’t matter if stories are accurate or not, let alone impartial: you’re not looking to win an argument in a public space with a neutral audience; you just want to get the most attention possible from like-minded people.

It’s a lamentable loop: social media drives more polarised behaviour, which leads to demands for more sensationalised content, or plain lies. ‘Fake news’ is a symptom of the way social media is designed.

Social media technology, combined with a world view in which all information is part of war and impartiality is impossible, has helped to undermine the sacrosanctity of facts.

Peter Pomerantsev in This Is Not Propaganda

The book came out in 2019 and my personal experience has been that things have become worse. We now have WhatsApp groups where people self-select to receive the version of truth they desire. Political parties have setup up hundreds of such groups to make sure that we always hear what we want to hear.

While it is easy to blame social-media and messaging apps for the current state of polarization, traditional media has also embraced the post-fact world.

About The New York Times, for example:

Under Sulzberger, “there has been a heavy investment in the growth of opinion at the Times,” the journalist continued, noting that Bennet is a friend. “That was something that A.G. wanted and approved, because it drives their subscription strategy. New York Times readers like to read opinions—especially opinions that align with their own—and they increasingly don’t like to read opinions that don’t align with their own.”

The Daily Beast

We often don’t appreciate what we have and take the current state of the world as granted. However, democracy, freedom and progress are all recent occurrences. For the the vast majority of human history, monarchy, serfdom and stagnation was the norm. If we can’t even be bothered to be informed, do our choices mean anything? Before we know it, we’ll be back to the dark-ages.

Recommendation: Worth a read.

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Mitigating Sequence Risk through Asset Allocation

Sequence Risk (sometimes called sequence-of-returns risk) is the effect that the order of returns has on a portfolio.

For example, say you look at NIFTY and find that it gave negative 10% returns 4 years out of 10, and the rest of the years it gave positive 10% returns. You want to be invested for 5 years, so you expect 2 of those years to be negative. Sequence risk means that it is possible that you could have all of those negative 4 years during the 5 year period that you have invested.

The order in which losses occur impacts the portfolio’s terminal value

The annualized return of the NIFTY 50 TR index, since inception through May-2020, is roughly 12%. However, it has not been without periods where it was down over 50%.

NIFY 50 TR cumulative returns since inception

The lower part of the chart shows the drawdowns that have occurred in the past. Sometimes, it has taken years to recover from losses. The problem is that most investors have a pre-defined time-frame in mind. They want to be invested, say, for 10 years. Not “forever.” This is where sequence risk becomes a problem.

For a 10-year period, if you re-sample the monthly returns of the NIFTY 50 TR index and re-construct a return time-series, say, a 100 times, and plot the cumulative returns of each, it looks something like this:

The blue line is the “average” monthly return compounded for 10-years

Put another way, there is a non-trivial chance that an investor could end up with negative returns in a given 10-year period even if NIFTY’s return distribution did not change.

So, what is an investor to do? There are two approaches that have known to work:

  1. Diversification. Allocate to non-correlated assets.
  2. Get Tactical. Markets are known to trend. Try and preemptively exit from assets who’s prices are trending down.

There are a million different ways to skin each of these approaches. The simplest one is to add bonds to the portfolio.

NIFTY 50 TR and 0-5yr TRI

Bonds, especially sovereign bonds (issued by stable countries, of course) have very low drawdowns. So when you combine it with equities, you end up with a lot less sequence risk than pure equities.

If you simulate different proportions of equities and bonds and plot them along with their standard deviations, you’ll get an idea of where to trade-off stability with returns.

Trade-off between risk and returns

Let’s say that the sweet-spot is equity/bond ratios with avg. returns more than 10% but lower-bounded at 7.5%, you get 45/55, 50/50 and 55/45 as ideal allocations. While a 60/40 equity/bond allocation is the go-to for most advisors, there is no reason why it can’t be a more conservative 45/55.

Cumulative returns of different allocation ratios

Note the lower drawdowns of diversified portfolios. While the equity-only portfolio would have had an annualized return of 11.88% during the period, diversified portfolios ranged from 10.10% – 10.56%. The trade-off is that diversified portfolios have vastly less sequence risk.

The left-tail has been flattened while returns are now clustered more towards the average

Diversification changes the shape of the return distribution so that an average investor has a greater probability of experiencing average returns.

Read more about portfolio allocation across different assets here.

Code and images are on github.

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Probabilistic Sharpe Ratio

There is absolutely zero stability in metrics used to analyze mutual fund performance. Whether it is alpha, beta or information ratio, they all vary over time and across market environments. Using them to pick the next “winning” fund is pointless. They are, at best, a measure of what happened in the past.

Mutual Funds: A quick note on performance metrics

Sharpe Ratio was one of the first attempts at quantifying investment returns. It is simply the average return divided by the standard deviation of returns. However, the approximation that returns are normally distributed makes it unsuitable for comparing across different investments/strategies.

But what if you kept the basic assumption that returns are normally distributed and introduced adjustments for kurtosis and skewness? One such approach is Marcos López de Prado’s Probabilistic Sharpe Ratio (pdf.)

Let’s say the calculated (historical) Sharpe Ratio of the investment is SR^. The benchmark has a Sharpe of SR*. Then, the Probabilistic Sharpe Ratio, PSR(SR*) = Prob[SR <= SR^]

Intuitively, PSR increases as the standard deviation of SR decreases, increases with positively skewed returns and decreases with fatter tails.

So, given investments with similar Sharpe Ratios, invest in the one that has a higher PSR.

We took two large-cap mutual funds that have been around since 2006, the NIFTY 50 TR index and a basic SMA-50 long-only strategy over NIFTY 50 TR to see how the ratios shake out.

Probabilistic Sharpe Ratio

From what we see here, both from a historical Sharpe as well as PSR, given a choice between MF1 and MF2, one would pick MF1.

Our take: PSR is valuable in cases where you have to choose between multiple strategies with equally attractive Sharpe Ratios since it gives a confidence level around that number.

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90 days of Minimum Volatility

We had discussed portfolios optimized for minimum volatility back in January (see: Low Volatility: Stock vs. Portfolio) and had setup Themes that track such strategies. Broadly, these fall into ETL (Expected Tail Loss) and Min-Var (Minimum Variance) optimized portfolios that either take in the entire universe of stocks or only those that have a high momentum score. So, we have Minimum Expected Tail Loss, Minimum Variance, Momentum (Min-ETL) and Momentum (Min-Variance).

We expect optimized portfolios of momentum stocks to perform better during market up-trends. During bears, we expect them to have lower drawdowns than the market. The Corona Virus Panic put these portfolios in through the wringer. Glad to report that they came out largely unscathed.

Minimum Volatility Portfolios vs. NIFTY 50

Our back-tests showed that optimized momentum portfolio would under-perform “raw” momentum during up-trends but should have lower drawdowns during down-trends.

Momentum: Optimized vs. raw

Optimized momentum portfolios saved the investor about 3-4% in drawdowns compared to the “raw” momentum portfolio. May not sound like much in this instance but think about the cumulative effect over multiple market corrections when you invest for the long-term.

Overall, optimized portfolios delivered what they promised.

WhatsApp us at +91-80-26650232 if you are interested in knowing more about these strategies.

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