Author: shyam

The futility of market timing?

We recently came across an article put out by Albert Bridge Capital titled the “The futility of market timing.” You can read it here. The authors use the S&P 500 index to show that the gap between perfect market timing (always buying at the lows) vs. the worst market timing (always buying at the highs) doesn’t matter over long periods of time (20+ years.)

We were curious about how returns from the NIFTY 50 would look like if we ran the same experiment. We looked at consecutive 10- and 20-year rolling periods starting from 1991 where an investor buys Rs. 1 lakh of the index every year at

  1. the highest level of that year (H)
  2. the lowest level of that year (L)
  3. some random day (R)

We added the random scenario (#3) because that is more-or-less the opposite of trying to time the market.

10-year rolling-period returns:
nifty.market-timing.10.annual
20-year rolling-period returns:
nifty.market-timing.20.annual

Unlike the S&P 500, the NIFTY 50 has been an extremely volatile beast. And given the wide gap in terminal wealths, there is always going to be a temptation to try and time the NIFTY 50.

Code on github.

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Stock and Bond Correlations and Volatility

Stocks and Bonds are not correlated. They are not negatively correlated. And neither are they positively correlated. One doesn’t “zig” when the other “zags.” This is exactly why portfolio allocations start with stocks and bonds – the diversification math works on uncorrelated asset classes. When you combine the two assets together you get lower portfolio volatility.

Here are some charts that show how the two asset classes differ:

S&P 500 and 3-month t-bills

sp500.tbill.correlation.1mo

sp500.tbill.volatility.1mo

Nifty 50 and 0-5 year TRI

nifty50.z5.correlation.1mo

nifty50.z5.volatility.1mo

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Global Equities Momentum

A brief introduction to Global Equities Momentum and a look at various alternative scenarios.
Read: Part I

Could it work on value indices?
Read: Part II

Swapping momentum at the final step boosts returns significantly.
Read: Part III

Averaging out returns over different formation periods boosts returns and reduces drawdowns.
Read: Part IV

Track the virtual portfolios we setup using these strategies and follow the trades on our Slack channel.
Details: Trades and Portfolio

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Global Equities Momentum, Part IV

Our GEM backtest in Part III used a 12-month formation period to measure momentum. Here, we look at alternative formation periods with an eye on drawdowns.

6- through 12-month formation periods

GEM.6-12mo.cumulative

Even though the 10-month version has higher returns, the 6-month one has lower peak drawdowns.

The average of all

The problem with picking one formation period out of 6 is that it smells of data-mining. What happens if you average them all out?

GEM.avg.cumulative

The average works in reducing drawdowns compared to the traditional 12-month version.

GEM.avg.dd

GEM.m12.dd

We will setup a virtual portfolio for this “averaging” strategy and post the link here when it is up and running.

Code and more charts on github.

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Global Equities Momentum, Part III

We saw in our earlier posts on Global Equities Momentum (Part I, Part II) that by swapping the momentum equivalent of the equity indices in the GEM decision tree, one could significantly boost returns. Also, momentum trumped value.

Correlation between momentum and base indices

In the original GEM dual momentum model, the S&P 500 index was used to decide and to trade. What we claim here is that we can continue to use the S&P 500 index to decide, but we will use the momentum equivalents to trade. To back our claim, we present the correlation in the monthly returns of the base/momentum index pairs:
SP500.USA-MOMENTUM.correlation
WORLD%20ex%20USA.WORLD%20ex%20USA%20MOMENTUM.correlation

The indices move pretty much in tandem.

Robustness

If dual momentum is robust, then our strategy piggybacks on its robustness through the decision tree. Where we differ is in the way we express the trade. And our backtest shows that GEM is superior to buying and holding the underlying indices themselves both in terms of returns and drawdowns:

USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.cumulative

Annual returns:
USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.annual

Instruments

Implementing this strategy is fairly straightforward. You need to track the following ETFs:

  • SPY: for S&P 500
  • BIL: for US T-bills
  • IDEV: World ex-US
  • MTUM: US Momentum
  • IMTM: World ex-US Momentum
  • AGG: Aggregated bond

You will be long one of the last three ETFs above at any given point in time:
USA%20MOMENTUM.WORLD%20ex%20USA%20MOMENTUM.GEM.instruments

We will setup a virtual portfolio for this strategy and post the link here when it is up and running.

Code and more charts on github.

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