A Conservative Tactical Momentum Strategy Using Bond ETFs [View article]

For the top 1 of the MF version backtested over 1994 - 2015 (end of July) and with VCVSX, FNMIX, VUSTX, VFISX, FAHDX in the basket CAR is 14.38% and MaxDD is -14.58% (monthly data from Yahoo). Volatility is 10.77%. Monthly win% is nearly 71%. Time spend in DrawDown is 55% with the longest DD period being 21 months.

This chart compares the equity growth of the CMT (MF, top 1) strategy (black) against the (monthly rebalanced) EW portfolio of its constituents (red), SPY (blue) and VUSTX (green): http://bit.ly/1MR58z9

Dividing capital over the best two funds of the MF basket results in a CAR of 13.11% and -11.57% MaxDD with Volatility at 8.41%. Win% is nearly equally high and TiD is 50% with a longest DD period of 17m.

Interestingly, applying a single lookback of 3 months (only) to the MF basket (top 1), CAR goes slightly up to 14.71%, but at the cost of -19.11% MaxDD. The win% is just below 70%. Time spend in DD goes up to 58% with a longest DD of nearly 3 years (34m).

For the ETF version backtested over 2010 - 2015 (end of July) with CWB, JNK, TLT, SHY, PCY in the basket CAR is 17,43% and MaxDD is -6.14%. Volatility is 10.91%. The data source for the ETFs is Norgate's Premium Data service.

The EFT allocations and their monthly returns are shown on this combined chart: http://bit.ly/1MR58zd

Thank you very much for taking the trouble to develop your own Monte Carl engine. Nicely done. And yes, I do take full responsibility, but at the same time I am glad we're deep in the woods now.

I really am, because your results offered a benchmark by proxy for taking another stab at Monte Carlo Analysis. With modified settings, hopefully the right ones this time, my new results pretty much confirm yours. To anyone terrified by my earlier comment, I apologize.

The Monte Carlo methodology is the same as described in my earlier comment, so trades are still taken into account without any dependency with the prior or following month. Statistics are derived from 1,000,000 random simulations again.

The points of interest are the 95% levels mentioned by Varan: - CAGR for 95% of the cases over 15% (match) - MaxDD for 95% of the cases under 32% (near match)

Albeit the meaning of the "|"-sign is not clear to me, I think you're pinpointing an issue that does bother me. The 139 trades have a profit spread from $132K - $-91K. However the distribution of the spread is skewed to the right (see chart below), due to the compounding effect as a result of the strategy's profitability combined with continued full capital commitment. Distribution of returns ($): http://bit.ly/1UPh5Ij

Due to the long tail from -100% the MC CAR graph isn't very informative all, because the charted curve gets too compressed. However the MC Final Equity is still presentable: http://bit.ly/1PvVdyU

The posted performance chart ends with equity at $1.413K. As the MC Equity distribution graph shows this is just about at the 50th percentile (see also the posted table with key levels).

If the chart is representative - emphasis added on "if" - there is 45% probability that the strategy will have a maxDD of 30% or less. The flip side: there is a 55% probability that the strategy's maxDD will be 30% or higher. Since we're talking absolute terms, less is better. Please do take note of Varan's observation.

Recently Monte Carlo Analysis was introduced in my backtest platform of choice. While I'm still exploring this feature and its implications, so bear with me, using pretty much out-of-the-box settings, Monte Carlo Analysis may reveal real risk hiding in the darker corners of the strategies we discuss over here.

The Monte Carlo simulation was run for the new composition IJJ, EFA, IEV, EPP, QQQ, EEM, TLT. First, a backtest starting December 31, 2003 and ending July 31, 2015 is performed. The backtest follows the GMR Simple rules for monthly portfolio reformation, but with trades executed at MOC. Next, from this original set of 139 trades the Monte Carlo engine randomly picks trades to produce a new, random set of again 139 trades. Some original trades may be skipped and some may be used more than once (permutation with repetition, or random sampling with replacement). This trade randomization process is repeated many times, for the below results even 1,000,000 times. Finally, during post-process distribution statistics and charts are generated from the data obtained during the randomization process.

The equity curve and statistics from the initial backtest look familiar to the results reported by Varan and others (me too): http://bit.ly/1Ww83Sg

The outcome of the Monte Carlo Analysis is summarized in a table showing key statistics derived from the distribution of the simulation results: http://bit.ly/1Ww85to

Subject to further investigation, by imposing a reasonably conservative confidence level of 95% it becomes possible to assess the probability of a specific outcome. With the said confidence level in mind, probability states the strategy's MaxDD% will reside somewhere between -11.71% and -100% as reported by the the table and chart (see red window). The reported MaxDD of -14.40% appears to have only a probability of less than 10%...

Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

Currently with a rolling 12 month return of nearly -19% for GMRE one wonders: is this just one of those difficult times the model investor has to deal with or is the strategy actually broken and in urgent need of a fix?

Suppose the strategy's universe would be adjusted similar to the new composition of Varan's Simple GMR, would the model still be delivering returns outperforming the general market, i.e. SPY?

Preserving GMRE's special flavour as much as possible, I suppose the universe would then become something like: SSO, QQQ, EFA, IEV, EPP, EEM and EDV with SHY still as safe haven.

Update: the "live" signals are now in accordance with the new composition of the GMR Simple universe: http://bit.ly/1IgkYO9

To add some perspective for the 7 asset basket: - profit table: http://bit.ly/1f0zhQ5 - allocations: http://bit.ly/1f0ziTX Results are with total return quotes provided by PremiumData.

@CostBasis: with IWC in the basket too, both CAR and MaxDD drop by about 3%.

By popular demand I published a page with "live" signals for the Simple GMR strategy: http://bit.ly/1IgkYO9 Use the signals in the spirit of varan's above remarks. Please take note of the slightly different approach: reforms are at the close of each month. (A post about the real risk involved in these kind of strategies is pending, but most likely won't be published before the summer ahead)

Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

Using Norgate Premium Data on my AmiBroker platform I have FEZ too as the pick for April: http://bit.ly/1C7ze8j (slight differences for the 3 months returns though) JW

Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

I don't want to post too much about EAA here, since the model differs from GMRE a lot, but again just as example to demonstrate a model that is adapting to the economic climate (or perhaps better: stockmarket sentiment) take a look at this Manhattan Allocation Diagram: http://bit.ly/1F1W71h

The diagram shows how the model picks the Top3 or Top2 assets based on an average momentum weighting along with an absolute momentum filter (hence the occasional Top2). C(r)ash protection is turned off but the model still yields a CAR of 14.80% with a MaxDD: -10.74% (monthly data) over a backtest covering 20 years.

## A Conservative Tactical Momentum Strategy Using Bond ETFs [View article]

This chart compares the equity growth of the CMT (MF, top 1) strategy (black) against the (monthly rebalanced) EW portfolio of its constituents (red), SPY (blue) and VUSTX (green): http://bit.ly/1MR58z9

Dividing capital over the best two funds of the MF basket results in a CAR of 13.11% and -11.57% MaxDD with Volatility at 8.41%. Win% is nearly equally high and TiD is 50% with a longest DD period of 17m.

Interestingly, applying a single lookback of 3 months (only) to the MF basket (top 1), CAR goes slightly up to 14.71%, but at the cost of -19.11% MaxDD. The win% is just below 70%. Time spend in DD goes up to 58% with a longest DD of nearly 3 years (34m).

For the ETF version backtested over 2010 - 2015 (end of July) with CWB, JNK, TLT, SHY, PCY in the basket CAR is 17,43% and MaxDD is -6.14%. Volatility is 10.91%. The data source for the ETFs is Norgate's Premium Data service.

The EFT allocations and their monthly returns are shown on this combined chart:

http://bit.ly/1MR58zd

Thanks for sharing guys.

## Simple GMR [View instapost]

I really am, because your results offered a benchmark by proxy for taking another stab at Monte Carlo Analysis. With modified settings, hopefully the right ones this time, my new results pretty much confirm yours. To anyone terrified by my earlier comment, I apologize.

The Monte Carlo methodology is the same as described in my earlier comment, so trades are still taken into account without any dependency with the prior or following month. Statistics are derived from 1,000,000 random simulations again.

The Monte Carlo table holds the key statistics:

http://bit.ly/1MLxw7w

The MC CAR chart presents the full data range for the compound annual growth rate:

http://bit.ly/1MLxyw5

The MC Drawdown chart paints MaxDD's distribution:

http://bit.ly/1MLxyw7

The points of interest are the 95% levels mentioned by Varan:

- CAGR for 95% of the cases over 15% (match)

- MaxDD for 95% of the cases under 32% (near match)

The 99% levels are noteworthy too.

## Simple GMR [View instapost]

Albeit the meaning of the "|"-sign is not clear to me, I think you're pinpointing an issue that does bother me.

The 139 trades have a profit spread from $132K - $-91K. However the distribution of the spread is skewed to the right (see chart below), due to the compounding effect as a result of the strategy's profitability combined with continued full capital commitment.

Distribution of returns ($): http://bit.ly/1UPh5Ij

## Simple GMR [View instapost]

However the MC Final Equity is still presentable:

http://bit.ly/1PvVdyU

The posted performance chart ends with equity at $1.413K. As the MC Equity distribution graph shows this is just about at the 50th percentile (see also the posted table with key levels).

## Simple GMR [View instapost]

The flip side: there is a 55% probability that the strategy's maxDD will be 30% or higher.

Since we're talking absolute terms, less is better.

Please do take note of Varan's observation.

## Simple GMR [View instapost]

The Monte Carlo simulation was run for the new composition IJJ, EFA, IEV, EPP, QQQ, EEM, TLT.

First, a backtest starting December 31, 2003 and ending July 31, 2015 is performed. The backtest follows the GMR Simple rules for monthly portfolio reformation, but with trades executed at MOC.

Next, from this original set of 139 trades the Monte Carlo engine randomly picks trades to produce a new, random set of again 139 trades. Some original trades may be skipped and some may be used more than once (permutation with repetition, or random sampling with replacement). This trade randomization process is repeated many times, for the below results even 1,000,000 times.

Finally, during post-process distribution statistics and charts are generated from the data obtained during the randomization process.

The equity curve and statistics from the initial backtest look familiar to the results reported by Varan and others (me too):

http://bit.ly/1Ww83Sg

The outcome of the Monte Carlo Analysis is summarized in a table showing key statistics derived from the distribution of the simulation results:

http://bit.ly/1Ww85to

One of the charts focuses on MaxDD%:

http://bit.ly/1Ww83Si

Subject to further investigation, by imposing a reasonably conservative confidence level of 95% it becomes possible to assess the probability of a specific outcome. With the said confidence level in mind, probability states the strategy's MaxDD% will reside somewhere between -11.71% and -100% as reported by the the table and chart (see red window). The reported MaxDD of -14.40% appears to have only a probability of less than 10%...

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

Suppose the strategy's universe would be adjusted similar to the new composition of Varan's Simple GMR, would the model still be delivering returns outperforming the general market, i.e. SPY?

Preserving GMRE's special flavour as much as possible, I suppose the universe would then become something like: SSO, QQQ, EFA, IEV, EPP, EEM and EDV with SHY still as safe haven.

Portfolio performance:

http://bit.ly/1DteNKT

Yearly return distribution:

http://bit.ly/1DteQpV

Profit Table:

http://bit.ly/1DteO1a

Backtest performed with AmiBroker using total return data obtained from Norgate's Premium Data service.

## Simple GMR [View instapost]

http://bit.ly/1IgkYO9

To add some perspective for the 7 asset basket:

- profit table: http://bit.ly/1f0zhQ5

- allocations: http://bit.ly/1f0ziTX

Results are with total return quotes provided by PremiumData.

@CostBasis: with IWC in the basket too, both CAR and MaxDD drop by about 3%.

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

http://tinyurl.com/nob...

## Simple GMR [View instapost]

http://bit.ly/1IgkYO9

Use the signals in the spirit of varan's above remarks.

Please take note of the slightly different approach: reforms are at the close of each month.

(A post about the real risk involved in these kind of strategies is pending, but most likely won't be published before the summer ahead)

## Simple GMR [View instapost]

http://tinyurl.com/ohp...

The screenshot also shows that May's pick EEM yielded a loss of -4.1%.

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

http://bit.ly/1C7ze8j

(slight differences for the 3 months returns though)

JW

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

## Marc Cohn's GMR (TAA) Strategy Updates And Analyses [View instapost]

http://bit.ly/1F1W71h

The diagram shows how the model picks the Top3 or Top2 assets based on an average momentum weighting along with an absolute momentum filter (hence the occasional Top2). C(r)ash protection is turned off but the model still yields a CAR of 14.80% with a MaxDD: -10.74% (monthly data) over a backtest covering 20 years.