Detailed Results

Below is the chart of each strategy over the month of August 2010, with the composite average of all systems:

And the results in tabular format:

 
Apart from 2 Bollinger Breakout, most systems are showing a good performance for last month.

2010, through the Composite Index

The composite index just about breaks even for the year with this strong August performance:

Appendix: System Details

System Rules and Parameters

All the systems were tested with the same simple position sizing rules of 1% per new trade. No other Money/Risk Management rules were used. No slippage was considered and a $15 RT commission applied. No return on margin is added to the system performance

The system rules are detailed on the Trading Blox online documentation.
The MA Crossover system was used with moving average pairs of 10-20, 20-50 and 50-200 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Bollinger Band system is the classic use of the Bollinger Bands with entries taking place at Breakouts. The parameters used were 20, 50 and 200 days with 2 standard deviations.
The Triple moving Average system was used with moving average triplets of 10-20-50, 20-50-200 and 50-200-800 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Donchian System is a simple version (with no Trade Direction filter) with channel lengths of 20, 50 and 200 days for entries (and 10, 25, 100 for exit). The stops/position sizes are set at 2x, 3x and 5x ATR respectively.

Portfolio Instruments

Covering over 50 instruments across Equities, Interest Rates, Currencies, Agriculturals, Metals and Energies, from around the world, the portfolio contains the following futures (CSI Symbols): AD, BP, CC, C, CD, CFC, CL2, CT, CU, YM, EBL, EBM, EBS, ED, EOX, ESM, FC, FEI, FFI, GC, HG, ICL, IND, IRB, JK2, JP2, JP6, JR2, JRB, JSK, JTI, JY, KC, KPO, KTB, KWR, LC, LGO, LH, MFX, MP, NG2, RA, RS, SB, S, SF, SI, STW, SXE, TRY, US, W, YTC.
Click here for a tabular view with description and exchange information.

Result Normalization

The system performances are adjusted for volatility to normalize the results. See why and how here.

Related posts:

1. the State of Trend Following in July
2. the State of Trend Following in June
3. The State of Trend Following report – Draft V0.2

Please find below all individual results for July 2010:

Notes
<sup>* YTD: Year-To-Date performance.
** AUM: Assets Under Management.
1. Abraham Trading was founded by Salem Abraham, after he was introduced to Managed Futures and Trend Following by Jerry Parker. He is considered as a “second-generation” Turtle.
2. Altis Partners started trading in 2001 and now manage over a $1B with their Altis Global Futures Portfolio. The figures referenced in the performance table are not provided by Altis Partners and no reliance should be taken as to their accuracy, and as a consequence the figures may not be in accordance with any CFTC / NFA performance reporting requirements.
3. The four founders of Aspect (Eugene Lambert, Anthony Todd, Michael Adam and Martin Lueck) were significant members of one of the most succesful funds in managed futures – AHL (Adam, Harding and Lueck).
4. BlueTrend, from BlueCrest Capital, is one of the largest Trend Following funds – headed by Ms. Leda Braga
5. Campbell & Company is one of the oldest Trend Following firms, operating for around 4 decades.
6. Chesapeake Capital was founded by Jerry Parker, a former Turtle.
7. Clarke Capital was founded by Michael Clarke in 1993.
8. Drury Capital, Inc., was founded in Illinois in 1992 by Mr. Bernard Drury.
9. Dunn Capital was founded by Bill Dunn.
10. Eckhardt Trading is the firm managed by William Eckhardt, who co-led the Turtle experiment with Richard Dennis
11. EMC Capital was founded by Liz Cheval, a former Turtle.
12. Hawksbill Capital was founded by Tom Shanks, a former Turtle.
13. Hyman Beck & Co. main principals are Alexander Hyman and Carl Beck.
14. JWH & Co. was founded by John W. Henry, Owner of the Boston Red Sox.
15. Originally ED & F Man. Became a succesful CTA under Larry Hite and went on to form part of The Man Group plc, which subsequently bought AHL to form the Man AHL: the systematic trading division of the Man group.
16. Millburn Ridgefield have been trading Trend Following models since the early 1970’s. As they report performance figures one month later, last month performance is not reported in this report and their YTD, AUM stats are from the month before.
17. Rabar Market Research is the company of Paul Rabar, a former Turtle.
18. Saxon Investment was founded by Howard Seidler, a former Turtle.
19. Superfund founder and CEO: Christian Baha.
20. Transtrend is a Trend follower CTA based in Netherlands
21. Winton Capital is a London-based CTA founded by Dave Harding (also co-founder of AHL).
 
Note that the figures referenced in the performance table are not provided directly by any of the funds/CTAs featured in this report, but are sourced from other publications such as hedge fund/CTA websites.</sup>

These are top of the range CTAs/Managed Futures funds in the Trend Following space.
Most of the traders behind these funds have been involved in the Turtle Trading experiment (2 excellent books on this topic: Complete Turtle Trader – featuring the actual turtle rules and The Way of the Turtle), featured in the legendary books by Jack Schwager: Market Wizards and New Market Wizards, or in Michael Covel’s dedicated Trend Following book.
 
 
 
 

Related posts:

1. Trend Following Wizards – June 2010
2. Trend Following Wizards: negative month (May), 2010 now in the red…
3. Trend Following Wizards – April 2010

Report Changes

As discussed last month, the system performances are still adjusted for volatility to normalize the results.

The portfolio has been subject to a few amendments, mostly a cleansing operation to remove/update irrelevant instruments (eg. not trading any more, no/very low volume). The updated list can be found at the end of the post with system details. These changes did not impact past results much.

Results for July 2010

Below is the chart of each strategy over the month of July 2010, with the composite average of all systems:

And the results in tabular format:

A bit of a better performance from the short-end side of systems but not so from the medium/long-term end of the scale.

2010, through the Composite Index

Below is how the composite index looks so far for 2010 (no rebalancing in the index):

Appendix: System Details

System Rules and Parameters

All the systems were tested with the same simple position sizing rules of 1% per new trade. No other Money/Risk Management rules were used. No slippage was considered and a $15 RT commission applied. No return on margin is added to the system performance

The system rules are detailed on the Trading Blox online documentation.
The MA Crossover system was used with moving average pairs of 10-20, 20-50 and 50-200 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Bollinger Band system is the classic use of the Bollinger Bands with entries taking place at Breakouts. The parameters used were 20, 50 and 200 days with 2 standard deviations.
The Triple moving Average system was used with moving average triplets of 10-20-50, 20-50-200 and 50-200-800 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Donchian System is a simple version (with no Trade Direction filter) with channel lengths of 20, 50 and 200 days for entries (and 10, 25, 100 for exit). The stops/position sizes are set at 2x, 3x and 5x ATR respectively.

Portfolio Instruments

Covering over 50 instruments across Equities, Interest Rates, Currencies, Agriculturals, Metals and Energies, from around the world, the portfolio contains the following futures (CSI Symbols): AD, BP, CC, C, CD, CFC, CL2, CT, CU, YM, EBL, EBM, EBS, ED, EOX, ESM, FC, FEI, FFI, GC, HG, ICL, IND, IRB, JK2, JP2, JP6, JR2, JRB, JSK, JTI, JY, KC, KPO, KTB, KWR, LC, LGO, LH, MFX, MP, NG2, RA, RS, SB, S, SF, SI, STW, SXE, TRY, US, W, YTC.
Click here for a tabular view with description and exchange information.

This month’s amendments were:
- Replaced CL with CL2, DJ with YM.
- Removed ET, LAC, LZC, IRB, JSK, KWR, M10, NGV, SJG

Related posts:

1. the State of Trend Following in August: good performance
2. the State of Trend Following in June
3. The State of Trend Following report – Draft V0.2

Instead of trying to see which funds performed best (as I did previously with the Geometric Information Ratio ranking), I wanted to see what sort of performance would be obtained by diversifying investments across all of the Wizard funds.

Historical Performance Background

To start with, let’s look at the historical individual performance of our wizards, starting from 1990:

Click to zoom in - VAMI for all funds since 1990 or inception, in log scale. Excuse the messy firework look, but try plotting 20 different overlapping time series in one chart...

And some performance statistics – to consider with some “perspective” since the different track record lengths tend to distort stat comparisons (ie. MaxDD increases with track period length, different periods might be more or less conducive to Trend Following, etc.):

 
Notes:
^{a: Number of years contained in the track record (since 1990)
b: Compound Annual Growth Rate
c: Maximum Drawdown level over full length of track record
d: Average Drawdown level over full length of track record
e: Modified Sharpe ratio is as per Jack Schwager’s definition in Managed Futures, Myths and Truths, which introduces interesting performance metrics. The Modified Sharpe ratio is simply a Sharpe ratio where Rf (risk-free rate of return) is set to 0 (makes it independent of leverage). mSR = E[R] / sd.
 
See further below for additional, wizard-specific notes}

 
As expected, most of the highest max drawdown figures can be foud amongst the longest track records (everybody’s worst drawdown is yet to come). Still, not bad showings for some of the oldies (TransTrend, Eckhardt, Millburn).

Diversifying across all Trend Following Wizards: The Index

The next step was simply to build an index to represent a “Fund of Funds” approach encompassing all the Wizards. Below are the chart and the stats for the index. Note that the index is not so realistic as it assumes a monthly rebalancing, which is not practically reproducible with CTA funds. The rebalancing is done on an equal allocation basis, for all funds available at that time (ie 10 funds in 1990 and 20 funds in 2009). This should still gives a good representation of an aggregate performance and should highlight the impact of diversification.

Click to zoom in - VAMI for TF Wizard Index, plotted in bright orange

 
One thing we can notice visually as well as in the stats numbers is that diversification allows the index to rise towards the top of the performance table, be it measured in raw performance, drawdown or volatility/sharpe ratio.

I like to think of diversification as a way of extracting value out of multiple semi-random processes and it seems to have done the trick on that exercise. Eggs, wizards, basket and all that…
 
Note that the index is not intended to represent Trend Following’s performance as a whole since 1990 (because of no accouting for survivorship bias), but rather a measure of the effect of diversification.
 
Additional Notes:
<sup>1. Abraham Trading was founded by Salem Abraham, after he was introduced to Managed Futures and Trend Following by Jerry Parker. He is considered as a “second-generation” Turtle.
2. Altis Partners started trading in 2001 and now manage over a $1B with their Altis Global Futures Portfolio. The figures referenced in the performance table are not provided by Altis Partners and no reliance should be taken as to their accuracy, and as a consequence the figures may not be in accordance with any CFTC / NFA performance reporting requirements
3. BlueTrend, from BlueCrest Capital, is one of the largest Trend Following funds – headed by Ms. Leda Braga
4. Campbell & Company is one of the oldest Trend Following firms, operating for around 4 decades.
5. Chesapeake Capital was founded by Jerry Parker, a former Turtle.
6. Clarke Capital was founded by Michael Clarke in 1993.
7. Drury Capital, Inc., was founded in Illinois in 1992 by Mr. Bernard Drury.
8. Dunn Capital was founded by Bill Dunn.
9. Eckhardt Trading is the firm managed by William Eckhardt, who co-led the Turtle experiment with Richard Dennis
10. EMC Capital was founded by Liz Cheval, a former Turtle.
11. Hawksbill Capital was founded by Tom Shanks, a former Turtle.
12. Hyman Beck & Co. main principals are Alexander Hyman and Carl Beck.
13. JWH & Co. was founded by John W. Henry, Owner of the Boston Red Sox.
14. Originally ED & F Man. Became a succesful CTA under Larry Hite and went on to form part of The Man Group plc, which subsequently bought AHL to form the Man AHL: the systematic trading division of the Man group.
15. Millburn Ridgefield have been trading Trend Following models since the early 1970’s. As they report performance figures one month later, last month performance is not reported in this report and their YTD, AUM stats are from the month before.
16. Rabar Market Research is the company of Paul Rabar, a former Turtle.
17. Saxon Investment was founded by Howard Seidler, a former Turtle.
18. Superfund founder and CEO: Christian Baha.
19. Transtrend is a Trend follower CTA based in Netherlands
20. Winton Capital is a London-based CTA founded by Dave Harding (also co-founder of AHL).
 
Note that the figures referenced in the performance table are not provided directly by any of the funds/CTAs featured in this report, but are sourced from other publications such as hedge fund/CTA websites.</sup>

Related posts:

1. Trend Following Wizards – June 2010
2. Trend Following Wizards: negative month (May), 2010 now in the red…
3. Trend Following Wizards

DB Optimal Yield Index

This idea of optimising roll yield is not a brand new approach, however I have never seen it applied to an active trading strategy.

In fact, I have only seen it applied in the Deutsche Bank Commodity Index (exact name is a mouthful: Deutsche Bank Liquid Commodity Index – Optimum Yield Diversified Excess Return – which I suspect has really only been devised to underlie their ETF fund tracking it).

Deutsche Bank seems to have taken on-board the fact that roll yield represents a non-negligible aspect of futures/commodity investing. From the index/fund website:

The Index is a rules-based index composed of futures contracts on 14 of the most heavily-traded and important physical commodities in the world.

Optimum Yield describes the process by which expiring futures contracts in the Index are replaced with new futures contracts. The Optimum Yield process seeks to pick the futures contract expiring in the next thirteen months that has the highest implied roll yield.

In effect, since the fund is always long, it tries to buy the contract which offers the highest rate of backwardation, or at least the lowest rate of contango.

DB do seem to produce some excess return through that process, as displayed by this comparative chart, taken from their marketing material:

Optimal Roll Yield Trend Following

I wanted to check how a similar concept would perform on an active trading strategy such as a Trend Following system. Typically, in mechanical futures trading, one usually uses the front-month contract — makes it easier to backtest (only one back-adjusted continuous time series to handle) and simpler to trade (only one contract to monitor and trade).

However, in a new optimal roll yield approach, for each trading signal to buy or sell, one could have a theoritical choice to trade any available contracts and their associated maturities. For any given date where a trading signal occurs, one could check the futures contracts yield curve and determine the contract which will optimise the roll yield (highest rate of backwardation, or at least the lowest rate of contango for a BUY signal and the opposite for a SELL signal).

The Methodology: MA Cross-over 50/20 with Optimal Roll Yield

The two components of Trend Following return we are dealing with here are the returns from the spot price beta moves and the roll yields from the futures contracts (TF returns breakdown here).

The idea is to generate the Trend Following signals based off the spot price movements and for each new signal, compute the yield curve to identify the contract which offers the most attractive roll yield (depending on the signal direction). For this example, I picked a very standard cross-over system using 50-day and 20-day MAs.

The process sequence looks like this:

1. Generate the Trend Following strategy signals based off the spot price movements (ie crossovers between the spot price 50-day and 20-day MAs); and for each new signal:
2. Compute the yield curve to identify the contract which offers the most attractive roll yield (depending on the signal direction).
3. Buy/Sell that contract
4. Hold the position until either: 1) the contract expires (roll-over) or 2) the position is reversed (new signal + yield curve computation to pick the best yielding contract)

The lookup for the “best” contract is limited to 12 months in the future.

Note that roll-overs should happen less frequently than with a standard approach (because you might buy a contract maturing in 12 months and hold it for the full 12 months – as opposed to rolling over to the front-month contract every month).

The Results for Crude Oil

Because I coded some of the test algorithm outside of Trading Blox (see p.s. below for more details), I decided to keep it simple to start with, and ran the test on one instrument only, keeping working with Crude Oil (since it instigated this series on roll yield).

As a reference point, the performance of the 20-50 MA cross-over system on front-month contracts (”standard” approach) returned a CAGR of 10.25% with a MaxDD of 46.85% and an annualized Sharpe ratio of 0.37 (no trade costs or slippage included in the test).

OK – enough introduction, here are the comparison results:

The chart shows it pretty clearly and the summary table confirms it:

 
The optimal roll yield approach seems to improve the overall system significantly, whatever metrics you wish to pick for comparison. Pretty pleasing results…

Volume and Slippage Considerations

However, there is an important aspect about trading in the front-month only: liquidity. And with liquidity come better fills and lower slippage — which can greatly impact trading system results.

My initial assumption was that if the optimal yield concept was viable for a large player like DB to run a fund with, I should not worry about liquidity for a similar approach with Trend Following. By checking the actual volume figures for each contract bought/sold with the strategy, I quickly realised that some trades had been made on days with very low volume (ie <50) and "only" 83% of trades on a daily volume over 1,000. Oops, was I just chasing an elusive unicorn? A theoritical result impossible to to apply in practical real-life trading...

Adding a liquidity filter to the roll yield algorithm would allow to reject contracts for which daily volume is too low and avoid liquidity problems. How much would it affect performance, though?

Not too much actually:

The filter is pretty simple: when it computes the yield curve and checks for the contract with the best roll yield, it only considers contract months for which daily volume is over 5,000.

And for completeness, the table summarizing the three tests undertaken:

Note that even with the liquidity filter, slippage might still be a bit better in the front-month contract, as this is where a big chunk of the trading is concentrated. However, real-life testing is the only way to verify and quantify this difference.

To get an idea of how slippage would affect the system performance in general, I ran the standard approach system as a backtest in Trading Blox, with slippage set at a pessimistic 25%. Under these conditions, the system performance (CAGR) dropped “only” by 2.5 percentage points.

Conclusion

One of my main concerns regarding this strategy was the potential loss in “raw price moves” (ie the fact that price trends would not propagate as well in alternative contract months), but the strong correlation between the standard and optimized approach seems to indicate that improved roll yield return does not come at the cost of beta spot price moves return, therefore providing a direct bonus.

It is quite evident that liquidity can become an issue and that a liquidity filter should be employed at a minimum. Moreover Crude Oil, used for this example, is one of the largest traded physical commodity. Other products might not offer enough liquidity depth, far in the yield curve. DB, however, can implement its optimal yield approach over 14 different instruments, which indicates that there is scope for this approach to be employed on additional products to Crude Oil. I believe such approach could have its place in a fully diversified Trend Following system – but only applied to the most liquid instruments.

Finally the optimal approach might generate some additional slippage compared to the traditional approach. However, this extra slippage cost should still be outweighed by the extra roll yield return, as evidenced in the Trading Blox slippage impact test.

Epilogue: Techie’s corner

In terms of techical implementation, this is slightly more complicated than standard back-testing because each instrument must use multiple price streams (for each individual contract) and cannot be handled by standard back-testing packages (that I know of, or without heavy customisation).

To avoid re-developing a back-testing package from scratch, I used my trusted copy of Trading Blox to generate the “standard/non-optimal roll yield” MA cross-over system output for a single instrument (Crude Oil), which output several files providing the dates of the signals as well as other useful computations such as position sizing with number of contracts and running Total Equity values. Using this information, I ran a second pass of processing, by reading the signals and other info generated by Trading Blox, and looping through the individual contract data in order to pick, for each entry signal, the best contract on the yield curve (this second part was coded outside of Trading Blox).

 
 
*Note: the Modified Sharpe ratio is as per Jack Schwager’s definition in Managed Futures, Myths and Truths, which introduces interesting performance metrics. The Modified Sharpe ratio is simply a Sharpe ratio where Rf (risk-free rate of return) is set to 0 (makes it independent of leverage). mSR = E[R] / sd.

Related posts:

1. Roll Yield and Commodity Yield Curve
2. Crude Oil, Contango and Roll Yield for Commodity Trading
3. Trend Following returns breakdown

The average monthly return is 0.29%, taking the average YTD figure to -1.48%, with a few Wizards being in the double-digit figures on both ends of the scale.

Note that three major players were added to the report this month: Altis Partners, BlueTrend and Campbell & Company.

Please find below all individual results for June 2010:

Notes
<sup>* YTD: Year-To-Date performance.
** AUM: Assets Under Management.
 
1. Abraham Trading was founded by Salem Abraham, after he was introduced to Managed Futures and Trend Following by Jerry Parker. He is considered as a “second-generation” Turtle.
2. Altis Partners started trading in 2001 and now manage over a $1B with their Altis Global Futures Portfolio. The figures referenced in the performance table are not provided by Altis Partners and no reliance should be taken as to their accuracy, and as a consequence the figures may not be in accordance with any CFTC / NFA performance reporting requirements
3. The four founders of Aspect (Eugene Lambert, Anthony Todd, Michael Adam and Martin Lueck) were significant members of one of the most succesful funds in managed futures – AHL (Adam, Harding and Lueck).
4. BlueTrend, from BlueCrest Capital, is one of the largest Trend Following funds – headed by Ms. Leda Braga
5. Campbell & Company is one of the oldest Trend Following firms, operating for around 4 decades.
6. Chesapeake Capital was founded by Jerry Parker, a former Turtle.
7. Clarke Capital was founded by Michael Clarke in 1993.
8. Drury Capital, Inc., was founded in Illinois in 1992 by Mr. Bernard Drury.
9. Dunn Capital was founded by Bill Dunn.
10. Eckhardt Trading is the firm managed by William Eckhardt, who co-led the Turtle experiment with Richard Dennis
11. EMC Capital was founded by Liz Cheval, a former Turtle.
12. Hawksbill Capital was founded by Tom Shanks, a former Turtle.
13. Hyman Beck & Co. main principals are Alexander Hyman and Carl Beck.
14. JWH & Co. was founded by John W. Henry, Owner of the Boston Red Sox.
15. Originally ED & F Man. Became a succesful CTA under Larry Hite and went on to form part of The Man Group plc, which subsequently bought AHL to form the Man AHL: the systematic trading division of the Man group.
16. Millburn Ridgefield have been trading Trend Following models since the early 1970’s. As they report performance figures one month later, last month performance is not reported in this report and their YTD, AUM stats are from the month before.
17. Rabar Market Research is the company of Paul Rabar, a former Turtle.
18. Saxon Investment was founded by Howard Seidler, a former Turtle.
19. Superfund founder and CEO: Christian Baha.
20. Transtrend is a Trend follower CTA based in Netherlands
21. Winton Capital is a London-based CTA founded by Dave Harding (also co-founder of AHL).
 
Note that the figures referenced in the performance table are not provided directly by any of the funds/CTAs featured in this report, but are sourced from other publications such as hedge fund/CTA websites.</sup>

These are top of the range CTAs/Managed Futures funds in the Trend Following space.
Most of the traders behind these funds have been involved in the Turtle Trading experiment (2 excellent books on this topic: Complete Turtle Trader – featuring the actual turtle rules and The Way of the Turtle), featured in the legendary books by Jack Schwager: Market Wizards and New Market Wizards, or in Michael Covel’s dedicated Trend Following book.
 
 
 
 

Related posts:

1. Trend Following Wizards – July 2010 (RED)
2. Trend Following Wizards: negative month (May), 2010 now in the red…
3. Trend Following Wizards – April 2010

With some delay, here is the July update of the State of Trend Following report. With quite a few changes this month.

Most systems use ATR-based stops/position sizing and most used to be set at 5x ATR – regardless of the timeframe of the signal (ie. 20 to 200 days) – which was not making the most sense. So, ATR multiples are now adjusted in line with the length of the trading signals (for example 2x ATR for Donchian-20, 3x ATR for Donchian-50, etc. – details of the system settings are detailed at the end of the post).

Additionally, I find that a fair comparison should normalize the results between the different systems. The MAR ratio attempts to do this by dividing CAGR by Max Drawdown; and in a similar manner I decided to “volatility-adjust” the performance of the systems in the report.

Normalizing results

A typical adjustment is the so-called risk-adjusted return (or rather volatility-adjusted return), which usually “baselines” the Max drawdown of the systems to a common value, and derive the resulting performance for each system.

Below is a table showing each system performance numbers from 1990 to 2009. Two extra columns have been added to show the “normalized return” and the multiplier coefficient to obtain this return (the multiplier coefficient is itself calculated by dividing an arbitrary Max Drawdown figure of 50% by the actual system Max DD).

 
This should remove any dependency on leverage, position size and risk per trade.

Another change resulting from this normalization is the exclusion of T-bill interest. I initially included it to make the tests more realistic and “reward” systems with a lower use of margin. But this return on margin cannot be leveraged up, so it makes no sense to keep including it when multiplying/normalizing results.

2010 for the Normalized Systems

Below is how the updated systems performed so far in 2010:

And a zoom on the month of June:

And the table containing the final value of each system (rebased at 100 at the beginning of each period) to help read the charts:

No clear global trends stands out in 2010, with the performance of all systems ranging from -15% to +20% so far. This is still very similar with the Trend Following Wizard performances..

June looks like an early peak followed by a dip for most systems. Most volatile is the medium-term Moving Average crossover, which sets both extremes before closing the month even.

Our two star performers from May (short-term Donchian and Bollinger systems) follow along in the peak at the beginning of the month, but their performances diverge at the end of the month, with the Donchian system collapsing to end the month near a 15% loss, whereas the BBO system completes the month even.

Meanwhile the composite index closes the month (and its YTD) at around -4%.

Appendix: System Details

System Rules and Parameters

All the systems were tested with the same simple position sizing rules of 1% per new trade. No other Money/Risk Management rules were used. No slippage was considered and a $15 RT commission applied. No return on margin is added to the system performance

The system rules are detailed on the Trading Blox online documentation.
The MA Crossover system was used with moving average pairs of 10-20, 20-50 and 50-200 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Bollinger Band system is the classic use of the Bollinger Bands with entries taking place at Breakouts. The parameters used were 20, 50 and 200 days with 2 standard deviations.
The Triple moving Average system was used with moving average triplets of 10-20-50, 20-50-200 and 50-200-800 days. The stops/position sizes are set at 2x, 3x and 5x ATR respectively.
The Donchian System is a simple version (with no Trade Direction filter) with channel lengths of 20, 50 and 200 days for entries (and 10, 25, 100 for exit). The stops/position sizes are set at 2x, 3x and 5x ATR respectively.

Portfolio Instruments

Covering 60 instruments across Equities, Interest Rates, Currencies, Agriculturals, Metals and Energies, from around the world, the portfolio contains the following futures (CSI Symbols): AD, BP, CC, C, CD, CFC, CL, CT, CU, DJ, EBL, EBM, EBS, ED, EOX, ESM, ET, FC, FEI, FFI, GC, HG, ICL, IND, IRB, JK2, JP2, JP6, JR2, JRB, JSK, JTI, JY, KC, KPO, KTB, KWR, LAC, LC, LGO, LH, LZC, M10, MFX, MP, NG2, NGV, RA, RS, SB, S, SF, SI, SJG, STW, SXE, TRY, US, W, YTC.
Click here for a tabular view with description and exchange information.

Related posts:

1. the State of Trend Following in July
2. the State of Trend Following in August: good performance
3. The State of Trend Following report – Draft V0.2

Following the previous post on backwardation, contango and crude oil, let’s look at how several factors can play a part in the overall trend following trading system performance.

I have previously referenced the study by EDHEC Risk, which shows the different sources of return of a Trend Following strategy.

The authors build the Mt. Lucas Management (MLM) index, which applies a 200-day moving average Trend Following strategy to a diversified range of 25 futures markets, rebalanced monthly. The performance of the index is separated in three periods and further broken down by the three individual sources of return identified by the authors:

1. T-Bill returns on marginable assets
2. Static returns from trend-following futures markets
3. Rebalancing gains

The chart below shows the representation of each component’s participation to the overall return:

You might find it surprising how relatively small the impact of the actual Trend Following strategy return is (I know I did…)

Further breakdown

The previous post highlighted that the performance of any Crude Oil futures contract was unable to match the performance of the spot price (despite a fairly high beta of 0.83). This is mostly because holding commodities via futures contracts expose you to the spot price beta move (as mostly anticipated) but also to the yield curve of that futures contract.

Over time, the impact of the backwardation or contango in the yield curve (roll yield) can create a large drift between the actual spot price performance and the equivalent futures contract.

This is very apparent in a simple “buy-and-hold” example (as in the Crude Oil case) but the same would apply to any trading strategy using futures instruments. Any futures trade could be broken down into two “components”:

• spot price beta move
• roll yield

And therefore the sum of all the trades resulting from a trading strategy (such as Trend Following) could fall into these two same categories, giving us now four overall components.

1. T-Bill returns on marginable assets
2. Returns from trend-following spot price beta moves
3. Roll yield
4. Rebalancing gains

Roll Yield: a misnomer?

Note that Roll yield is not aptly named; it implies that the act of rolling contracts yield a specific return. However, the return resulting from contango or backwardation is merely “crystallized” at the time of rolling contracts: the contango premium (backwardation discount) actually deflates over time gradually.

For example, think of buying a Crude Oil contract at the beginning of the month with a premium over spot of $1 per barrel (at $76). If the spot price does not change during the whole month, the price of the future contract simply converges to the spot price ($75) – the premium simply deflates. When selling the contract at the end of the month, you will have lost $1 without doing a contract roll and with the price having stayed stable.
 
 
Breaking down the actual components of a global strategy might be helpful in order to understanding the sources of returns and focus on a specific one to possibly improve it and enhance the overall system. For example, a more sophisticated approach to rolling contracts might enhance the overall roll yield produced by the trading strategy.

Related posts:

1. Crude Oil, Contango and Roll Yield for Commodity Trading
2. Better Trend Following via improved Roll Yield
3. Roll Yield and Commodity Yield Curve

I have recently been browsing the great resource that is the SSRN (Social Science Research Network), looking for some Managed Futures/CTA/Trend Following papers.

Here is a list of some I have bookmarked:

Is Managed Futures an Asset Class? The Search for the Beta of Commodity Futures
Abstract:
We hypothesize that the classic arbitrage pricing theory contains circular logic, and as a consequence, its natural state is disequilibrium, not equilibrium. We extend this hypothesis to suggest that the term structure of the futures price curve, while indicative of a potential roll return benefit, in fact implies a complex series of roll yield permutations. Similarly, the hedging response function elicits a behavioral risk management mechanisms, and therefore, corroborates social reflexivity. Such models are inter-related and each reflects certain qualities and dynamics within the overall futures market paradigm.

With respect to managed futures, it is an observable materialization of behavioral finance, where risk, return, leverage and skill operate un-tethered from the anchor of an accurate representation of beta. In other words, it defies rational expectations equilibrium, the efficient market hypothesis and allied models – the CAPM, arbitrage pricing theory or otherwise – to single-handedly isolate a persistent source of return without that source eventually slipping away.

CTA/Managed Futures Strategy Benchmarks: Performance and Review
Abstract:
In this paper we provide: 1) a brief synopsis of the benefits of managed futures investment; 2) a short review of manager based CTA benchmark construction; and 3) an empirical analysis on the relative performance of various CTA benchmarks (non-investible manager based indices, investible manager based indices, and passive security based indices). In this analysis the various CTA indices are also compared on a zero risk (e.g., Treasury Bill), total risk (Sharpe Ratio), market factor risk (e.g., S&P 500) and strategy risk (e.g., passive futures based CTA index) and peer group basis (investible and noninvestible manager based indices). Lastly, for a selected set of CTAs with full data over the period of analysis an example of the use of various CTA benchmarks in determining excess peer group return, and zero risk, total risk, market risk or strategy (futures based) risk excess return is provided.

Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors
Abstract:
nvestors face significant barriers in evaluating the performance of hedge funds and commodity trading advisors (CTAs). The only available performance data comes from voluntary reporting to private companies. Funds have incentives to strategically report to these companies, causing these data sets to be severely biased. And, because hedge funds use nonlinear, state-dependent, leveraged strategies, it has proven difficult to determine whether they add value relative to benchmarks. We focus on commodity trading advisors, a subset of hedge funds, and show that during the period 1994-2007 CTA excess returns to investors (i.e., net of fees) averaged 85 basis points per annum over US T-bills, which is insignificantly different from zero. We estimate that CTAs on average earned gross excess returns (i.e., before fees) of 5.4%, which implies that funds captured most of their performance through charging fees. Yet, even before fees we find that CTAs display no alpha relative to simple futures strategies that are in the public domain. We argue that CTAs appear to persist as an asset class despite their poor performance, because they face no market discipline based on credible information. Our evidence suggests that investors’ experience of poor performance is not common knowledge.

Performance Persistence for Managed Futures
Abstract:
Past literature on managed futures funds has found little evidence that the top performing funds can be predicted. But, the past literature has used small datasets and methods which had little power to reject the null hypothesis of no performance persistence. The objective of this research is to: determine whether performance persists for managed futures advisors using large datasets and methods which have power to reject the null hypothesis.
We use data from public funds, private funds, and commodity trading advisors (CTAs). The analysis proceeds in four steps. First, a regression approach is used to determine whether after adjusting for changes in overall returns and differences in leverage that funds all have the same mean returns. Second, we use Monte Carlo methods to demonstrate that Elton, Gruber, and Rentzler’s methods have little power to reject false null hypotheses and will reject true null hypotheses too often. Third, we conduct an out-of-sample test of various methods of selecting the top funds. Fourth, since we do find some performance persistence, we seek to explain the sources of this performance persistence by using regressions of (a) returns against CTA characteristics, (b) return risk against CTA characteristics, (c) returns against lagged returns, and (d) changes in investment against lagged returns.
Performance persistence could exist due to either differences in cost or differences in the skill of the manager. Our results favor skill as the explanation since returns were positively correlated with cost. The performance persistence is statistically significant, but is small relative to the variation in the data (only 2-4% of the total variation). But, the performance persistence is large relative to the mean. Monte Carlo methods showed that the methods used in past research could often not reject false null hypotheses and would reject true null hypotheses too often.
Out-of-sample tests confirmed that there is some performance persistence, but it is small relative to the noise in the data. A return/risk measure showed more persistence than either of the return measures. Picking CTAs based on returns in the most recent year may even be worse than a strategy of randomly picking a CTA.

Managed Futures and Hedge Funds: A Match Made in Heaven
Abstract:
In this paper we study the possible role of managed futures in portfolios of stocks, bonds and hedge funds. We find that allocating to managed futures allow investors to achieve a very substantial degree of overall risk reduction at limited costs. Apart from their lower expected return, managed futures appear to be more effective diversifiers than hedge funds. Adding managed futures to a portfolio of stocks and bonds will reduce that portfolio’s standard deviation more and quicker than hedge funds will, and without the undesirable side-effects on skewness and kurtosis. Overall portfolio standard deviation can be reduced further by combining both hedge funds and managed futures with stocks and bonds. As long as at least 45-50% of the alternatives allocation is allocated to managed futures, this again will not have any negative side-effects on skewness and kurtosis.

A Quantitative Analysis of CTA Funds
Abstract:
Our research studies various properties of commodity trading advisors (CTAs) from a quantitative point of view. Our investigation is based on a commercial database of 549 funds and focuses on the period 1990 to present.

Firstly, CTAs’ return distributions are analyzed and strong evidence of non-normality is found, stressing the need for portfolio allocation techniques which take into account higher-order moments.

Secondly, relative persistence in return distribution parameters is studied. We find strong persistence for volatility, but fail to find significant persistence for average return or higher order moments.

Thirdly, we review the major benchmarks available to the industry and build new benchmarks from our dataset. This allows us to infer the magnitude of various biases. We study homogeneity of 2 CTA subsets, namely trend-followers and non-trend-followers, and study the diversification possibilities in a CTA portfolio.

In the second part of the study, we focus on linking CTAs returns with that of traditional assets. After showing that a buy and hold multi-factor linear model fails to explain CTAs returns, we point out the presence of option-like payoffs in CTAs return patterns. Trend-following CTAs exhibit straddle-like payoffs, while non-trend-followers’ return patterns that are reminiscient of a long call option.

Lastly, using simple trading algorithms based on moving averages, we propose a linear market model in which factors capture the dynamic nature of CTA managers’ strategies. Our model leads to significant improvements over the classical model. Notably, we show that our model is able to closely replicate a broad index of CTAs for long out-of-sample periods.
 
 
Have a great weekend!
 
 
^{Picture credits: rugosa rosa@flickr}

Related posts:

1. Are you reading the essential books?
2. Mammoth Hedge Fund moves into Trend Following

This contrasts with the +35% result found in some of the strategies in the State of Trend Following Report. If the assumption that CTA performances and simple Trend Following strategies (which compose the State of TF report) holds true, one might want to extrapolate and speculate on what type of strategies/timeframe CTAs run.

As a side note, if you have any suggestions of large recognized Trend Following CTAs/Funds to be included in this report, please let me know in the comments. Funds currently on my list of possible additions (provided they publish the information easily accessible): BlueTrend, Graham, Campbell.

Here are the detailed results for May 2010:

Notes
<sup>* YTD: Year-To-Date performance.
** AUM: Assets Under Management.
1. Abraham Trading was founded by Salem Abraham, after he was introduced to Managed Futures and Trend Following by Jerry Parker. He is considered as a “second-generation” Turtle.
2. The four founders of Aspect (Eugene Lambert, Anthony Todd, Michael Adam and Martin Lueck) were significant members of one of the most succesful funds in managed futures – AHL (Adam, Harding and Lueck)
3. Chesapeake Capital was founded by Jerry Parker, a former Turtle.
4. Clarke Capital was founded by Michael Clarke in 1993.
5. Drury Capital, Inc., was founded in Illinois in 1992 by Mr. Bernard Drury.
6. Dunn Capital was founded by Bill Dunn.
7. Eckhardt Trading is the firm managed by William Eckhardt, who co-led the Turtle experiment with Richard Dennis
8. EMC Capital was founded by Liz Cheval, a former Turtle.
9. Hawksbill Capital was founded by Tom Shanks, a former Turtle.
10. Hyman Beck & Co. main principals are Alexander Hyman and Carl Beck.
11. JWH & Co. was founded by John W. Henry, Owner of the Boston Red Sox.
12. Originally ED & F Man. Became a succesful CTA under Larry Hite and went on to form part of The Man Group plc, which subsequently bought AHL to form the Man AHL: the systematic trading division of the Man group.
13. Millburn Ridgefield have been trading Trend Following models since the early 1970’s. As they report performance figures one month later, last month performance is not reported in this report and their YTD, AUM stats are from the month before.
14. Rabar Market Research is the company of Paul Rabar, a former Turtle.
15. Saxon Investment was founded by Howard Seidler, a former Turtle.
16. Superfund founder and CEO: Christian Baha.
17. Transtrend is a Trend follower CTA based in Netherlands
18. Winton Capital is a London-based CTA founded by Dave Harding (also co-founder of AHL).</sup>

These are top of the range CTAs/Managed Futures funds in the Trend Following space.
Most of the traders behind these funds have been involved in the Turtle Trading experiment (2 excellent books on this topic: Complete Turtle Trader – featuring the actual turtle rules and The Way of the Turtle), featured in the legendary books by Jack Schwager: Market Wizards and New Market Wizards, or in Michael Covel’s dedicated Trend Following book.
 
 
 
 

Related posts:

1. Trend Following Wizards – June 2010
2. Trend Following Wizards – July 2010 (RED)
3. Trend Following Wizards – January 2010