Diversified Trend Trading Approach

Diversified Systematic Trend Following - The Quiet Achievers



This post looks at the long term performance metrics of alternative investments in the category of diversified systematic trend following managers.

Unlike a classic long term buy and hold investment approach typically deployed by most passive Fund Managers that attempt to match overall market performance returns (Beta), this class of active Fund Manager specialise in 'following the trend' by utilising a diversified portfolio with the intent of outperforming the market return (Alpha).

This diversification is achieved through a combination of techniques including system diversification, market diversification and time-frame diversification.

The intent of diversification is to reduce the volatility of returns generated by a chosen individual system component allowing for a less volatile risk-return profile that can otherwise be achieved. For example an investment in any single instrument generates it's own distinctive equity curve over the course of the investment period. A combination of investments in an investment portrfolio also generates a distinctive equity curve that comprise the summation of each individual contributing equity curve.

The aim of the game with portfolio management is to achieve an optimal risk-return profile over the investment period.

As we all know there is a continuous trade-off between risk and return. In general a higher return comes with higher risk....but the skilled trader and portfolio manager has a few tricks up their sleeve to deal with this dillema. What stops the typical investor from leveraging up their performance returns to achieve heady results is the classic trade-off between reward and risk. Uncertainty hides within every investment decision in our complex markets. The main weapon used to address the inherent volatility of returns present in any investment is a sound appreciation of correlation and what this means in an investment portfolio.

Correlation is a statistical measure of how two or more investments move in relation to each other. If instruments in the portfolio are highly correlated, they tend to move in a similar manner. When one goes up, the other correlated instrument goes up and vice versa. Negatively correlated investments on the other hand are diametrically opposed. When one goes up, the other goes down and vice versa. What a portfolio manager is after however is a portfolio comprising instruments of zero correlation with each other.

Portfolios comprising postively or negatively correlated instruments tend to accentuate performance returns in good times and lousy returns during bad times. In boom times such as asset bubbles, the equity curve of a portfolio of +1/-1 correlated instruments can offer extreme performance returns that are unsustainable and it is during these times that even unskilled investors/traders are treated graciously by the market....however the contrary is also true when hard times hit and the bubbles burst......For example a global crisis will see a sea of red in the watch list which will then be echoed through the trading floors. This is because the correlated nature of markets typically only reveals itself when the tide retreats.

The reasons for correlated market movements are numerous but in general, a correlation is created when a dependency is generated between individual investments. For example, when the market experiences a shock and investors scarper for the exits it is not simply a single investment that suffers. The investor/trader might have investments in other assets that require liquidation to extract themselves from that single position and the possibility of a margin call....as a result....like a set of domino's, the impacts spread from investment to investment across the global market place.

Another example might be the internal dependencies that exist between investments such as listed equity instruments. For example an ASX listed contractor providing services to an ASX listed mining company would be correlated to the performance of the major client. A hit to the ASX Listed mining company would be translated to a hit to the supplier and a compounded hit to the index.

It is important to note that correlation changes over time. What has been evident particularly since 2008 is the tendency for more investments to become correlated in part attributed to rising intra and inter-market dependencies that may be attributed in part to central bank intervention.

The key to successful diversification is to reduce the internal correlation that exists within a portfolio to as close to zero correlation as possible. While more and more asset classes are becoming more correlated, portfolio managers can now turn to different approaches such as system diversification to specifically address this tendency. In reducing a portfolio's correlation, what is being achieved is the ironing out of the volatile return distributions of the portfolio to a more linear consistent profile. In doing so we smooth the portfolio equity curve by adding new investment contributions that specifically address the volatility weaknesses of each individual contributor to allow the investor to sleep at night and avoid ulcers. More importantly, a portfolio equity curve with a smooth linear equity curve allows you to leverage your position and magnify returns within managed risk constraints.

Diversification is often called the 'only free lunch'....and for good reason. It is a critical component of risk management.

Anyway....back to our review of diversified systematic trend following managers. Does it work and what sort of long-term investment performance are we talking about?

For those that might profess that 'markets are efficient' and that no performance edge can be achieved to outperform beta aside from random luck....the following might fall on deaf ears.....however for those investors out there who prefer to use quantifiable measures as opposed to gut instinct, you could for example refer to the actual long-term audited performance returns of some of the Commodity Trading Advisers (CTA's)....and be pleasantly surprised......and no....this does not include the Warren Buffet story.

We don't hear much about these guys....namely because they fly in the face of investment logic and recognise that the human brain has it's floors and actually contributes to making markets inefficient through irrational human behaviour.

It is during market crisis periods when rational investment behaviour is thrown out with the bath water and mass hysteria (of fear or greed) contributes to collective behaviour that drives the fat tails of market uncertainty. Most of the time markets are generally efficient....however some of the time they simply aren't. It is during these periods that trend following shines.

The guys we are talking about are always ready to pounce on these times as they span across asset classes using diversification as their primary weapon to simply be there at the right time when pandemonium strikes, have fully automated systems that are ready to pounce 24/7, simply turn off the market news feeds as they instantly react to current price as opposed to predicting future price, and apply algorithms with very simple trading rules to reap the rewards of irrational behaviour.........again and again and again...to dramatic effect.

To highlight their performance lets look at the market return of an investment portfolio that mirrors the performance of the S&P500 TR Index (total return including dividends). This would be a familiar benchmark for the passive Fund Manager in the buy and hold fraternity that seeks to offer Beta. By the way.....the passive Fund Manager rarely achieves or outperforms beta as you would see in industry SPIVA scorecard results (S&P Indices versus Active managers)....but I digress.......another day perhaps.

Chart 1: S&P500 TR Index - January 2002 to September 2016


What does the equity curve of the chart above say? Well in terms of performance, an investment portfolio that mirrored the performance of the S&P 500 Index, would possess similar risk-return characteristics as the overall index (assuming that investment returns were all reinvested). This means that over the course of the 14 year period, your initial investment of $1,000 would have achieved a compound annual growth of 6.33% PA and now be worth $2,500, but......and here is the but.......... to enjoy that return, you would have had to endure a nasty drawdown of 30% in 2003 and a gut-wrenching drawdown of 50.95% in 2008. More important however is the question to you of whether you would have had the nerve to ride out a 50% decline in the value of your investment without scarpering for the exits.

Is is easy to look back in hindsight to a past market crisis with a brave voice and say, I could have handled that....but a different kettle of fish to be in the middle of a wicked drawdown and wondering when the pain will cease or if your investment will ever recover in value. You should all try it some time. What doesn't kill you makes you stronger.

For those with poor memories or are new to the game, you may be congratulating yourselves on the strong performance of the equity markets since 2009, however as mentioned previously, everyone becomes an investment expert during bull markets. In the great bull run at the end of the last century, a long term buy and hold investor could not put a foot wrong.....until of course they did.

However...for those revelling in the current market madness of this equity bubble driven by a glut of printed money by the central banks, just ask yourself this....... how long is this bull run going to continue?......tomorrow?, next week?, next month?, next year?....let's leave that to the CNN expert commentary shall we as many investment gurus of the news networks have the incredible ability to actually read the future?

The more prudent investor however who has stood the test of time and possesses a longer memory might not be so bullish as the average pundit and may prefer to actually trade/invest in 'what is' as opposed to 'what may be'. Some of these wise investors have already been through the school of hard knocks and have let history teach them a lesson.

In other words they simply follow the trend and don't try to predict where price will be tomorrow....... They simply ride the current price and cut losses short. In a nutshell, that is the secret sauce of trend following. Simply be patient, manage risk, be prepared to pull that trigger when the price comes to you and protect your capital at all costs......and simple human behaviour will deliver you long term returns. History has demonstrated this again and again for time immemorial. It's as simple as that. These simple heuristics of trend following are sufficient to cut through the noise of the complex financial markets.

Now have a look at the scaled CAGR of 4.97% in Chart 1. This metric is used as a comparable measure to rate different investments in terms of risk-reward. We will be using this shortly to compare the performance of the S&P500 TR Index against our CTA's.

Don't worry about the other statistics such as skew, kurtosis and annualised sharpe....that's just stats guff for geeks like me.

So without more further ado, let's put the facts on the table about these quiet achievers of the investment industry that call themselves diversified systematic trand traders.

Here are the performance metrics of 47 active fund managers that are classed (in general) as diversified systematic trend traders.

Table 1: CTA Performance Metrics - January 2002 to September 2016


Put the glasses on and spend some time becoming familiar with the table. The important columns to focus on are CAGR and the Adjusted CAGR ( a risk weighted return measure). You will see that I have ranked these performance metrics across the chosen Fund Managers of Table 1.

Note that at the bottom of the Table, I have also included the performance metrics of the S&P500 TR Index, Fidelity Spartan 500 ETF (that mirrors the performance of the S&P500) and also the Trend Following Index (TF Index) which is an index compiled from the 47 fund managers contained in this performance assessment.

I have also included a column titled "TF Index Correl". This column highlights how correlated each Fund is to the TF Index. Those in green are strongly correlated to each other. In other words, their performance metrics are similar and reflect the performance metrics of a typical systematic diversified trend trading Fund Manager (FM). What this means is that despite what you may hear, there is not much secret sauce in the strategies employed by these FM's. They simply focus on simple proven recipes that work.

Also note how the performance of these trend following FM's correlate with the S&P 500 Index (refer to the column S&P 500 Correl). The very low correlations with the S&P500 Index reflect that these FM's aggresssively employ diversification techniques to ensure the overall performance of the portfolio is as uncorrelated with the equity market as possible. This alternative investment class is very attractive to the Portfolio Manager given their very low correlation to equities and as a result are often used by the PM to assist in flattenning the return distributions of portfolios that are concentrated in equities.

What a low correlation to equities means is that when the equity market collapses, these FM's shine. This feature in industry talk terms is referred to as the 'CTA smile' and is a classic symptom that emerges during market crisis. It is usually the CTA's that are sitting on the other side of your trade exits when the market death knell sounds and investors flurry to the exits.

Let's have a look at the equity curve of the TF Index (that represents the overall performance of this entire group). Refer to 2008 when the GFC was in full swing. Look at how the TF Index performed during this period. Guess who were the beneficiaries of this market crisis.

Chart 2: Trend Following Index (TF Index) - January 2002 to September 2016


Now have a closer look at the chart above to see what it says in performance terms. That's right, the scaled CAGR is 3.7 x higher than the S&P500 TR Index. What this says is that over the period concerned from 2002 to 2016, diversified portfolio trend following has significantly outperformed Beta. Your initial investment of $1000 in the TF Index would now we worth $3,500.....and according to the investment guru's, trend following is dead....LOL.

Perhaps we should be critical thinkers and not necessarily take heed of what the 'experts' say. Do you see anything in the performance metrics above that suggests that trend following is dead? No matter how you cut it, the equity curve clearly demonstrates the power of this very under-rated approach to investment.
Let's did a little deeper into the statistics and see who are the best performers in this group as these guys deserve a special mention.

Award for the best raw CAGR - Dreiss Research Corporation: Diversified Program.


OMG.......have a look at that stellar performance above. Look how it dwarves the result of the S&P500 TR Index and also the TF Index equity curves. Is this random luck? Well if you consider random luck to apply to a performance history of over 14 years.....then we probably have a different idea of what 'random luck' means.

An investment of $1,000 in January 2002 fully invested over the period would now be worth $12,000. Imagine you were a time traveller.....where would you now be parking some of your hard earned investment capital?

Bill Dreiss from Driess research has applied a similar trend following trading recipe to his performance with little change over the entire period from fund inception. While some may have concern over the volatility of returns delivered....Bill and his investors don't. I tried to catch up with Bill, but he was on his morning surf.



Award for the best Adjusted CAGR - Fort LP: Global Diversified Program.



It truly doesn't get much better than this. Fort has nailed it and has demonstrated a superb track record since 2002. For the Funds that I track in this alternative investment class, Fort currently offers the greatest risk weighted return of them all. Look how it dwarves the performance of the TF Index and the S&P500 TR Index. Also look at the drawdown history.........impressive.

So what does this post demonstrate? In searching for strong risk weighted investment returns it is worthwhile doing your homework and not accepting the position of the status quo in the investment industry.

Cheers C

Don't be Fooled by Market Randomness



The central conundrum to every trader is the question.....are the markets random or not?

The reality however is that the market is a random walk much of the time punctuated with periods of non-random directed price behaviour. The market exhibits behaviour characterised by fat tails. What this means in statistical terms is that markets carry more risk than what normal distributions characterised by random Brownian motion imply.

Real world events induce abrupt change to otherwise well-behaved markets creating extreme variation within these otherwise random conditions leading to a significant proportion of overall price variance. In mathematical terms market movements are therefore better characterised by the term Levy Flight, which is a class of random walk, in which step lengths have a probability distribution that is heavy tailed.....anyway enough with the jargon.

Market randomness in this context relates to uncertainty arising from a lack of perfect information. The same randomness exists within the motion of the water molecules of a cloud (a complex system). With access to perfect information about the actions taken by all market participants, then it theoretically would be possible to predict the exact trajectory of price from one given moment in time to another .......however for practical purposes, the reality is that it is simply insurmountable (intractable) to have access to perfect information. This therefore has implications on the legitimacy of the claims associated with the Efficient Market Hypothesis. The best that we can hope for in complex systems is to predict the overall direction of price by looking at the collective action of all its participants (over a large sample set) to gauge 'gross statistical measures' of market movements.

The reality appears to be this....... that market participants (humans and their associated creations [algorithms]) make 'subjective' decisions based on partial information that is available to them, combined with a swathe of other reasons (rational or irrational), that are specific to the participant. The participant interacts with the market (the system) at only two times.......the entry and the exit. At these two points of time, the reason for this interaction are effectively transferred into the price of the instrument. We can therefore say that the market now contains all the information pertinent to that particular participant's behaviour. At each interaction in time (where a buyer transacts with a seller) this information is progressively transferred to the market.

With this interpretation we can therefore say that the market, at all points in time, fully reflect all the information available to the market. As a result those that agree with this statement would necessarily conclude that the ability to predict future price is futile as there is no further available information not already accessed by the market. This is a very powerful argument for those that believe in the Efficient Market Hypothesis......however it is not the full picture.

This interpretation fails to include the lurking non-random feature of a market referred to as autocorrelation which results in non-random directional price moves (or Levy Flight).

Autocorrelation (also known as serial correlation) is the correlation of a signal at a particular point in time with a previous signal in a past time. This feature is what gives rise to long term market direction attributed to underlying fundamental/economic push factors which reveal themselves only over a large data sample set, or in periods of crisis, where the plethora of reasons for why a participant interacts with the market is reduced to simple fear or greed decisions which drive co-ordinated collective human behaviour.

In the short term timeframe (the period of time where traders tend to focus), the nature of the market is highly random. Given that the 'tick' is the fundamental unit upon which price action is constructed, the information gleaned from short term patterns (eg. 1 min to 5 mins) cannot with any statistical certainty (low sample size) have any predictive power as their contribution to total price action over an extended timeframe may or may not have any material impact on the longer term trajectory of price.

The market is an open nested system which has a lower limit (or asymmetrical boundary condition). What this means is that while the market has an indefinite open-ended upper bound, it has a finite closed lower bound, being the tick or the interaction between two participants (a buyer and a seller).

This ensures that what is effectively a random price fluctuation in the shortest timescale becomes progressively more non-random towards the upper limits due to repeatable participant behaviour that progressively starts dominating the noise of the market. That is why monthly charts exhibit a market profile that is usually symptomatic of the underlying driving fundamentals that drive overall 'gross price behaviour' , whereas on the shortest timescales, the market profile is simply a consequence of the placement and timing of trading activity which could be for a host of factors.

It is the same asymmetrical boundary condition that is thought to be applied to the structure of our universe, in that what were thought to be random quantum fluctuations at the smallest scales at the Big Bang, according to the Standard Cosmological Model, are now believed to be the formative seeds under universal expansion that now represent the grand structure of the cosmos. This is not a mystical statement. It is just a standard thermodynamic feature of a particular class of complex open system.

At any particular moment in time over a narrow sample, the trade decisions made may have no context to the overall direction that price may take based on market consensus, which is a broader longer term measure of overall price direction. The ability therefore to use short term price patterns as a predictive measure of future price movement is far more unlikely to possess the requisite statistical validity required to deliver a trading edge over the long term.

For example, have a look at the following price chart of a sample instrument and predict the future direction of the market.


Unfortunately if you had predicted that the market is bullish and is likely to continue, then if I told you that this was a totally random price series at the tick by tick level, what would you think now? It is a totally random price series by the way........and a random price series by definition has absolutely no predictive power.
However as we increase the sample size of ticks and extend the timeframe upwards to the weekly and monthly timeframe, what previously was a random price series may ultimately manifest into a component of a non-random (auto-correlated) time series. Crazy huh? You may be familiar with the expression...."order from chaos".

Visually, you will not be able to discriminate between a random price series and an auto-correlated one. However we have statistical tools such as autocorrelation that can distinguish the two within confidence interval limits. Remember in probability land.....nothing is certain....however statistical tools allow us to assess what is more likely to be random or non-random price behaviour.

Now have a look at the chart below which applies a slight bias of 2% per period to the random data above. Here is how it now looks.


Once again we would interpret this visually to have a strong directional bias and conclude that the market was very bullish. However the degree of non-randomness in this price series is minimal. In other words the market profile in this particular example is considerably random with a slight upside non-directional bias. It is this very small non-random drift (positive or negative) where alpha lurks or the feature we use as traders to gain 'the statistical edge' in our systems.

This is what ultimately caps our aspirations to make squillions from trading...... The reality is that not much alpha (a real exploitable opportunity) actually exists in a modern mature market. As a result these mature markets with less alpha are more tailored for traders that employ mean reversion techniques, which is a convergent strategy, that assumes an extended price move from the longer term mean is an outlier that will ultimately revert back to the mean of price action as the sample size increases.

On the contrary however emerging markets appear to display persistent alpha (where strong non-random price directionality rears it's head frequently). These markets offer significant opportunities for trend traders, which is a divergent strategy, that assumes that a price move away from the mean of longer term price action will continue on under momentum as persistent market behaviour begins to dominate market noise over an extended sample set.

Emerging markets such as relatively new indices or instruments typically commence their life with relatively lower random 'noise' than more mature markets. This may in part be attributed to the nature of the participants at the early stages of a markets development who are mostly the institutional players of a particular size or scale.

Arbitrage that exists in an emerging market takes some time to disappear. The composition of participants and the nature of participation appears to have a strong role to play in the way price action unfolds.

With more mature complex markets however, the nature, scale and composition of participants serves to capitalise on any arbitrage present and quickly exploit these latent opportunities. In this sense, the more mature markets tend to be more efficient leading to intense complexity in price action and a far greater extent of 'noise'.

We must always remember that in this zero sum game (or negative sum game with frictional costs), there is always the counterparty who is taking an opposite view on the way they think price will play out.

In complex markets, arbitrage opportunities are extremely quickly exploited leaving a feeding frenzy in it's wake with a lot of wounded participants. The patterns of activity left behind from this feeding frenzy are therefore exceedingly complex and their predictive power is harder to crack. .....anyway I am getting off track.



Now compare the random and slightly non-random graphs below to see the affect of what a touch of bias does to an otherwise random price series.



I hope this drums home the point that without the use of statistical tools to interrogate the market, using visual clues alone to trade the market is as effective as reading tea leaves.

The ability to predict future price action with more reliability tend to emerge as the sample size is increased (eg. more time or more data). With an increased data set, we can become more and more confident that the representative directionality in price action is not just a random ephemeral feature and more a pattern of durability and persistence associated with repeated or collective participant behaviour.

This repeated or collective behaviour leads to an autocorrelated time series which is what the market statistician (or quant) is seeking to discriminate. The impact on price associated with co-ordinated or repeated price behaviour reveals itself as a bias in overall price direction (a gross movement)....a small non-random drift embedded in otherwise random price patterns leads to a gross statistical feature. The same can be said for clouds. The individual motions of atoms in a cloud are random in nature but the collective actions of all the atoms lead to the gross statistical features of the cloud itself such as it's overall motion and size.

As traders, when you enter or exit the market, you will never know with certainty whether the trade result could be attributed to random or non-random price movement.

Have a laugh the next time you watch the news and the market pundits confidently state with authority that BHP enjoyed a 5% uplift due to recent merger talks.........the reality is that it may simply be attributed to a cat running over the keyboard of an institutional trader from a large bank......you will never know the actual reason for the price movement be it rational, irrational, random, or non-random. The market and the information contained therein is just too complex to interpret with fidelity.

The key to finding an edge in your trading endeavours is to engage in a consistent behaviour over an extended data set where, as more trades are undertaken, the cumulative trade results become more statistically valid.

At the end of the day, your performance results from trading are dominated by simple random chance, but the fine line we are seeking as traders is whether our systems have a slight positive edge or not. The small non-random bias in the market from time to time is what delivers that edge and more than compensates us for the frictional costs of trading to deliver positive expectancy. Once we have a small positive expectancy, that really is the most difficult aspect of trading as we can then leverage our returns using a range of Money Management (MM) techniques (eg. position sizing).....to aim for improved returns within managed risk constraints.

The best measure to use to assess whether you have positive expectancy (or a definitive edge) is to use a large sample of trades that span across as wide a range of market conditions as possible.......or as Nick Radge (a highly regarded portfolio manager here on the Sunshine Coast and veteran trader) states...."don't focus on a single trade......focus on the next thousand trades".

To be able to stay in the game for the next thousand trades in a market riddled with uncertainty requires that risk management must be your over-riding objective in order to protect your capital. So the secret sauce to successful trading lies in the application of consistent repetive behaviour that has been rigurously tested across a broad range of market conditions....and that has a statistical positive edge......in tandem with strict risk management that allows you to stay alive long enough in an uncertain market to reap the bounty of limited alpha when it is available.

While theories such as 'The Efficient Market Hypothesis' (EMH) are powerful arguments for random markets they are not truly reflective of the underlying reality. These theories employ simplifying assumptions that for complex systems, fail to recognise the propensity for power laws to apply.

A power law is a relationship found in complex systems that exist between two quantities (typically a relational dependency) whereby a relative change in one quantity creates a proportional change in another quantity, and is independent of the initial sizes of these quantities (eg. varies as the power of another). Markets are incredibly complex structures and a small disturbance at one time and place can, through these nested interdependent relationships, create a cumulative large domino effect. This is what emerges from a complex system when the fat tails of Levy Drift rear their ugly head.....in a similar manner to the butterfly effect that we may see occassionally which disrupts natural complex systems.

Some of the key things to remember with this market is that all participants do not have equal position sizes or agendas. If they did, then random walk would rule. Market behaviour associated with human beings and decision making (eg. price flexibility) plus dis-proportionate position sizes and repeated behaviour leads to bias or directional price movement.

For example, in the real world let's say an institution has the task of unloading it's entire holding of a single instrument for hedging purposes. It obviously cannot uinload it's entire holding in a single trade and needs to progressively reduce it's exposure over time. This committed and continued emphasis (sustained and coordinated action) against the swarm of traders taking counter positions each with a different agenda and trading timeframe amplifies price action in a specific directional movement until the process of unloading subsides. This is what creates directional price movement. This process continued at regular intervals creates 'real' trends whether they be sideways or up and down.

When attempting to interpret the behaviour of a complex system such as a financial market, it is useful to turn to other complex systems to identify potentially common characteristics. For example let's assume we are proponents of EMH and conduct experiments of how a handful of sand grains (participants) thrown into a pond (the market) interact with this medium. We will note that due to the simplying assumption applied in this modelling, namely that each participant's market interaction (sand grain) has equal weighting, the impact of the interaction with the pond will be to create a wave disturbance that emanates from each sand grain and quickly dissipates by destructive interference. The resulting wave disturbance is ephemeral (short lived) in nature just as an efficient market under normal trading produces.

Now let's look at the market reality, by removing this simplyfying assumption. Let's assume we have a handful of pebbles of different sizes including sand grains. This is more representative of a modern market and typifies the complex mix of institutions, investors and traders etc. and their disproportionate impact with the market. Now throw this collection into the same still pond. What we will observe is that the residual disturbance is a composite of constructive and destructive interference and is more enduring in nature than the prior example. This latter example is far more representative of a 'real market setting' ......but look at what arises from this market context when we relax simplifying assumptions, we get the emergence of a non-random market feature that provides the foundations upon which a trader, as opposed to a gambler, speculates.

It is this feature (which is missing in EMH) of the real market that actually is responsible for manifesting non-random market behaviour and the reason we get so many fat tails that cannot be faithfully described by a normal distribution. Gaussian yes, normal no. I hear you Nassim Taleb.

Without perfect information on the mechanics of all the constituents of this market, we simply cannot predict the exact nature of the any predictive outcome, but using the Law of Large Numbers we certainly can predict (using probabilities) the average outcome of the entire system in very broad ways such as that price will rise, track sideways or fall. That's about all the reliable information we can extract from this market but this is enough to give the required edge to tip the results in your favour.

For example, if we heat a hot air balloon, we cannot determine the exact mechanics of all the gas molecules but we can determine with confidence that the average result of increased molecular activity will cause the walls of the ballon to expand with a little bit more force and we can describe this movement by the gas laws.
If you can accept this system complexity, then the financial markets are not so hard to understand or interpret, and you do not need to step into scientific 'woo' land to attempt to understand it.....but a bit of thermodynamics certainly helps. Order from disorder is a central pillar to the laws of classical physics and chemistry. It's how any large classical and complex system works such as our weather, steam engines and our financial markets.

Just remember.......in a population of a million gamblers, where everybody risks $1 on a bet of a non-biased coin toss where the winners then play each other for round after round to a single final outcome ...... randomness ensures at the end of the game that there will always be a winner who pockets $1 million dollars.....and yes....a large proportion of the general public will think they have special powers.

Don't be fooled by Randomness.

Trade well.

C

Dear C

Nice to have you back.
Apart from 3-4 guys like you, there is not much left in FF anymore worth spending time...

Thanks for the kind words T....really appreciated. :-)

C

Saturday Night Fever folks.....:-)

Smoother than the Sum of its Parts: An Approach to Portfolio Optimisation

In my previous post Dont be fooled by Randomness, we explored the central feature of a modern efficient market. Namely the dominance of random price action (or noise).

To explain how this noise is manifested, think of the behaviour of the collective participants that make up a modern market. The participants span the spectrum of timescales from the intraday traders and High Frequency Trader (HFT) outfits who interact with the market on the very short timescale to long term position traders who interact with the market on a far more selective and infrequent basis. Each of the different participant behavioural groupings vary in the outcomes they are seeking from their interaction and in the timing and position size of their interactions. Putting this all together as a collective image over time, we can imagine the maelstrom of interactions and the intractable nature of predicting future price action with fidelity.

While this plethora of interactions create chart patterns (or fossilised imprints of these interactions), these chart patterns may simply be attributed to effective random outcomes of very short term participant behaviour and have no impact on the longer term trace of price interactions that is reflective of longer term gross overall price action.

It is the heuristic processes of the brain that make us believe that there is a meaning to every pattern, but what we dont realise is that our brain uses pattern recognition as a rapid response basis to ascribe meaning to every sensory input we receive. It takes shortcuts given the complexity of sensory stimuli it receives and the need to respond smartly, just in case the stimuli is a real threat.

This feature is an outcome of natural selection where the senses relay information to the brain and by pattern recognition, these stimuli are compared with what is held in memory to convey a rapid meaning to the observer that may or may not be representative of the external reality. Everyone can see an image in a cloud or a constellation and many will assign a causative factor to it, however the reality may be that this feature has no more meaning to it than a pattern created from random interactions of a system.

The way we assess sensory stimuli is hard-wired in the way we are designed and a result of how we have evolved to deal with external environmental inputs such as perceived threats or perceived benefits, whether they are real or not.

Dont believe me. Then have a look at the image below and stare at the central cross for a moment.


You will observe that the pattern starts to disappear and re-appear over time.This (Troxler Fading) is how your brain, in responding to stimuli, uses pattern recognition as a basis to heuristically resolve sensory inputs. Over time, as you fixate on a particular point for a short period, an unchanging stimulus away from the fixation point will fade away and disappear. Your brain is heuristically assessing the image and rendering a result that may be contrary to the actual sensory input received.

Similar pattern recognition heuristics are applied to simple day to day tasks such as reading. For example, your brain will be able to render meaning into the following paragraph simply because it utilises the first and last letters of words as a heuristic shortcut before it fills in the gaps with later examination to resolve any further comprehension difficulties.

"I cdn'uolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg: the phaonmneel pweor of the hmuan mnid. It deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Scuh a cdonition is arppoiatrely cllaed Typoglycemia .

"Amzanig huh? Yaeh and you awlyas thguoht slpeling was ipmorantt."

Now if we can accept that perhaps the brain is not the best tool to assess a complex system with fidelity, all is not lost as we can utilise mathematics (namely statistics) to peer into a complex system and utilise the principle of the Law of Large Numbers to render gross statistical features that are more than likely to actually be real exploitable opportunities (Alpha).

Lets take the analogy of a biased coin toss. In a narrow sample selection, there is no guarantee that this bias will be detected, however over a greater sample size, the slight bias to the coin is reflected by the gross statistical results of the entire test. This feature is a characteristic of uncertainty and attributed to the Law of Large Numbers.

Lets assume that we are now all on the same page. I would therefore like to take you on a journey into portfolio management where we apply statistical principles to amplify non-random signals such as bias from the noise of the markets.


Translating Market Behaviour into Return Distributions

The first thing we need to understand is how the features of the price action in modern efficient markets translate to the return distributions (equity curves) you obtain from trading activities.

Dependent on the system that you deploy to trade a given market, a profitable trade result will be generated when the price action of the market coincides with the rules of your system. An unprofitable (loss) trade result will be generated when the price action of the market fails to observe the rules of your system.

The rules of your system dicate terms to the market in how you profit from it and furthermore the rules of your system restrict how your system responds to market uncertainty.

The more prescriptive your rules are, the less breathing rule given to market uncertainty and the less ability for your system to handle market volatility. More rules actually introduce larger negative drawdowns into your system.

Given that markets are complex dynamic systems which fleetingly change market conditions at a whim, the more prescriptive your system is, the less you can endure this natural dynamic feature of markets. More robust systems in general are simpler and have less prescriptive rules. Market conditions that may be in place for a considerable period of time can and do turn on a knife edge into a very different market condition to trap the unsuspecting trader.

Now here is the rub and something to digest. For the discretionary trader out there, they may have an edge in trading a select number of market conditions when compared to a single automated system (eg. an algorithm), however to survive long term across all market conditions that can be thrown your way, you need a myriad of robust systems to achieve this.

This is the domain in which automated systems eat discretionary traders for breakfast. The human brain may be able to handle 5 different systems or so to navigate different market conditions, but a diversified portfolio of 50 plus systems across say 50 seperate markets cannot be managed by that subjective grey stuff in your skull and can only be accomplished through automation.

To assist in making robust systems it is handy to broadly classify market regimes into 6 broad categories of volatile or non-volatile combinations of bear, bull or sideways congestion phases.

Most trading systems can handle say 2-3 of these 6 possible market conditions, but no single system can handle all of them and be profitable across the board. Better systems are those that can be profitable across say 2-3 different market conditions and simply preserve substantial capital loss across the balance of market conditions.

The problem with unpredictable markets however is that you can never determine with confidence how long a particular detrimental market condition/s may last. Most of the time you survive to tell the tale.but some of the time you dont. In this world of probabilities, that some of the time is enough to defeat you and it is the 'some of the time' where risk management is your secret to survival.

Every system used to trade any market instrument or collection of market instruments generates its own distinctive equity curve and each equity curve is a unique signature of how the systems constraints or variables responds to prevailing market conditions. The signature is reflected by the distribution of returns from each trade result that when graphically portrayed provide information about the strengths and weakness of the approach.

The equity curve provides information through the volatility of returns generated that indicate whether the trade journey was a smooth enjoyable ride with limited volatility, or whether the journey was an ulcer ridden ride on a pogo stick?

For example the equity curves below represent the return distributions of 3 month Treasury Bills (grey smooth line) against the orange volatile line representing the return distribution of a strategy that mirrors the performance of the S&P500 TR (Total Return including Dividends) Index.

Chart 1: 3 Month Treasury Bill Versus S&P500 TR Index


Look closely at the profile of the equity curves (grey line and orange line). The risk-free nature of the 3 month Treasury Bill is reflected by the smooth volatility profile of the greyt curve.however for this pain free ride, between 2002 to 2016 your initial $1000 investment would appreciate 1.24% (refer to CAGR) per annum and now be worth $1,199.

Chart 2: S&P500 TR Index Versus 3 Month Treasury Bill


Now have a look at the risk-return profile of the (orange) S&P500TR Index. Ride em cowboy.

A buy and hold approach with an investment that simulated the S&P500 would have been a nervous ride. For the joy of a 6.33% return per annum over the period, you would have experienced two nail-biting drawdowns. A drawdown of 30% on your investment almost immediately upon commencement in 2002/2003 (enough to make you question whether you had it right or wrong)..and then a gut-wrenching drawdown of 51% in 2008/2009 associated with the GFC.

What we see here when we compare the return profiles of these two different classes of investment is the classic risk-reward trade-off. Low return with low risk versus higher return with higher risk.

Now there is a further problem with a volatile return distribution. The chart above assumes you invested $1000 on 1st January 2002. What would have happened if you had invested your hard earned $1000 in November 2007?.*ooooops*..you would immediately have entered a wicked drawdown where 50% of your investment would have vanished before your eyes. It would have taken until December 2011 (4 years) before you would have clawed your way out of this pit of despair and seen blue sky.and furthermore there is no guarantee in this land of uncertainty that the investment would ever have recovered in your lifetime. Just span the last 200 years or so of market history to get an appreciation of what I am saying.

So what can we do to address the volatility of returns to hold onto the gains but at the same time reduce our risk exposure? Its time we started investigating the shady art of portfolio magic where you can create something from nothing.

Portfolio Magic
What portfolio management is all about is to engage in a systematic method of constructing a portfolio of investments from ground up that utilises principles of risk management at all steps along the way to achieve an optimal risk-return profile.

You remember how I told you that a modern efficient market is dominated by noise with perhaps an occasional non-random feature (signal) that gives a bias to this overall noise. Well the return series of every equity curve is also representative of this noise-to-signal market feature.

The equity curve produced by a set of trading rules applied to any complex market comprises a large portion of returns that are simply the result of random price action with a smaller component of the returns being attributed to the trading outcomes arising from valid signals that are non-random in nature.

Now here is the magic trick. If you superimpose a non-correlated return series upon another by portfolio blending, two things happen.

Firstly, the noise of each return series starts cancelling each other out through a physical principle called destructive interference. The noise resulting from the combined portfolio will actually be lower than the noise of each separate equity curve.

Secondly, given the timing differences of valid signals between non-correlated series, the non-random features of each equity curve do not destructively interfere and we can generally summate both results.

The result of blending therefore amplifies the signal to noise ratio of the return series.

This is a very powerful feature of portfolio management that gives you a free lunch.

By blending non-correlated equity curves within a portfolio, you actually get a portfolio that has a risk-reward profile that is greater than the sum of its parts.

If you can grasp this concept, then congrats........You are now ready to understand how to construct a robust portfolio.

Portfolio Construction
In a nutshell, the process of portfolio construction is a simple one. What ultimately we will be doing is to simply blend equity curves of specifically selected strategies to iron out the volatility kinks to and deliver an exceptional risk-reward profile for your portfolio that is better than what each isolated component can otherwise achieve

But before we commence blending the mix we need to ensure that the portfolio constituents are quality inputs..remember in this field of statistics that garbage in equals garbage out. Like a Masterchef you are now ready to find those key ingredients to make the perfect dish that is greater than the sum of its parts.

In the land of reality we need to understand that there is no single solution for capturing all available alpha. What we aim to achieve is to compile a diversified list of systems trading across timeframes and asset classes with the intent that each system contribution captures a small portion of available alpha yet at the same time does not contribute to excessive risk exposure for the entire collection.

If we can achieve this, then we can apply the secret sauce of Portfolio Magic by constructing composite return distributions that through careful blending, offer an enhanced risk-reward result that is greater than the sum of its parts.

Without getting into the specific details which we can explore at a later date, the following non-exhaustive list are pre-requisites you need to understand to assist you in building that perfect portfolio from robust inputs that can navigate these complex markets.

Focus on risk and the profits will simply arise from market behaviour:
As traders, it is hard not to be attracted by the lure of stunning performance returns and this is where most traders tend to dedicate their efforts. The reality however is that this myopic focus fails to appreciate the other essential side of the symmetrical equation of risk-reward, namely risk.

The key requirement for your system in an efficient market is to be able to survive the storm of uncertainty comprising the incessant noise of the market, the non-random market features and the fairly frequent fat tails of non-random directional movement.

The central premise of this post is based on a story of survival in an unpredictable marketplace where simply having a system available at the right time and place is sufficient to exploit alpha. We can all look back in hindsight and say that we could have caught that major market move if we were there at the right time..but a different kettle of fish for those that actually participate in these probabilistic events. How do you ensure that you are there at the right time and place without attempting to predict when those events occur?

You therefore must have your systems turned on 24/7 that are capable of surviving market uncertainty but at the same time exploit alpha when it rears its head when market conditions are favourable to your system. You must be present and riding that wave of opportunity even though you will never know when that wave turns into a tsunami.

Look for small amounts of alpha which can then be consolidated by a portfolio into large alpha:
Modern efficient markets as discussed are characterised by randomness, but occasionally within this noise there is a slight bias that leads to exploitable opportunities (arbitrage). The ephemeral opportunities in a modern market quickly gets exploited as algorithms and traders adapt to these new opportunities. Alpha appears here and there at unpredictable times and we need to recognise that there is no single solution that can capture all that is available. The market is dynamic and as such certain systems may have their time in the sun capitalising on an exploitable opportunity but quickly that opportunity is arbitraged away until a new opportunity is identified by a system pioneer. Finding the appropriate solution for harvesting alpha for all market conditions (the holy grail) is like searching for a needle in a haystack.

Provided that each contributing component of the portfolio has positive expectancy (an edge) and contributes modest alpha to the entire portfolio, by being able to survive the storm of uncertainty through a broad range of market conditions, we can then compile a powerful portfolio solution that gets the best bang for buck in terms of risk-reward for your finite investment capital.

Use as much Data as Possible:
Use as much data history as possible that is representative of as many market conditions as possible in assessing your investment universe for candidates that may be suitable for your portfolio..and you may have a better chance of riding the storms.

The significant degree of non-predictive noise in an efficient market has the ability to fool you. What you may think is a profitable strategy that uses a small data set, may just be simple luck. Never underestimate randomness in the market. It is everywhere and is attributed to the complex interactions that occur between its participants.

You will never be able to determine with certainty whether your system has a statistical edge..the best you will be able to achieve is a level of confidence within a defined confidence interval.

Predicting these markets is a futile exercise. The best we can come up with is whether or not your system has survived over an extended history of market conditions. The assumption therefore is that history repeats. but unfortunately to have any confidence in this statement, means that you need to have as much history as you can get your hands on that spans as great a range of potential market conditions as possible.

Fortunately we have studies on the last 800 or so years of market history from the days of the great tulip bubble that lead us to concluding that certain market behaviours associated with human nature are persistently repeatable, namely fear and greed. This simple lesson from history of repeatable human behaviour is what the diversified systematic trend follower predates on.

Having faith in a historical data set is not a perfect assumption as new market conditions may emerge that we have never experienced before.but it is the best that we can hope for. As humans we tend to have short memories especially when markets are booming. The best tip I can give to you is start taking your history lessons seriously and remember.Lest we Forget!!!!!

Chart 3: The Bar of Death USDCHF (US Dollar Collapse on Swiss Bank Exchange Rate de-peg 16 January 2015 )

Attached Image (click to enlarge)

While events such as the Swissy de-peg (above) have the potential to knock a trader who is overexposed in a particular asset for six, the diversified portfolio manager that has significant cross-asset diversification can ride out these calamities with limited impact.

Complete market failure Force majeure is also something you need to always keep in the back of your mind. It may just happen. Lets say that a World War 111 breaks out.then what would be the impacts on your portfolio? Unfortunately we can never totally eliminate risk but at least we can learn from history to identify ways to mitigate risks that have been prevalent in the past.

Choose Liquid Instruments:
Liquidity is a valuable risk management lever during adverse times. You may not appreciate its significance until that very time you want to get extract yourself from a painful loss. When you need liquidity, it may not be there.

Liquidity relates to the ability for you to turn your investments into the most liquid instrument being cash. Low liquidity can be responsible for significant volatility in your return profile. The point about liquidity is that it only rears its ugly head when times are tough for investors or we are facing extreme volatility such as a market crisis.

The result of low liquidity is an equity curve that may have attractive strongly rising equity curves in periods of boom, but look for long enough and you will find that these periods of boom are periodically interspersed with significant periods of bust.

If you think that the S&P500 TR is too risky for your liking, then it is time to smell the thorns and realise that the equity curve associated with illiquid instruments can be more pronounced.

This is not to say that traditional liquid markets cannot become illiquid at times, but in this context, liquidity is a relative measure. If an exchange has a high volume of trade that is not dominated by selling, the price a buyer offers and a seller accepts will be fairly close. During market routs however, this principle is tested, with buyers demanding deep discounts, but in general the impact of lower market liquidity for highly liquid instruments is less than illiquid markets such as real-estate.

Finite capital is a limitation that severely affects your ability to diversify.
When assessing potential candidates for inclusion in your portfolio you need to be able to assess the strengths and weaknesses of each return series in terms of risk weighted returns. This is essential as your finite capital constraints mean that you need to select from alternative offerrings to produce the healthiest overall portfolio.

The impact on finite capital constraints rears its head during a drawdown. Lets assume that you have only $100,000 as trading capital. A single trading strategy using position sizing that over the course of an extended data set (say 10 years of history) has a drawdown of 50% means that half your available trading capital will need to be allocated as a risk measure to that strategy. This therefore severely restricts your ability to diversify into other systems.

Before we commence any investment we need to have a sound understanding of our personal risk tolerances. Without that knowledge you may find that under very normal market conditions (such as a short term unfavourable market condition), that the drawdown impact may exceed your risk tolerance leading to detrimental investment decisions that result in you abandoning a potentially very successful investment strategy.

For example, none of us would expect the stock market to experience an unfavourable drawdown of 50%, but simply refer to your history books as in black and white we have a history of experiences such as this throughout the stock market record.

Understand using the Law of Large Numbers what the Maximum adverse excursion of your system will be.
To understand the risk of an investment, a long term equity curve provides an understanding of the history of volatility of that instrument over time. We can also use statistical measures such as the Annualised Sharpe Ratio to provide an indication of the volatility of the distribution of returns..however my preferred ratio is the Return to Drawdown ratio (or alternatively Adjusted CAGR).

The reason for my preference for using Drawdown as a risk measure is that I like to know in $ terms, the impacts on my portfolio as a worst case scenario. I like to carefully consider this maximum drawdown against my personal risk tolerance and consider how I would psychologically fare in the midst of this horrible event.
The problem with drawdowns is that you never know how low they could go, or how quickly your portfolio can recover to levels pre-drawdown. The questions you will be asking yourself during this time are truly damaging to the psyche. Is my system broke?......will I ever dig myself out of this?..are we going to zero?.........what will my wife and family say?......am I just a gambler?......that 1% return offering by my bank now looks attractive!!!.. etc, etc, etc.

The problem with using the maximum drawdown as a measure of system risk is that the actual drawdown is a feature arising from a series of adverse trade events or importantly the sequence of losing trades you may have. Change the sequence of trades and you have a different drawdown profile.

To overcome this problem of relying on the historic maximum drawdown of a system as a representive measure of risk we can apply a Monte Carlo simulation (reorders the trade sequence) to the trade history of a backtest to assess where the actual back test plotted in this Monte Carlo sequence.

Chart 4: Plot of Equity Curve within Monte Carlo Configuration


If you find that your distribution of returns using actual historical data plots in the upper or lower quadrants of the Monte-Carlo Distribution, this tells you important information. For example, the chart of the left (above) shows a back-test result which plotted in the higher range of the entire Monte Carlo distribution. This suggests that your result is not representative of the average of the sample and in relative terms, the trade sequence of the backtest produced a better result than average. As a result, you would not use the historical results as a proxy measure to assess the risk-reward profile of the contributing system.

The curve on the right hand chart however falls in the middle of the distribution of returns and is more likely to be representative of your future result. As a result, your historical backtest could be regarded as a useful proxy measure or benchmark.

Once you are happy with an equity curve that is representative of the sample of return distributions, we can then more reliable use the maximum drawdown of that series as a guide to maximum adverse risk.

Now remember, the old adage to apply is to assume that your worst drawdown is ahead of you. What this means is that the longer you are in this game, the greater probability of a consecutive run of losing streaks. As a precautionary tale the way you should treat the maximum drawdown experienced in the past 10 years or so using the Monte Carlo simulation above to validate the result, is to then assume that your future worst drawdown will be twice as large as the drawdown produced by the average return distribution. This really ups the ante and ensures you stretch yourself in finding the best possible ingredients for your portfolio.

Finite investment capital is a major obstacle for diversification. One way to overcome this obstacle to an extent is to use Return to Drawdown Ratios as a method to select between investment alternatives. This way you are able to hone in on those investments that offer superior risk weighted return metrics for your portfolio allowing you to get a bigger bang for your finite buck. The Return to Drawdown ratio allows you to rank different investments and determine the scalability of your investments in your portfolio in accordance with your risk appetite.

CAGR is an acronym for Compound Annual Growth Rate. Having a knowledge of the adjusted CAGR allows you to compare risk weighted performance returns against alternative investments. To calculate adjusted CAGR, what we do is take the raw CAGR of a particular investment (say 15%) and take note of its maximum drawdown (say 52%). Now lets say our maximum risk tolerance is 30%. The adjusted CAGR needs to re-weight the raw CAGR to reflect a 30% drawdown. To achieve this apply the following formula (Raw CAGR/Max Drawdown) x Max Drawdown tolerance) = (0.15/0.52)x 0.30 = 8.65%. We can apply the Adjusted CAGR formula to all our alternative investments so we can rank them all in terms of their risk adjusted return result.

For example lets compare two return series, but before we do we conclude that a 30% drawdown is our risk tolerance level. :

Example 1 = a $100K investment in system 1 that offers an annualised raw return (or CAGR) of 15% per annum between 2002 and 2016 and a max drawdown 60% or a ($60K) hit to the portfolio at some point in time.

Example 2 = a $100K investment offering a 10% raw CAGR between 2002 and 2016 and max drawdown of 10% or a ($10K) hit to the portfolio at some point in time.

A focus on return alone would make you conclude that example 1 was the better alternative.

Now lets look at the risk adjusted return result using Adjusted CAGR.
Example 1 = 0.15/0.60 x 0.30 = 7.5%
Example 2 = 0.10/0.10 x 0.30 = 30%

While the raw returns suggest that Example 1 is preferable, a risk weighted comparative assessment clearly concludes that Example 2 is far better ..In fact 4x as preferable. What this means is that pound for pound, Example 2 is a far better investment offering as it offers much steadier returns with lower risk.

Become familiar with Correlation between Return Series
It is the risk-reward relationships that are the most important aspect to portfolio management..not simply raw returns. Asset correlation is a very powerful feature that the Portfolio Manager has up his/her sleeve to drive strong returns within managed risk constraints.

Without going in to too much detail here, as asset correlation has already been discussed in prior posts, how assets move in in relation to each other defines the extent of correlation in the return series between them.

Assets by virtue of their relational dependency can move in concert together (positively correlated eg. approaching +1), or be diametrically opposed (perfectly anti-correlated) in their relative movements (approaching -1).

This relational dependency (approaching +1 or -1) between a pair of assets produces unsustainable growth in the portfolio returns during boom times but acts as a double edged sword offering dramatic drawdowns during bad times.

Importantly for a portfolio manager, a correlated portfolio exacerbates the volatility of returns whereas an un-correlated portfolio smooths the distribution of returns.

Principles of diversification seek to reduce this correlation to as close to zero as possible, thereby when one asset in the portfolio is entering a drawdown, another asset is entering a draw-up. The result for the portfolio being a linear equity curve as opposed to a volatile return series.

Lets assume a return series relative to a portfolio has a correlation of +1. This means that the return series moves in concert with the overall portfolio. When the portfolio is encountering a drawdown, the instrument is also in a drawdown phase. The cumulative impact of that instrument to the overall portfolio therefore exacerbates the drawdown of the portfolio.

What a portfolio manager would therefore seek to do would be to search for a return distribution that offered a -1 correlation to that instrument and thereby to together produce a 0 correlation (+1 plus -1) to that portfolio and thereby minimise the risk impact on the overall portfolio while benefitting from the two returns of both asset series.

Lets look at an example.

Table 1: Performance metrics of the Top Long Term Fund Managers in the Trend Following Space


Refer to the highlighted row in the Table 1 that provides performance results of the Dreiss Research Corporation.

A portfolio comprising the Diversified Program offered by Bill Dreiss offers a very solid return of 17.76% CAGR between 2002 to 2016, however this heady return entails suffering a maximum drawdown over the period of 51.44%. While this drawdown may be too much to stomach for a trader engaging in a single system, it is this sort of return distribution that is music to the ears of the Portfolio Manager who may be eager to include by allocation a portion of this return distribution into his portfolio to leverage performance returns, provided he can water down the drawdown impact on the entire portfolio.

Lets see if a Portfolio Manager can cheat by jumping onto the back of Bill Dreiss's stunning performance and include an asset in his portfolio that assists in smoothing this rocky road while at the same time benefitting from the great results of Bill.

What we are looking for then is a return distribution that serves to dampen the volatility. We can see that the Dreiss Research Program has very little correlation with the S&P500 TR index. Refer to the highlighted column S&P 500 Correl. Look at how the Dreiss program correlates with this index at -0.02. This is as about uncorrelated as you can get (zero being perfectly uncorrelated). Ideally we would be looking for a return series that was anti-correlated with the Dreiss Research Program (offering a correlation approaching -1), however an uncorrelated return series will do the trick.

Lets have a look at the equity curves of Dreiss against the S&P500 TR Index before we blend them.

Chart 5: Dreiss Research Diversified Program Performance Metrics


Closely look at the green line (Dreiss equity curve) and the blue line (S&P500 TR). As you closely look, you will see that when Dreiss is performing strongly, the S&P500 TR Index is entering a drawdown and vice versa. Just by a visual interrogation of the equity curves you can identify if the different return series are correlated or not.

Now lets look at the key performance statistics to see how this compares down the track with a blended result simply using correlation as a guide.



Now, when we blend these two return streams into a new portfolio, we want to capitalise on the positive qualities (the returns of Dreiss) while at the same time, plugging those nasty drawdown periods and in particular that incurred in 2012. So lets merge the two income streams with an equal weighting applied to each and see the result.

Chart 6: Dreiss Research Diversified Program and S&P500 TR Blend Performance Metrics


The red equity curve represents the blended result. Now remember we are not focussing on the actual raw return. What we need to focus on are the risk-reward metrics. The reason for this will become obvious shortly.

Here are the performance results when compared to Driess alone:


Concentrate on those risk-reward metrics that have been highlighted and in particular the Return to Drawdown ratio. What this tells you is that the blended result has a higher relative risk-reward ratio and delivers more bang for buck with less ulcers. The scaled CAGR is a comparative measure assuming your max risk tolerance is a 40% drawdown. Pound for pound the blended result produces an adjusted CAGR of 21.41% for a max drawdown of 40% whereas the Dreiss program alone produces a scaled CAGR of 13.72% for the same 40% drawdown.

Under finite capital constraints you can therefore allocate more of your portfolio to the blended result than to Dreiss alone and supercharge your performance results.

Understand Methods of Diversification
While asset diversification or timeframe diversification can reduce your risk exposure to a degree, as more and more markets are becoming correlated, turning to other methods of diversification such as system diversification is becoming more and more important.

While you have little control over the correlation that may exist between different markets or timeframes, where you do have control is in your system design, which can be specifically configured to possess a unique equity curve that addresses a portfolios weakness by plugging the volatility gaps.

What this is saying is that system diversification and specifically your ability to design systems to respond to particular market conditions is your ideal tool for portfolio management.

Only consider systems in your selection that offer Positive Expectancy
Each contributing system to your overall portfolio must have positive expectancy. It is not essential that this positive expectancy is high, but provided each system contributes weak alpha to the portfolio, then we can cook this up to produce stunning returns in a portfolio.

Identifying systems with positive expectancy is one of the harder challenges of this game..but is a critical feature of portfolio robustness. What this protects against is the likelihood that a failure in one element brings down the entire portfolio.

The principle of quality in and quality out is in play here. A system with a true edge by definition has been able to survive what history has thrown at it and has still been able to produce a return stream that more than compensates for the frictional costs of trading.

Simply blending non-correlated return streams that have not had a demonstrated track record over recent market history is not going to get you anywhere.

Using a Diversified Portfolio allows you to employ simpler system ingredients
Holding a trade in your portfolio indefinitely, increases risk to your portfolio like a slowly heating pressure-cooker. As a result, buy and hold strategies typically have the greatest volatility in return profiles. How we release this risk pressure through trade management is critical for the portfolio manager.

While setting stops is an essential feature of risk management for a single system to release this risk potential, under a diversified portfolio where any individual component may only represent say 0.5% - 2% of your total portfolio, then the need for a stop as a release valve is not an urgent necessity.

However this does not mean that we abandon the risk of an adverse event for a single instrument. What we do however is to engage a different type of exit such as a time based stop or a performance based exit approach that prevents us from uncessarily introducing drawdowns into our portfolio.

Remember I mentioned that the rules of your system apply constraints to the way price action must unfold for you to be profitable. A classic stop loss is a significant constraint that more often than not, unnecessarily extracts you from a trade. Under a diversified portfolio where you can give your trades greater freeedom to breathe, this constraint is lifted allowing you the luxury of seeking simpler strategies that would not be considered by a single system trader.

Understand the Benefits of Positive Skew
It is essential that you understand if your system components have negative or positive skew. Skewness is a term in statistics used to describe asymmetry from the normal distribution in a set of statistical data.

For a diversified portfolio we are after systems with positive skew, namely, lots of small losses with the occasional big win.

A single system trader who does not have the capability to enjoy the benefits of diversification usually elects the converse negatively skewed strategy. Namely a strategy with lots of small wins interspersed with the occasional large loss.

Negative skew strategies have the ability to lull you into a false state of confidence as you believe that you have found the Holy Grail, only to then find, when market conditions change, that you have the mother of all problems on your hands.

Does this remind you of a Martingale strategy (commonly referred to as the Gamblers fallacy)? I hope so, as Martingales have the mother of all negative skews.

It is essential for risk management purposes that you blow off risk steam in your portfolio by being prepared to cut losses short and allowing your winners to run. Yes you have more volatile return distribution profiles, but this is a sign that you have released intrinsic risk from your portfolio.

Convergent strategies such as mean reversion tend to be negative skewed and offer great, almost perfect equity curves for a period of time..until they dont and it is too late to defend. RIP Long Term capital management (LTCM).

Conclusion

Hopefully this post has provided some food for thought for those who may be struggling to find a consistent return in these modern mature markets. There is a solution, but that solution requires you to recognise that there is no single winning system or Holy Grail.

You have to be prepared to cheat and use portfolio construction as a method to ride market uncertainty, but to do this you need to divert your attention from system returns and focus on the all-important risk management aspects of generating these returns.

There is a reason we say that with increased return comes increased risk. These two common features of every investment are co-dependent features.
Thankfully this post provides a few clues in how you can use well-known risk management methods to at least ensure that you can optimise the risk-return relationship.

Trade well

C

Oi Oi !


Just in time for Xmas ..


Logging in to find you've re-surfaced is a nice surprise mate.. this place just isn't the same when your not around...

I mean, who else plays the Funk-ing tunes !!

Welcome back C..

Woooohooooo......It's great to be back with my friends Ja.

A few changes to the forum such as a confounding new array of Emojis....christ talk about choices!!!!!!....and a like button. Who would have thunk it :-)

Now for the important stuff. Time to dust off the cobwebs and get serious. Ja's in da house!!!!!!

Welcome mr copernicus, as always your analysis, knowledge, experience, conclusions and your way of translating ideas simply worthy of a master. You are a master.

It really is a pleasure my friend. Thanks for the kind words of support. :-)

Hey C, nice to have you back mate. Thank you for the mini-book you've posted above! I've only read the first two posts in detail so far, so apologies if what I'm about to say have been addressed in the third post.

One thing that stands out for me from reading the first post is the wide range of results that these funds achieve, even though their managers are basically on the same page with their investment beliefs. For instance, the leader in the Adjusted CAGR ranking is Fund 25 (Fort LP) at 31.31%, and the lagger is Fund 3 (AIS) at -0.75%. Most (90%) of the AIS fund is based on discretionary trading as opposed to systematic trading by the looks of it, so I guess that's one reason for the big difference. But even funds which mix strategies and include a large chunk of systematic trend following have a much smaller Adjusted CAGR, such as Fund 23 (EMC) at 8.82% (which is still great, by the way!). I've noticed that the best performers have a large chunk of CT (counter-trend) trading, which as we've discussed on here before seems to have been growing in significance over recent years due to the markets' increasing tendency to revert to the mean. Whether this will continue in the future remains to be seen.

Anyway, I guess the bottom line is that even if you're an experienced fund or portfolio manager who understands the importance of trend following and diversification, you still have to choose the right mix of instruments and strategies in order to gain the best results, which obviously includes luck as well as skill due to the unknown nature of the markets.

Regarding your second post about randomness, I mostly agree with what you say and of course it's true that longer timeframe charts, such as monthly, have more bias from macroeconomic drivers than shorter timeframe charts, such as five-minute. This is one reason why discretionary traders who place a lot of weight on FA hate the smaller timeframes so much!

However, I'm increasingly coming to the conclusion that exploitable edges do exist in the seemingly random smaller timeframes, and these edges are derived from large-volume players moving price in order to fill or empty their books more efficiently in areas of deep liquidity. A few traders can identify where these liquidity pools are and predict the movement of price towards and then away from them almost effortlessly, but for most of us it's bloody hard work and requires a lot of screen time.

Generally though, I agree that short timeframes should be avoided, not only because of their random nature but also because of the high transaction costs involved (cost per trade is higher due to smaller profit per trade, and there's a tendency to trade more frequently).

Stuff to do now but I'll read the third post properly later today. Thanks

Hey NT. How is beautiful Hokkaido going?

Sorry about the Mini-books....LOL. I had prepared them earlier and thought I would add them to this thread as I would like to steer away from the same old same old about trend following to discuss topics of interest to me that (if I draw a long bow) may be relevent to trading *sniggers*. I will try to keep them relevent, but I think you know me by now.......so we hopefully are in for some unusual topics that don't get discussed much on forums like these.

You have keen eyes NT and I agree with you. There is a disparity in performance between the FM's that utilise similar trend following recipes.....however I have found a useful way to discriminate those that apply a similar recipe and those that don't. If you look back at that table again and refer to the last column titled 'TF Index Correl'. I have highlighted in green those FM's that, over the very long term, possess similar correlations with the Trend Following Index (TF Index) which is compiled from the 47 funds in the table. Those FM's with high correlations have similar TF recipes and you can see as you scroll down the list that there is less variation in the performance metrics of these highlighted FM's than those that do not correlate with the TF Index such as AIS (which you mentioned).

However....as you correctly indicate (eg. when referring to Fund 23 which is part of the highlighted camp), there is still a fairly wide variation in the performance metrics of those funds that possess similar correlations. You need to get under the hood to fully examine the strategies employed by these FM's to see how they tick....but no doubt a significant degree of variation can also be attributed to simple random variation in returns.

While these FM's protect their IP like gold, I have been able to dig out a bit of secret sauce on Bill Dreiss's approach which uses fractal wave theory and trend strength indicators (refer to the attached). However I have not been able to dredge much up on the other FM's except anecdotal statements from their Annual Reports and other generic sources regarding the simplistic form of trend following they apply over a diversified base.

A fair bit of research has been obtained since 2008 by the FM industry on the significant increased deployment of other systems to flatten the volatility profile attributed to a simple focus on trend following. Quite a few podcasts on 'Top Traders Unplugged' have discussed this strategic direction in the trend following camp. FM's recognise that market conditions have significantly altered since 2008. The global marketplace of liquid instruments is starting to become super-efficient with the advent of HFT, making mean reversion a strategy of choice under normal marklet conditions, and restricting diversified trend following to becoming progressively more dependent oin 'crisis alpha' for their bread and butter. Prior to 2008, this was not the case.

As a a result, trend Following FM's have been forced to either step out into progressively higher timeframes away from HFT (such as that applied by Chesapeake and Dunn) to exploit alpha, or alternatively FM's are starting to dilute the Trend following concentration in their portfolios to a degree to capture other forms of alpha by including other non-TF systems in their mix such as mean reversion and countertrend techniques...plus a growing base of pattern recognition systems.

Interestingly, in this competetive battle to attract portfolio allocation, it appears that the simpler option of stepping out to higher timeframes appears to be winning the race....for the moment at least, possibly attributed to the reduction in positive skew in those portfolios that attempt to blend the mix.

I also hear you NT regarding alpha opportunities that may exist in shorter timeframes. Unfortunately I have little expertise in this area and cannot offer any valuable comment.... The post on randomness no doubt is polluted by bias from my point of view...however, given my ultimate penchant for deep value investment techniques. a simpler path for me is turn towards the longer term timeframes where economic drivers start revealing their appearance as opposed to investing heavily in technology geared to low latency and taking the years to achieve the significant skill required to extract alpha from the noise of the short timeframe.

If you find any secret sauce in your research in this area NT please let me know as I really enjoy taking these forays with you into new territory (like the Reschke exrecise we recently looked at together a few months back).

Great to hear from you NT. Looking forward to hearing your comments on my next Tolstoy epic. :-)

C

Hi C, I've just finished your third post. Bloody hell, that was epic! And dripping with quality. Reminds me of Paranoid Android...


I might have said this before but you could definitely write a book and receive a nice source of income from your deep pool of experience and lucid presentation skills, rather than posting it all on FF for free! Oh, and Hokkaido is cold and snowy, as usual this time of year, but thanks for asking. Some people love the white stuff but not I. It just increases our heating bill and makes me fall on my arse from time to time. When the children are through uni and the inlaws have demised I'll be moving to a warmer part of the world, maybe even your neck of the woods, wife-permitting

Not read those attachments yet but will do soon. Thanks very much for your long reply to my comments!

Right, back to my Sunday night drinking Have a good one and bye for now!

Cheers mate. I had to laugh regarding your lack of enthusiasm for the snow.

Talking about the weather, a few hours back a wicked storm moved in after a really humid day in South East Queensland and delivered golf ball sized hail to my suburb and fierce local wind gusts. All the power is down here and I am surviving on battery life till my tablet dies.

It would be great if you decided to return to sunny QLD mate......but if you do, bring a crash helmet.

Have a chilled sunday evening mate.

Cheers

C

PS Blacking out for now.

PPS...loved the radiohead.....that hit the spot for a Sunday evening.

Hope you've got your power back now, C. Hail in Queensland? Who'd have thought it! Cheers, NT

Nerd and u will always be, welcome back home .

Hi Bass. Great to be back. :-)

Yep, we are back online. Houston we now have communications. Cheers NT.

Dear C

How can I send you -if you dont mind-a PM?

Morning T :-)

I forgot that I had turned PM messaging off while I was away from the forum. I have now enabled it so you can PM me now.

I hope all is well mate.

C