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Market correlation

Our latest improvement in…The Market Correlation Investable Attribute

In this blog post, we are going to explain the most recent improvements in the last investment attribute (IA) to join Darwinex’ ranks: Mr. Mc, AKA. Market Correlation.

However, before we get our hands dirty, let me briefly explain what correlation is for those of you who are not familiar with this concept.

Correlation is a statistic that measures the degree to which two variables move in relation to each other.

What are the 2 variables we use in Darwinex to calculate the Mc grade?

You are 100 % right! The relationship between your DARWIN’s return curve and the return curves in the underlying assets in which you trade.

Let me explain this further with an illustrative example.

Imagine that your DARWIN has yielded nice returns over the last year. As a result, 100% of Investors would think that you are a trading superstar and money would be pouring in into your DARWIN, right? Well, not that fast.

What if Darwinex’ algos discover that you have always been long EURUSD in 2017?

In this case, the return of your DARWIN will be 100% correlated with the EURUSD and you should not be awarded with any trading medal since you have basically made 1 trading decision in 2017: go long EURUSD.

In this extreme example, your Mc grade would be 0 which, in turn, would deteriorate your D-Score very badly, making it impossible to get a D-Score over 50, irrespective of the rest of the 11 investment attributes. Therefore, no AuM, no DarwinIA, no fame, no superstar status and an empty pocket 🙁

Darwinex Improves The Mc Investment Attribute

However, after having given this a lot of thought, we have reached the conclusion that this is not the most accurate way to measure the Mc attribute.

Well, to be totally honest with you, we already knew that this calculation was just an approximation. Nevertheless, we decided to implement it anyway for 2 primary reasons:

  • It would add much more value to our proprietary diagnostic toolkit
  • It would penalize “one-trick ponies” strategies which would likely prevent investors from investing in a “random” strategy -100% dependent on an exogenous factor, EURUSD evolution-

Why was our calculation only an approximation instead of 100% accurate?

This is due to the fact that, in the old Mc version, Darwinex didn’t take into consideration either the DARWIN’s leverage, which is now considered to be a trading decision in and of itself, nor the nº of D-Periods of experience accrued with such correlation

  • DARWIN leverage

Going back to our example, remember that your DARWIN has always been long EURUSD, imagine that the  leverage applied by our risk manager in your DARWIN, in order to offer an asset with a monthly target risk set at 10% VaR, had varied over time based on leverage changes in your underlying trading strategy. Modifications due to your technical or fundamental analysis/market conditions/predictions… on the EURUSD.

The DARWIN could have been using 5:1 leverage in some trades, 2:1 in others and then up to 8:1, etc. This way, both your DARWIN’s return curve and the EURUSD curve could look very different.

It is a fact that you have always been long EURUSD but your DARWIN could have been using very low leverage when the EURUSD went down, and more leverage when it went your way.

You’d thus be making a much better return % than the underlying asset in which you were trading.

  • Experience accrued: nºof D-Periods

The experience factor is a new variable that we have decided to introduce in the final calculation of Mc.

It is not the same to be highly correlated with the underlying asset during 1 week than in the course of 1 year, and we believe that the impact on the final grade can not be the same.

The tolerance level of the Mc algorithm will be inversely proportional to the number of D-Periods of experience during which said correlation is maintained.

Following our example, if the algorithm detected a significant correlation with the EURUSD, but this has occurred for a short period of time -1 D-Period-, the deterioration in the note of Mc would be lower than if you had 5.

The greater the nº of D-Periods keeping such correlation, the lower the degree of tolerance of the Mc attribute and the greater the penalty imposed to the D-Score.

So, after having thrown your strategy to the wolves, it turns out that you could still be trading superstar!

In summary, we have tweaked the Mc algo so we calculate its grade based on positions in the same asset– considering both leverage a trading decision in and of itself and the Experience factor.

Please note that the “leverage factor” change will improve accuracy of the Mc score in “medium-long term” strategies while not affecting scalpers or day traders and the “experience factor” will improve the Mc in almost all DARWINs.

Trade safe!

 


Do you want to say something about our latest improvement in the Mc investing attribute? You’re welcome to share your thoughts with other members of our Community here

DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory

In 1952 [1], the great Harry Markowitz published a paper on portfolio selection that essentially set the stage for modern portfolio theory in a mathematical context.

Harry Markowitz

Harry Markowitz – Nobel Prize Winning Economist

For those not familiar with this Nobel Prize winning economist [2], he devised a methodology whereby investors could mathematically evaluate the proportion of total available capital to allocate, to each constituent asset in a portfolio of assets.

His method was based on just the means and variances of asset returns.

For different choices of capital allocation per asset in a portfolio, different combinations of mean (μ) and variance (σ²) would materialize, collectively referred to as the attainable set.

As investors always want the highest possible return for the lowest possible risk, Markowitz termed all those combinations of μ and σ² where either:

1) σ² was the minimum possible value for a given μ, or

2) μ was the maximum possible value for a given σ²,

.. as the efficient set, or “efficient frontier” as it’s more popularly known.

Harry Markowitz - Efficient Frontier ModelHow did it benefit investors?

Markowitz Portfolio Theory (MPT) stated that investors should select a portfolio from the efficient set, depending on their risk appetite.

However,

The variances of asset returns in a portfolio do not fully explain the risk taken by an investor, and MPT is therefore not entirely applicable in practice.

For instance, MPT does not reveal the Value-at-Risk (VaR), extreme variations in an asset’s risk profile during times of high volatility, nor the Capacity of a given portfolio.

Darwinex’ Solution to Markowitz Portfolio Selection

Years of proprietary R&D at Darwinex, reliably addresses some of the inherent problems in traditional mean-variance portfolio construction & optimization.

All DARWIN (Dynamic Asset & Risk Weighted INvestment) assets listed on The Darwin Exchange are measured in terms of 12 Core Investment Attributes that go far beyond mean and variance.

These are:

  1. Experience
  2. Market Correlation
  3. Risk Stability (in terms of VaR)
  4. Risk Adjustment (in terms of intervention to stabilize VaR)
  5. Open Strategy
  6. Close Strategy
  7. Positive Return Consistency
  8. Negative Return Consistency
  9. Duration Consistency
  10. Loss Aversion
  11. Performance
  12. Capacity

With these robust behavioral analytics, DARWIN investors are able to iteratively filter assets in order to maximize expected returns and minimize standard deviation (risk), with zero mathematical optimization necessary to achieve desired allocations.

In fact, even an equally-weighted portfolio arrived at using DARWIN Filters presents a more statistically robust set of portfolio allocations, than mean-variance optimization where the possibility of overfitting to asset returns is a hidden risk.

DARWIN Filters

Creating custom combinations of the 12 investment attributes allows investors to analyse the behavioral machinery of assets they wish to include in their portfolios.

As Value-at-Risk (VaR), Excursion Analysis (+/- return consistency) and Capacity among others, become integral components of an investor’s selection criteria, the risks presented by traditional MPT (as discussed earlier), are effectively mitigated.

Perhaps the best way to demonstrate the effectiveness of this approach to portfolio construction, is through an example.

EXAMPLE: Real portfolio constructed using DARWIN Filters

A portfolio of 15 highly uncorrelated DARWIN assets (with an impressive Sharpe Ratio) was built using just DARWIN filters and zero mathematical optimization.

For inspiration, here are the actual realised returns of this portfolio between June 2014 and March 2017, both gross and net of performance fees:

DARWIN Portfolio Returns (June 2014 - March 2017)

DARWIN Portfolio Returns (June 2014 – March 2017)

And here is this DARWIN Portfolio’s performance against the S&P500 over the same time period:

DARWIN Portfolio vs. S&P500 (June 2014 - March 2017)

DARWIN Portfolio vs. S&P500 (June 2014 – March 2017)

Steps used in portfolio construction:

1) DARWIN Filters were first created using a combination of the 12 available Investment Attributes (as listed earlier), to define the investment criteria.

Create DARWIN Investment Attribute Filters

Create DARWIN Investment Attribute Filters

This filtered the initial full list of over 1,000 listed DARWIN assets, down to 15.

 

2) Monthly Returns listed publicly on each of the 15 DARWINs’ pages, were then used to construct a Variance-Covariance Matrix.

DARWIN Asset Returns

DARWIN Asset Returns

 

DARWIN Variance-Covariance Matrix

DARWIN Variance-Covariance Matrix

 

3) Assigning equal weights of 6.67% to all 15 assets, Expected Portfolio Returns and Standard Deviation were then duly calculated.

This led to a DARWIN portfolio with the following features:

DARWIN Portfolio Backtest Statistics

DARWIN Portfolio Backtest Statistics

 

 

 

 

4) For sake of exercise, here is a comparison of what MPT optimized allocations would be for the same portfolio:

Equal vs. MPT Optimized Portfolio Weights

Equal vs. MPT Optimized Portfolio Weights

 

 

 

 

 

 

 

 

 

 

 

 

MPT Optimized Portfolio Backtest Statistics

MPT Optimized Portfolio Backtest Statistics

 

 

 

 

An MPT optimized DARWIN portfolio would indeed have lead to a higher Sharpe Ratio (5.92 vs. 4.17), higher Expected Return (38.80% vs 28.61%), and a slightly higher Standard Deviation (5.71% vs. 5.66%)..

.. but at the risk of allocating a majority of available capital to only 4 out of 15 assets in the portfolio.

In this scenario – and as per Markowitz Portfolio Theory – a conservative investor would likely have opted for the equally-weighted portfolio, while a more aggressive investor may have opted for the MPT-optimized portfolio.

In both cases however, DARWIN Filters enabled both profiles of investor to consider the important attributes of Value-at-Risk (VaR), Capacity, Risk Stability and Consistency..

.. as opposed to traditional MPT mean-variance analysis where these would have been overlooked.


References

[1] Markowitz, H. (1952) Portfolio Selection. The Journal of Finance, Vol. 7, No. 1, 77-91. March. 1952.
www.jstor.org.proxy.lib.chalmers.se/stable/10.2307/2975974?origin=api (2012-10-30)

[2] The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1990.
http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/markowitz-facts.html

Watch this video to learn more about a DARWIN’s Investable Attributes:
* please activate CC mode to view subtitles.

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