# DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory

In 1952 , 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 – Nobel Prize Winning Economist

For those not familiar with this Nobel Prize winning economist , 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 (╬╝) and variance (Žā┬▓) 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 ╬╝ and┬ĀŽā┬▓ where either:

1)┬ĀŽā┬▓ was the minimum possible value for a given ╬╝, or

2) ╬╝ was the maximum possible value for a given Žā┬▓,

.. as the efficient set, or ŌĆ£efficient frontierŌĆØ as itŌĆÖs more popularly known.

## How 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 assetŌĆÖs risk profile during times of high volatility, nor the Capacity of a given portfolio.

## DarwinexŌĆÖ 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 investorŌĆÖs 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)

### 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)

### 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

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 DARWINsŌĆÖ pages, were then used to construct a Variance-Covariance Matrix. DARWIN Asset Returns 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

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 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

 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)

 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