As we all know “there is no Grail”, but if I was asked to say what is closest to the Grail, I would definitely answer “market neutrality“. Market neutrality means that the portfolio returns are independent of the market situation. It allows us to earn more regardless of whether the market is falling or growing, and drawdown periods of such a portfolio last much less than with standard approaches.

In this series of articles, you will learn how to make market-neutral investment portfolios that have returns much higher and drawdowns much lower than S&P500.

Here is one example of a market neutral-portfolio

Fig. 1: Market neutral portfolio (blue) and S&P500 (red)

Pay attention to the yield (over 2.8x times) and the maximum drawdown (less than 2.9x times) of the portfolio compared to S&P500, with almost the same standard deviation “Stdev”.

The same standard deviation means that both portfolios go up and down in the same way when the natural background, non-crisis market fluctuations occur.

Note: The portfolio is built using ETFs– the most convenient tools for the average investor.

Let’s look at what this market neutrality is and why it works so well

A measure of market orientation is a correlation. Correlation is, roughly speaking, the similarity of dynamics of different values. It is measured in the range from +1 (full copy of the dynamics) to -1 (the complete opposite of the dynamics), a correlation of 0 indicates the independence of the values of one from the other.

To begin with, we will determine what is not market neutral. For example, S&P500. Since S&P500 is the main indicator of the world economy, S&P500 itself is often referred to as the “market”. Accordingly, the dynamics of S&P500 is the most complete reflection of the market dynamics. Let’s take a closer look Fig. 2. There is ETF SPY chart with a strong correlation to the market +1 (highlighted in red), full copy of S&P500:

Fig. 2: ETF SPY chart with a strong correlation to the market +1 (red), full copy of S&P500

The chart uses “total returns”, which means that the chart takes into account not only the exchange rate growth of the instrument but also the dividends paid and reinvested in it.

Once we understand what a +1 correlation is, let’s look at other examples with a correlation less than +1 (Fig. 3).

Fig. 3: QQQ chart (blue), DIA (red) vs SPY (orange)

ETF’s QQQ and DIA have an almost complete correlation to S&P500, as they are also securities indices within the American economy. They may have a different composition of companies, but their structural similarity makes their dynamics almost identical.

Now let’s take a look at the chart of gold (ETF GLD) versus S&P500 (SPY) with a correlation close to zero (Fig. 4).

We can see a very weak correlation. Sometimes they go together, sometimes opposite, often independently of each other.

The following is a chart of US treasures (ETF TLT) compared to ETF SPY (Fig. 5). We observe a negative correlation, often going in the opposite direction from each other, especially on market falls (marked with black boxes).

There is a clear fundamental reason why treasuries behave this way. We will discuss this in detail in the following articles.

Now that we understand the correlation as a measure of market orientation, we can reveal the “graality” of this knowledge

We need to collect in the portfolio the maximum number of the least dependent, preferably even opposite from each other returns.

In our list, the least dependent and more opposed to each other are SPY (+1) and TLT (-0.35). Let’s combine them with each other in an equal 50% to 50% ratio and see what happens. For clarity, we will compare the resulting chart with SPY 100%.

**You can see that the “miracle of least correlations” has occurred** (Fig. 6). We have a yield (highlighted in green), as in S&P500, while the maximum drawdown (highlighted in blue) has become more than 2 times lower than in S&P500. And the standard deviation (highlighted in purple) has decreased. Our portfolio has become much more stable, less sagging and has not lost any returns!

Fig. 6.: SPY+TLT (blue), SPY (red)

As you can see, inverse correlations cancel each other out, stabilizing the portfolio.

If we do something similar to the less inversely correlated SPY (+1) and GLD (+0.06) series, we will see that the “miracle of least correlations” is much weaker (Fig. 7).

Fig. 7: SPY+GLD (blue), SPY (red)

The yield is about the same as that of SPY/TLT, but the drawdown is higher and the standard deviation is also higher.

If we take a similar action on instruments with a very high correlation, there will be no “miracle of least correlations” at all.

Indeed, the yield, standard deviation and drawdowns of very similar QQQ+SPY are approximately the same as that of pure SPY, which may be a proof of our hypothesis “from the contrary” (Fig. 8).

Fig. 8: QQQ+SPY (blue), SPY (red)

Now let’s talk about the quality of the portfolio

We are interested in several parameters: annual yield, standard deviation, maximum drawdown and correlation to the market. But it is not just these parameters that are important individually, but their relationship to each other.

Let’s compare the portfolios listed above with each other for the same time period, so that this is correct (Fig. 9).

Fig. 9: SPY+TLT (blue), SPY+GLD (red), SPY+QQQ (orange)

The first portfolio (SPY-TLT) is much more stable per unit of risk than the second (SPY-GLD) and, even more so, the third (SPY-QQQ). We can understand this more fully through the ratios yield/standard deviation (hereinafter eSharpe) and yield/maximum drawdown (hereinafter CALMAR).

Note: eSharpe (easy Sharpe) is a lightweight version of the Sharpe ratio, from which the dynamics of the risk-free% rate has been removed.

**First portfolio: 8.7 / 8 = 1.09 eSharpe and 8.7 / 22.3 = 0.39 CALMAR**

**Second portfolio: 9 / 11.6 = 0.77 eSharpe and 9/25, 6 = 0.35 CALMAR**

**Third portfolio: 10.5 / 15.6 = 0.67 eSharpe and 10.5 / 50 = 0.21 CALMAR**

The higher the number, the better. The higher the number, the better. Now we see that the first portfolio is more efficient than the third one by 1.09 / 0.67 = 1.63 times by the eSharpe metric and by 0.39 / 0.21 = 1.86 times by the CALMAR metric.

Pay attention to the red box where the correlation ratios for the market are displayed, and you will understand why this happens.

If history since 2005 is not sufficient for you, then let’s test not through ETF, but through total returns indexes, the data of which is available already since 1978

Fig. 10: Large cap (analogue of SPY) (blue), Long treasures (analogue of TLT) (red), Gold (analogue of GLD) (orange)

Now we can combine everything with the same portfolio structure that we’ve tested (Fig. 11).

Fig. 11: Large cap+Long treasures (blue), Large cap+Gold (red), Large cap (orange)

Compare the eSharpe and CALMAR ratios for each portfolio on your own and you’ll see that the “miracle of least correlations” really works even over a span of more than 40 years.

**And now look carefully at the very first chart in this article again.** At this point you already have a small piece of the puzzle called “market neutrality”. In the following articles, we will put the entire puzzle together completely and you will understand every element of the market neutrality strategy.

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*All calculations are done in portfoliovisualizer. By clicking on the link, you can check all the results yourself:** https://www.portfoliovisualizer.com/backtest-portfolio#analysisResults*

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See you soon and good luck in your business)