In my previous post on The Single Most Important Performance Driver, I said that the optimal portfolio solution is to put together multiple investment strategies that thrive in different market states.

**This is the multi-strategy approach**.

Let’s now use numbers to demonstrate **how a multi-strategy portfolio is superior to individual strategies. **

**Introducing the Hedge Fund Research Database**

The HFRI database contains many hedge fund indices. These indices are designed to capture the breadth of hedge fund performance across all strategies and regions.

They only show the aggregated performance and cannot be directly invested in.

Out of all the strategy classifications available, I’ve picked a few to represent the most diverse strategies out there. Below is the summary table of the annual returns of the strategies going back to 1990.

As you can see, the various strategies performed differently in some years.

It is from this observation that a whole industry of fund of funds sprouted up over the last couple of decades to help investors mix and match different strategies to form a non-correlated portfolio of hedge funds.

The intention is to achieve a better risk-return profile along the lines of Modern Portfolio Theory.

**How Different Are These Strategies?**

Below is the correlation matrix of these strategies computed using their monthly returns for the entire period.

Most of them exhibit low correlation to each other. Correlation is interpreted between the ranges of -1 to 1. If A and B have a correlation of 1, they move in lockstep. If A and B have a negative correlation, then they move in opposites.

Below are the equity curves of the various strategies. I’ve also included the equity curves of an equally weighted basket of all six strategies, with and without monthly re-balancing.

**Performance Statistics Comparison**

All the strategies have generated returns over the years but some more than others. **However, it is incomplete to look only at the total returns generated.**

We also have to look at how much risk was taken to generate those returns. For that, we turn now to look at their monthly return statistics.

There is clearly a difference between strategies that try to neutralize directional risk (equity market neutral, merger arbitrage, relative value) and strategies that don’t (quantitative directional, systematic diversified, emerging markets).

The former tends to have a better risk-adjusted return as measured by the Sharpe Ratio. However, this comes with the sacrifice of lower returns. A good question is why don’t we use leverage to increase the returns since the volatility and maximum draw-down can afford to be higher?

**The Danger Of Negative Skew**

In order to neutralize directional risk, a pair of opposing trades need to be put on. A simple example is to buy Apple and sell Google. (* because you think Apple might go up, but you might be wrong and tech stocks might go down, so buying Apple gives you upside benefits, and shorting Google gives you downside benefits. inherently, you benefit if your thesis is right or wrong, but one side will go bad while the other side wins, which results in minor net gains*).

This means that although the net exposure is zero, the gross exposure is double. The position is inherently leveraged and if things go wrong, it can go downhill very quickly. This is why such strategies tend to have a negative skew in their return profiles.

This means that they tend to have many winning trades but occasionally will experience large losses. An example of negative skew can be seen in the distribution of monthly returns for the Merger Arbitrage strategy.

The return on a spread is always smaller than an outright position since one leg of the spread is always acting as a drag. Hence, most market neutral or arbitrage funds would already employ some form of leverage to juice up returns. It is unwise to leverage up even more.

**What About Directional Strategies?**

While directional strategies tend to generate higher returns, they do so by taking on more risk. Hence, their Sharpe Ratio tends to be lower. They suffer from nasty draw-downs and also have more losing months. Unless you have a very strong stomach, it is very hard to stick to directional strategies through the ups and downs. The distribution of monthly returns for the Emerging Markets strategy demonstrates this.

**Is There A Middle Path?**

Let’s look at the monthly return statistics for the equal weight basket.

I did not factor in re-balancing cost but a more sensible re-balancing threshold can be used instead of blindly re-balancing every month. This would minimize cost. The benefit of re-balancing should outweigh the cost.

This looks like a pretty decent middle path but what if we still want the kind of high returns that emerging markets can offer?

**Having Your Cake And Eating It**

Let’s run our equal-weight basket with **a leverage of 1.2 times** and see how the statistics look like. We only do it with re-balancing this time.

**The return is now comparable with emerging markets but it was achieved with much lower risk.****The maximum draw-down is also much lower with fewer losing months.**

Below is the new equity curve.

This is the closest to having your cake and eating it too.

**Why Is It OK To Use Leverage Now?**

I said earlier that it is unwise to use leverage on non-directional strategies because of negative skew so why is it ok to use leverage on the equal weight basket?

**The answer is because the basket contains strategies with positive skew which is the opposite of negative skew**. Look at the distribution of monthly returns for the Systematic Diversified strategy.

There are quite a number of small losing months but very rare big losing months.

In fact, there are many more big winning months than big losing months. **This is to be expected since trend-following strategies follow the cardinal rule of cutting losers and letting winners run. This is similar in nature to buying options.**

Adding positive skew strategies to the basket would result in an overall better return distribution profile as shown below.

This is almost like the return profile of Merger Arbitrage except without the tail risk.

**Editor’s Notes: Quantitative Investment Course**

We have now created a quantitative investment course together with Patrick Ling and Lim Eng Guan who are portfolio managers in a hedge fund.

The objectives of the teachings in the course will be skewed towards **3 main objectives**:

**Long term returns 10-15%:**To achieve an absolute return of 10-15% per year over the long term**Low volatility:**To dramatically reduce the volatility of the invested capital (so that we sleep better at night)**Reduced tail risk:**To dramatically lower tail risk against market shocks like the 2008 global financial crisis

This is the backtested performance of their investment strategy to reflect how they would have fared in the markets under their current strategy.

Notice that 2008 recorded a -5.7% drop in returns whereas the rest of the world suffered drops of 50% or more. This is what we mean when we say reduce tail risks

You can sign up for tickets to their introductory session here.

**If you have always been confused about how to approach the markets**- I
**f you hate reading annual reports or finding trends on charts** **If you just want to be able to consistently push out 10-15% returns per year over the long term, this is the course for you.**

- Masters of Science in Wealth Management, SMU
- Bachelor of Civil Engineering (2nd Upper Class) from NUS

Patrick is a portfolio manager of a systematic hedge fund. He has extensive experience, having spent more than a decade in the asset management and banking industry working through various roles since 2005. These include managing private client portfolios, covering hedge fund clients for equity derivatives products and strategy, product control on derivative and structured products and fund management.

Prior to all these, he started out his first career in the civil service in 2000. After building up his initial savings, he started investing in stocks. From there, he developed a keen interest in financial markets which led him to make a mid-career switch into the finance industry. Gradually, he moved beyond picking stocks to adopting a global macro mind-set covering multiple asset classes. This helped him navigate the 2008 financial crisis successfully. Eventually, he settled on the systematic data driven approach towards investing.

Passing the skeptic litmus test: if the system is so robust and successful, why did the authors not keep the secret sauce to themselves?

Because the market is much bigger than one can take all the profits out of it. U need billions and billions to move markets. If one only has a fraction of it, there’s no harm sharing the information because it won’t affect one’s profitability using the methodology.

Is this for cryptocurrency investors or forex traders?

This if for quantitative investing.