To be a successful active investor, it is important to have a clear, big picture overview of our portfolio of assets and strategies. It is all too easy to get bogged down in the details of strategies and their execution only to be blindsided by a change in the market environment, which may turn an entire strategy into a losing proposition.
A better way to actively manage our portfolio composition, to optimize its return and exposure to risk, can be achieved by taking the current market regime into account.
To make this process objective and repeatable, we can develop models according to which we adjust our portfolio´s exposure to the market: leveraged or reduced exposure, net long or short and which specific strategies to use in any given environment.
In essence the development of this “Meta Strategy” is about concentrating exposure at opportune times and reducing risky strategies when probabilities for negative events rise – in contrast to running a portfolio of diversified assets and strategies continually.
I will build this model around the S&P 500 as a benchmark, because equity risk is the main driver of return in most portfolios. U.S. large caps represent economic conditions best and the availability and quality of the data around the U.S. economy is unparalleled.
- A quick, easy-to-read summary of the Meta Strategy model can be found here.
- This in-depth article series first lays out the foundations in detail here and here,
- it then goes on to describe a system to gradually change exposure to different assets and strategies by combining different kind of indicators,
- finally I analyze the technical and fundamental indicators I use for the Meta Strategy.
- The entire strategy is laid out in building blocks that can easily be adjusted or exchanged to suit individual preferences and to incorporate future research.
What is the main objective?
If we could manage to get out of the equity market during severe declines and be long and leveraged otherwise, it would be easy to ignore short term gyrations and simply sit them out.
And, if we could manage to avoid some of the bigger corrections and unexpected drops, like the one we just witnessed at the end of 2018, we would probably be as close to the holy grail of investing as we are ever likely to get.
The biggest challenge is to judge the point at which it makes sense to get out of the market to be on the safe side (when to take emerging volatility seriously and when to disregard it as noise) – as well as when to get back into risky assets and strategies to avoid giving up too much upside.
What would I like a Meta Strategy to achieve?
What I personally would like to achieve is to rotate my core portfolio between different asset classes and also to gradually adjust exposure to different strategies according to market conditions relying on quantified data.
This should lead to higher risk-adjusted returns over time.
I want to concentrate on using my most efficient strategies when the probability for success is highest and wait patiently on the sidelines or be minimally invested, whenever chances are not so good. The model is supposed to tell me exactly where we are at any given moment.
This objective gives the Meta Strategy a tilt in preferences: Because I can use additional, unconstrained strategies that are much more profitable in good times than the equity market itself, but carry higher crash risk, my focus is on downside protection. I don’t mind sitting on the sidelines frequently, because my strategies can easily make up for missed opportunities quickly, if they steer clear of severe drawdowns.
Extraordinarily profitable strategies in good times
In addition to a core tactical ETF portfolio, that rotates between risky, save and alternative assets using the inputs of the model, I want to run unconstrained strategies, that may invest with leverage, derivatives, short exposure and in unconventional “asset classes”, for example, volatility.
Negative skew strategies can have extraordinarily high Sharpe ratios (risk-adjusted returns) in good times, but inevitably combine this with a high crash risk – rare, but severe.
Examples for such strategies are:
- Leveraged equity exposure
- Short volatility strategies
- other strategies with a similar return-to-risk profile
One method to reap the rewards while mitigating the risks, is to balance these strategies with positively skewed strategies (e.g. trend following, hedging, etc.). However in this article series I want to look at using them in a very concentrated fashion – but only in the most suitable environment. Both ideas have advantages and disadvantages and it would be best to integrate both in a good, active portfolio.
Next up, I will analyze the foundations for building the Meta Strategy. Looking at many possible indicators, fundamental as well as technical, I want to see, if there is any evidence, that adapting a portfolio to market conditions can be successful?