It is often said that unconstrained managers are able to adopt a ‘go anywhere’ approach, which differ from more traditional ‘naïve’ strategies that tend to be very constrained by close alignment to a benchmark or reference index. However, unconstrained does not mean undisciplined – the investment process is typically backed by rules-based methodology, even in the discretionary space. The positive difference can be further amplified by systematic trading programmes, which can seek excess returns across a broad spectrum of asset classes and risks, expressing both long and short conviction. Since systematic portfolios are more broadly diversified, they typically offer a smoother return profile and greater downside insulation in the event of a sustained selloff.
In the simplest terms, dispersion in outcomes helps to create opportunities. Dispersion happens when prices of different assets move differently rather than all rising or falling with the tide. It is a phenomenon that over the last decade, until recently, has been in short supply. Hence, naïve and passive strategies have done well in recent years as the liquidity provided by central bank quantitative easing created a rising tide that has lifted all boats. With assets moving in lockstep, all that investors needed to do was ‘buy the market’ and trust that prices would keep rising. We are now entering a much more interesting environment for unconstrained strategies, with dispersion and divergence evident in the path of monetary policy across the US, Europe and Japan. This is also true of economic growth rates and inflation. Differentials and fundamentals are becoming increasingly pertinent – this drives price dispersion and investment opportunity.
We find investors are beginning to appreciate that equities and risk assets in general are priced for perfection - continued stable growth without much in the way of interruption. That could prove to be fine, but there is risk that growth slows for one of many reasons including trade protectionism, wage inflation and political risk. There is a clear reticence to add to equity risk in general today. Meanwhile, it is difficult for investors to be too optimistic over the outlook for bonds unless they think interest rates are going to fall to -4% or -5%. It is all but a mathematical certainty that investors will get lower returns from their bond exposures in the future than they have become accustomed to in recent decades. So, investors see equities as pretty fully priced and bonds as generally expensive. This places additional emphasis on the investor need for diversification of return drivers. A diversified systematic macro programme or an unconstrained multi-asset approach can complement a traditional equity portfolio, while alternative risk premia strategies typically offer steady, incremental returns, making them an attractive source of ‘bond replacement’ though more akin to corporate bonds than sovereign bonds. It is no surprise that these approaches have rapidly gained in popularity in this environment.
Discretionary strategies are typically characterised by a relatively small number of positions, any of which can exert a significant influence on the overall performance of the portfolio – this is especially true in the case of truly unconstrained discretionary managers. Conversely, systematic programmes typically invest in a larger array of positions, asset classes and opportunities where research has identified a statistical edge. Since the source of returns are more divergent, the performance profile of a systematic trading programme often exhibits a low correlation to a discretionary strategy, thus improving efficiency in a portfolio context. Think of the human advantage being in selecting the one or few excellent ideas and the systematic advantage being in selecting hundreds of good ideas at the same time.
As a systematic or rules-based manager, data is at the heart of our investment process and drives all of the trades we make. However, it is essential to point out that more data does not necessarily mean more informational value. We have access to a large variety of data sources. These can range from price data to fundamental data like earnings quality to newer data types like information about what companies are shipping around the world, or how full a shopping mall parking lot is. For macro investing, we can use satellite and weather data to predict crop yields and track power or gas network demand in real time. Of course not all data is useful for investment. The real skill is in being able to sift through large amounts of data to find the signal value and then being able to trade the right markets to capitalise on our information edge for our clients.
In our multi-asset portfolios, we look across the liquid asset classes of equities, fixed income, commodities and currencies. In our global equities portfolio, we look at about 15,000 stocks globally and our investable universe contains approximately 3,000 stocks. We have a number of quality and value indicators which are constructed from financial statements (balance sheets, earnings statements, etc); technical or price based data is used to identify momentum, sector rotations and low risk effects; while we user newer data types to assess signal value from sell-side analysts’ forecasts and news. Combining big data with our sophisticated analytical tools, often now called machine learning, means we can have an investment view on thousands of stocks rather than the tens of stocks a typical discretionary manager might invest in.
Being a rules-based manager means that our risk management is unwavering and rigorous. Risk management runs throughout our processes and is very multi-dimensional. We control risk to the usual things like company, sector and country, but then also to factors, to trading costs and to crisis scenarios. One obvious strength of the systematic approach is that a trading programme is never going to change its mind in the heat of the moment. As such behavioural risk is lowered or even nullified.
The information in this document is given for information purposes only and does not qualify as investment advice. Opinions and assessments contained in this document may change and reflect the point of view of GAM in the current economic environment. No liability shall be accepted for the accuracy and completeness of the information. Past performance is no indicator for the current or future development.