There are many stories about legendary alpha generators like George Soros and John Paulson but each likely rose to fame with just one trade. For Soros it was his bet against the British pound which broke the Bank of England in 1992 while Paulson rose to prominence by shorting the US subprime market at the peak of the credit bubble just prior to the Global Financial Crisis.
While both a great stories—Paulson’s case is seen to have helped inspire a Hollywood movie—institutional investors today generate alpha in a different way. Soros and Paulson made money in the traditional way, but the industry has moved on to modern alpha.
Modern alpha relies on multiple sources and is therefore more stable and recurring than the traditional “big bets”. For most investors, the main source of alpha is fundamental research. But to add stability it is plausible to combine fundamental research with quantitative strategies as an additional alpha source.
Quantitative strategies are not calibrated to maximise performance but rather deliver the best risk-adjusted returns throughout a cycle. They rely on low maximum drawdowns and relatively high Sharpe ratios, and the alpha is made portable through the use of derivatives to any portfolio in need of extra performance. An example of such a strategy is our Smart Carry (SC), which is a credit default swap (CDS)-based systematic strategy used for harvesting the credit risk premia in fixed income or multi-strategy portfolios.
We execute the strategy via the CDX North America Investment Grade index, which is a diversified universe consisting of 125 issuers. The index offers significantly more liquidity than bonds which is one of the key requirements for quantitative strategies in order to minimise transaction costs. In addition, the index offers lower default risk than traditional portfolios as every six months weaker issuers are required to leave the index and allow stronger issuers to move in.
SC relies on "carry" as the main performance factor and seeks to neutralise risk ahead of major volatility shocks. The strategy is designed to be either long or neutral on credit risk and uses one of three models—multi-feature model, autoregressive integrated moving average (ARIMA) model and spread momentum model—at each point in time, subject to the validity of pre-determined conditions.
Every quantitative strategy starts with back testing and the creation of a statistic data package to allow the development team to decide if the strategy is ready to go “live”. But the development team cannot assess the real validity of a strategy in normal market conditions until it actually goes live and comes up against constrained liquidity and unforeseen events. SC has proven over the last two years that it is able to perform during periods of market volatility caused by such events as the COVID-19 pandemic and the war in Ukraine.
As mentioned, SC is powered by three different models. Most of the time signals rely on a multi-feature model so that a high number of market factors can be used to generate a signal. However, we found that during certain market periods the forecasting power of the model dropped to unacceptable levels and a rule-based switch to an ARIMA model took place. If the latter is not able to meet certain pre-conditions, a spread momentum model is used as a decision maker. SC will switch back to the multi-feature model when its reliability recovers.
The reliance on a family of models rather a single one adds to our desire to deliver a stable string of returns, as can be seen with our back testing as well as “real life” results.
A key advantage of portable alpha strategies is the flexibility they offer to institutional clients in finding customized solutions. SC can be attached to any portfolio, and the potential alpha can be scaled up or down depending on the client's risk budget. Changes to the risk profile can be applied with ease given the breadth and liquidity of the CDX market.
While modern alpha may not create the same Hollywood-worthy stories as traditional alpha, it delivers higher Sharpe ratios with minimal human biases or errors. Quantitative strategies have been a great add-on to the range of alpha sources we use in portfolio construction, given its stability, flexibility as well as portability.
There can be no assurance that any performance will be achieved in any given market condition or cycle.
Past performance or any prediction, projection or forecast is not indicative of future performance.
Any comparison to a reference index or benchmark may have material inherent limitations and therefore should not be relied upon.