Institutional investors’ thinking about risk allocation has been dominated for some time by traditional beta sources, mainly long exposure to equities and bonds. The market regime provided little reason for radical change. After all, for two decades, stock and bond returns were negatively correlated, enabling 60/40 equity/bond mixes to deliver both a risk-balanced allocation framework and attractive returns.
That world changed abruptly with the market setbacks of 2022, as both asset classes endured major selloffs—leaving investors less confident that a simple equity/bond mix can provide effective diversification. And with a very different market regime in place, they can’t expect these asset classes to reprise their bull market real returns, either. As a result, alternative return streams are likely to feature more prominently in strategic asset-allocation discussions.
But the world of alternative strategies has been evolving fast in recent years, so before considering an enhanced allocation to alternatives, it’s critical for investors to understand those changes and their implications for portfolio design. Here, we’ll highlight several developments for systematic alternative strategies, identify three ways for investors to avoid crowded trades where returns are becoming more challenged and instead identify areas with more return potential.
Long-Term Signals Becoming Less Effective as Markets Grow More Efficient
Advances in computing power, analytical speed and automated trading are eroding the profitability of well-known longer-term strategies. Information is increasingly becoming available, and the market can price it into assets faster.
Valuation anomalies are being recognized and priced into markets much quicker, with information advantages eliminated much sooner. Strategies with longer-term investment horizons are becoming less effective than strategies focused on short-term anomalies. Investors need to recognize this trend, adapt to have a healthy mix of longer and shorter time-horizon strategies, and seek more evolved, systematic strategies where information advantages aren’t so readily competed away.
For instance, a corporate event-driven strategy seeks to profit from a firm’s changes or announcements that aren’t fully priced by the market immediately. One classic example is earnings surprise—holding shares of companies that have had positive surprises. This strategy used to be profitable for holding periods of up to three months, but the time period before stocks fully incorporated earnings surprise dwindled until the strategy became ineffective. A number of similar but lesser-known or commoditized strategies are still profitable. However, to benefit from these anomalies, investors need a systematic process that can capture and react to announcements very quickly—even so, we’ve seen holding periods shrink from weeks to days. These examples also emphasize the need for efficient trading and execution platforms that enable fast, cheap execution.
Specialization and Localization Can Sharpen a Competitive Edge
In the past, systematic strategies using data from specific markets have often been rolled out globally and (with suitable adjustments) across asset classes. This was a generic way of implementing factor-based strategies that could prove effective across countries and asset classes. However, as markets have become more efficient, it’s now far less likely that one approach will be universally effective. That makes it much more important to apply a specialist focus, pay attention to local distinctions and evaluate systematic strategies with relevant local data.
Institutional investors can increase their edge by identifying areas where strategies are expected to work best, concentrating their allocations in the most promising segments. For instance, some markets are more advanced and sophisticated or more tightly regulated. In these more advanced cases, technology is likely to make markets relatively more efficient, driving down returns. So, it makes sense to diversify by allocating to less sophisticated markets with higher prospective payoffs. And because there are important differences between advanced and less advanced markets, it’s important to choose systematic strategies selectively.
China is a case in point.
The country’s investment markets are among the largest globally but aren’t yet fully mature, so it isn’t effective to try capturing anomalies by applying an advanced-market framework and factors. For example, advanced markets tend to be dominated by institutional money, whereas China’s markets are driven predominantly by retail investors. Advanced markets also have sophisticated regulatory structures overseen by regulators and central banks that are typically independent from governments. China’s regulatory system, in contrast, is less advanced—and the Chinese government can and does intervene in investment markets through the People’s Bank of China.
These distinctions create quite different incentives, so differentiated signals must be captured and exploited by specialized teams well versed in local markets. Sentiment-related factors, as one example, tend to fare better in retail investor–driven markets. By studying local phenomena and using local data and ideas in less advanced markets, investors can select the most relevant factors for their systematic strategies—and may be able to extract higher returns.
Merger arbitrage exemplifies a more complex, idiosyncratic strategy. It aims to profit from corporate merger and acquisition (M&A) activity by evaluating the benefits of prospective deals and the probability that they complete successfully. The core ideas and trading rules underlying merger arbitrage strategies aren’t complicated, and the strategies themselves aren’t intrinsically fast moving. But it takes insight to understand the nuances of various M&A situations and efficiently extract the potential return stream. That skill level isn’t readily commoditized or machine-deployed, so the complexities of M&A can create a moat for well-designed merger arbitrage strategies that can’t easily be surmounted by technology. To make this strategy effective, the key is to use the experience and insights of leading M&A specialist practitioners to enhance the rules-based investment process.
New Technologies Can Create Fresh Insights
Technology advances have eroded the alpha from classic systematic strategies, but technology can be a friend too, as new research and development has continued to push the envelope of new return streams.
Until recently, tech advances were mostly directed toward faster data processing and trade execution. These improvements enabled investors to analyze data much faster—but tended ultimately to make systematic trades less profitable. More recently, technology has identified fresh insights by interrogating vast amounts of data. These insights might include detecting nonlinear trends not readily observable by human fundamental research, or identifying sentiment changes through natural language processing analysis of news media or companies’ reports.
Today, artificial intelligence can achieve new levels of insight and connect multiple data points instantaneously—a task that would previously have taken human analysts days or weeks. These insights are now creating the basis for new factors and systematic ways to apply them profitably.
Although returns from classic systematic strategies may have dwindled in recent years, we believe that systematic approaches can still generate valuable alpha that’s uncorrelated with traditional beta return streams. The key is refocusing on shorter-term strategies, concentrating on specialist approaches in less advanced or nuanced markets, and fully using the latest technologies. With that game plan, investors can open new systematic strategy doors as the old ones are starting to close. In an environment that demands new ways to enhance portfolios, that’s a promising avenue.
