Year-ahead commentary is challenging for us: We don’t have a crystal ball, and we believe predictions contribute more to investment mistakes than outperformance. But the New Year does offer opportunity to consider recent experiences in long-run strategic terms.
(If our look ahead to 2026 sounds similar to what we published in 2025, 2024, 2023, and 2022, it’s because we believe in a consistent approach to asset management. Although our philosophy is consistent, the themes for the coming year are fresh – as you’ll see below.)
Looking back on our 2025 commentary, we’ve identified three key 2026 risk factors and ways to manage them:
- Bonds: Amid the usual considerations around interest rates, the eternal truth holds: Focus on downside risk mitigation.
- Stocks: The major US indices remain heavily concentrated in a few dominant technology firms and are trading at multiples not seen since prior bubbles.
- Artificial Intelligence & Investing: Making investment decisions with the help of A.I. needs some demystification for decerning investors.
Bonds: Invest for Yield or Risk Mitigation?
For typical individual investors, fixed income’s goal is not to maximize return but instead to preserve capital when equity markets are stressed. When investors forget this and use fixed income to target returns instead of resilience, trouble can follow. Higher yields in fixed income are almost always compensation for additional risks such as credit, liquidity, leverage, or duration. In volatile markets, these risks can undermine fixed income’s diversification benefit.
While many market participants are trying to make sense of a rising unemployment rate and uncertainty about how well inflation is contained, we continue to believe in systematic risk management that focuses on the downside.
In this framework, yield becomes a benefit—not the objective—ensuring the fixed-income sleeve strengthens, rather than compromises, the overall portfolio.
Stocks: Concentration risk & high P/E ratios – What, me worry?
Early in the year we considered what would happen if the Magnificent 7 don’t stay magnificent. We enter 2026 with a similar setup. The Magnificent 7 retain about a 30% share of the S&P 500’s market cap, and are valued at roughly 8 times forward sales, per Yardeni Research. Market concentration risk aside, elevated valuation ratios are often cited as a headwind to future returns. In reality no one can reliably predict where markets are headed: Valuations could just as easily become significantly more expensive over the next decade as they could become meaningfully cheaper.
That uncertainty is precisely why diversification matters. History may offer little justification for market-leading stocks trading at 45x, 30x, or even 15x earnings today—but markets are not bound by historical precedent. Diversifier strategies that systematically adapt to
changing market conditions can help manage risk, particularly if valuations ultimately revert toward their historical averages.
Investors with concerns about US stock market downside can use a long-short strategy like our Tactical Equity Fund to manage broad market risk. Investors interested in stock market exposure, but who would like to diversify their portfolios may want to look at the Counterpoint Quantitative Equity ETF, whose unique approach and equal weighting dramatically limits exposure to Magnificent 7 stocks.
As the Magnificent 7 have continued to meet investors’ expectations, a “junk rally” in less-profitable, speculative stocks has also emerged. We believe such rallies eventually subside, and when they do, long-short strategies tend to profit from a correction in junk stocks’ mispricing.
When contemplating the future of US stock market risk, it’s important to remember that not all diversifier strategies are created equal. Advisors must weigh liquidity, correlation, and volatility considerations when considering which diversifiers will best serve clients.
Artificial Intelligence & Investing: I’m sorry I can’t do that Dave.
Artificial intelligence has become a widely used term in investment management, but not all A.I.-driven strategies are built the same. At its core, A.I. is a prediction engine—one that learns from historical data to estimate what may happen next. While this technology can be powerful, effectiveness depends on model design, data inputs, and the interpretation of outputs. Investors should be wary of treating all A.I. solutions as interchangeable. Superficial similarities in branding can mask meaningful differences in methodology and rigor.
The most durable A.I. strategies also recognize the importance of human oversight. Models are only as good as their inputs. Thoughtful variable selection grounded in economic logic is essential to avoid chasing noise or statistical coincidences. Rather than replace judgment, effective A.I. augments it—combining human insight with
machine-driven pattern recognition. For investors, the key is to understand how an A.I. strategy is built, why its inputs were chosen, and how its predictions are governed.
Conclusion
We believe bold predictions often leave investors worse off. On the other hand, our strongest belief is in systematic processes that prepare portfolios for many scenarios.
Low-correlation systematic strategies can have the paradoxical effect of freeing investors from focusing too heavily on big drivers of traditional asset class performance – things like interest rate regime change, the AI revolution, and global conflict. When a portion of a portfolio includes systematic alternatives, an investor can afford to be a little less focused on making big predictions, a little humbler about what we actually know about the future, and a lot more focused on achieving favorable long-term results.