Alternative Thinking

This article updates AQR’s earlier analysis on inflation sensitivities and inflation protection, highlighting the benefits of commodities and trend following strategies and how to evaluate different implementations. It asks, “How should you prepare for the possibility of another global inflation shock?”

Total portfolio approach (TPA) is a broad philosophy for institutional portfolio management that has become increasingly popular over the last few years. We explore potential benefits and risks of TPA when compared to traditional strategic asset allocation, and offer practical demonstrations of how TPA might help investors achieve their goals.

We update our estimates of medium-term (5- to 10-year) expected returns for major asset classes. We also include a discussion on currency risk and currency hedging, with particular considerations for U.S. and European investors.

We examine the popular “Buy the Dip” strategy and find it consistently underperforms a simple buy-and-hold approach. Our research shows that investors seeking to time markets may find greater success following trends rather than fighting them.

This paper explores how capital-efficient investments—such as private equity, hedge funds, and portable alpha—can help investors unlock the full benefits of diversification without relying on direct leverage. We show how these approaches can improve portfolio resilience and long-term returns, particularly for leverage-constrained investors.

Streaky return streams - ones that that can perform well or poorly for extended periods - are challenging for investors to comprehend and stick with. Yet, this very "complexity risk” may be what earns investors an additional risk premium, leading to above average risk-adjusted returns.

We update our estimates of medium-term (5- to 10-year) expected returns for major asset classes. We also include a discussion on corporate earnings growth: the market consensus is for more strong growth to come – especially in the U.S. But what is a reasonable medium-term forecast for allocators?

In the second issue of our 2024 Alternative Thinking series, we showed that machine learning techniques can be used to help improve market timing strategies. In this issue, we extend these concepts to constructing stock selection strategies following a similar framework. Our results indicate more complex models utilizing machine learning techniques yield performance improvements relative to a simple, linear approach in the range of 50-100%, suggesting that machine learning can help to build better stock selection portfolios.

This paper presents one justifiable set of inputs and finds that alternatives earn themselves a sizable strategic allocation. Investors are encouraged to compare these results with their own assumptions, constraints and allocations as they look to build a resilient portfolio for long-term investment success.

Common wisdom has suggested that small, simple models are best suited for market timing applications, given finance’s “small data” constraint and naturally low predictability. However, we show that complex models better identify true nonlinear relationships and therefore produce better market timing strategy performance. We validate this "virtue of complexity" result in three practical market timing applications.