We present a Composite Leading Indicator Model for the US market which focuses on absolute return. The model uses five sub-strategies that each trade a well know leading macroeconomic-indicator. For illustrative reasons and to include signal diversification we also employ trend or momentum overlays on some of the sub-strategies. Overall, we are satisfied with the outcomes of this initial model driven by economic reasoning as it outperforms consistently on a risk-adjusted basis since 1960.

We revisit ReSolve Asset Management's study from 2016 on adaptive asset allocation for a broad ETF universe. We show that only the momentum strategy outperformed baselines post-publication.

In June 2023, we constructed and published our machine-learning recession model which alerted to heightened risk. As of October 2023, the model still flags a recession warning. In this update, we provide a quick summary of the current signal.

In our quest to find out if an investor can successfully navigate US recessionary periods, we construct systematic allocation strategies - easily implemented with ETFs - that use our estimated recession probabilities from our machine learning model.We find that a few straightforward strategies could potentially help investors steer through difficult macroeconomic environments

We explore a systematic bond strategy named Heine’s bond trading model. We find that it beats the benchmark on downside risk, especially during the 2022 crisis, but achieves relatively the same on other metrics. We also try a simpler trend-following system, and although it underperforms the two previous strategies, it does a good job of reducing max drawdown while being much less complex to implement.

In the ever-changing landscape of the economy, the topic of recession risk looms large, capturing the attention of both investors and economists alike. To shed light on this pressing issue, we have devised a machine-learning approach to estimate recession risk using logistic modeling.

We present a straightforward systematic investing strategy that uses hidden Markov regimes to switch between risky equities and cash. We show how this beats both buy-and-hold and simple trend-following strategies.

The PAA (Protective Asset Allocation) portfolio is a Global Tactical Asset Allocation model using a multi-asset universe. It can be easily implemented through ETFs. The strategy uses an innovative dual momentum framework and originated in the paper of Keller and Keung in early 2016. We revisit its performance seven years after publication and still find it to be a very interesting alternative to the traditional 60/40 stock/bond portfolio.

We predict medium-to-long-term equilibrium exchange rates (using a BEER model) for developed currencies and determine their deviation from fair value levels. We introduce systematic value strategies that are able to exploit fair value deviations and consistently achieve high Sharpe ratios out-of-sample.