Focusing on the out-of-sample predictive power of the federal funds rate (FFR) for equity premiums, the author analyzes the profitability and economic significance of dynamic trading strategies that rely on this forecasting relationship.

The theory that expansionary monetary policy has a positive effect on the stock market—and vice versa—has been discussed and analyzed by academics and industry participants for several years. The author notes that the focus of the growing literature has been the predictability of the equity premium from such factors as aggregate financial ratios, bond yield spreads, and macroeconomic variables. So far, little attention has been given to the FFR, which is an omission that the author seeks to correct.

He constructs several trading strategies across different asset classes to test the relationship between equity premiums and the FFR. For all these dynamic strategies, the allocation to riskier assets is increased following a decrease in the FFR, and vice versa. The results of the author’s analysis reveal that the FFR is useful in building stock market–timing investment strategies. All the defined strategies dominate a buy-and-hold strategy that holds the stock index with leverage.

To analyze the predictive power of changes in the FFR on equity premiums, the author focuses on defining multiple trading strategies used to test the relationship between the changes in the FFR and the equity premiums.

The key defined trading strategies fall into two groups: nonparametric market-timing strategies and parametric trading strategies based on predictive regression.

The author constructs two types of equity market-timing strategies based on the FFR. The first strategy uses a nonparametric rule based on the magnitude and sign of the change in the FFR. It goes long the stock index when the change in the FFR is negative and above a threshold. It goes short the stock index and invests the proceeds in a risk-free asset when the change in the FFR is positive and above the threshold.

The second strategy is a parametric strategy—based on an out-of-sample predictive regression of the market equity premium on the change in the FFR— that goes long the stock index when its forecast excess is positive. When the forecast excess return is negative, the strategy shorts the index and invests proceeds in the risk-free asset.

The parametric (regression-based) strategy produces an annual Sharpe ratio of 0.39 (versus 0.32 for the buy-and-hold strategy), and the nonparametric market-timing strategy produces an annual Sharpe ratio of 0.55 (versus 0.41 for the passive strategy).

The author, using both parametric and nonparametric analyses, defines two rotation trading strategies for equity portfolios. For both approaches, when monetary policy is expansionary, the strategy goes long the portfolio with the greatest average return (within a certain class) and short the portfolio with the lowest average return. These rotation strategies produce a range of annual Sharpe ratios from 0.44 to 0.78.

The author, by providing multiple strategies and detailed explanations of these strategies, makes his research easily accessible to anyone who would like to replicate the study or modify the defined strategies. The key reason for the research is the dearth of academic studies on the impact of the FFR on equity premiums. It would have been helpful to have a view of how returns from strategies based on the FFR compare with those of other predictors that have received more attention (such as aggregate financial ratios, bond yield spreads, and macroeconomic variables on equity premiums).

Nevertheless, the quality of the research makes it useful to both market participants and academics who may be interested in assessing the impact of monetary policy on stock markets and developing strategies to manage the effects of changes in monetary policy on stock market prices.