Short-Term Interest Rates and Stock Returns: A Test of Predictive Power and Market Efficiency

Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

^* Corresponding author: Yang Hong: yanghong@unc.edu

Abstract

Using weekly data from 2015 to 2025, this study examines the predicted link between short-term federal funds rates and stock market performance. Specifically, this paper examines whether changes in the U.S. federal funds rate can forecast returns on the S&P 500 index. To construct a forward-looking interest rate variable, the paper applies an ARIMA (AutoRegressive Integrated Moving Average) model to generate one-step-ahead forecasts of interest rates. These predicted rates are then used in regression analysis and Granger causality tests to assess their impact on weekly stock returns. Although some lagged relationships appear statistically significant, the overall results support the Efficient Market Hypothesis (EMH), which suggests that public information like interest rates is rapidly absorbed into asset prices. Consequently, interest rate changes do not consistently improve return prediction. By concentrating on high-frequency data and validating the short-term forecasting potential of monetary policy indicators in highly efficient financial markets, this findings add to the body of work.