Evaluating the Performance of Nearest Neighbour Algorithms when Forecasting US Industry Returns

Using both industry-specific data on 55 US industry sectors and an extensive range of macroeconomic variables, the authors compare the performance of nearest neighbour algorithms, OLS, and a number of two-stage models based on these two methods, when forecasting industry returns. As industry returns are a relatively under-researched area in the Finance literature, we also give a brief review of the existing theories as part motivation for our specific choice of variables, which are commonly employed by asset managers in practice. Performance is measured by the Information Coefficient (IC), which is defined as the average correlation between the 55 forecasted returns and the realised returns across industries over time. Due to transaction costs, investors and asset managers typically want a steady outperformance over time. Hence, the volatility of IC is taken into account through the application of “Sharpe Ratios”. We find that two-stage procedures mixing industry-specific information with macroeconomic indicators generally outperform both the stand-alone nearest neighbour algorithms and time-series based OLS macroeconomic models.