Can NN-algorithms and macroeconomic data improve OLS industry returns forecasts?*

Christian Pedersen and Stephen Satchell

The European Journal of Finance, 2003, vol. 9, issue 3, 273-289

Abstract: Using an extensive range of macroeconomic indicators and a number of two-stage models mixing OLS and a non-parametric approach known as the nearest neighbour algorithm, the authors analyse the potential for improving forecasts of US industry returns over those built by OLS on industry-specific variables only. Basic performance is measured by the average cross-sectional correlation over time between the 55 forecasted returns and the realized returns across industries. Since investors and asset managers typically want a steady performance over time, the volatility of this cross-sectional correlation is further taken into account in an adaptation of the Sharpe Ratio. Strong evidence is found in favour of certain macroeconomic factors as dominant industry return predictors, and some two-stage models based either purely on OLS or a mix between OLS and the non-linear model can lift both cross-sectional forecasting correlation and Sharpe Ratio. However, viewed overall in relation to the benchmark OLS model, performance is not consistently improved by any particular model.

Keywords: nearest neighbour algorithm; OLS industry returns; forecasting (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1080/1351847032000051244

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