Forecasting crude oil prices: A scaled PCA approach

In this paper, we employ a novel dimension reduction approach, the scaled principal component analysis (s-PCA), to improve the oil price predictability with technical indicators. The empirical results show that the s-PCA model outperforms various competing models both in- and out-of-sample. From a market timing perspective, an oil futures investor can realize a larger Sharpe ratio using the s-PCA approach than using the competing models and Buy-and-Hold strategy. Furthermore, we investigate the driving forces behind the superior performance of the s-PCA model from a loading perspective. We illustrate that the s-PCA model can identify technical indicators with strong predictive power and put relatively large loadings on them when constructing diffusion indexes. Finally, our results are robust to a series of settings.