Investor sentiment indicates how far an asset value deviates from its economic
fundamentals. In this article, we review various measures of investor sentiment based on …

In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict
oil price returns with a large set of predictors. The out-of-sample results indicate that the …

This paper proposes a novel supervised learning technique for forecasting: scaled principal
component analysis (sPCA). The sPCA improves the traditional principal component …

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 …

Recently, there has been a fast-growing literature on the determinants of corporate bond
returns, in particular, the driving force of cross-sectional return variation. In this review, we …

To improve the predictability of crude oil futures market returns, this paper proposes a new
combination approach based on principal component analysis (PCA). The PCA combination …

In this paper, we employ an iterated combination approach to examine oil price predictability
with a large set of predictors, including 18 macroeconomic variables and 18 technical …

The goal of our paper is to improve the accuracy of stock return forecasts by combining new
technical indicators and a new two-step economic constraint forecasting model. Empirical …

We find that combining de-noising stock returns by wavelet transform with new proposed
technical indicators can significantly improve the accuracy of stock returns forecasts, in …

Studies of bond return predictability find a puzzling disparity between strong statistical
evidence of return predictability and the failure to convert return forecasts into economic …