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About this paper
Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou, Zhanhao Pan
The authors forecast monthly corporate credit spreads by selecting 20 informative signals from a broader economic and financial dataset using mutual information, then stacking predictions from multilayer perceptron, random forest, K-nearest-neighbors, and kernel-ridge models. On 120 monthly observations and one 70/30 holdout, selected-feature stacking reached test R² 0.920, but generalization remains unestablished.
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