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This paper studies the application of machine learning in extracting themarket implied features from historical risk neutral corporate bond yields. Weconsider the example of a hypothetical illiquid fixed income market. Afterchoosing a surrogate liquid market, we apply the Denoising Autoencoderalgorithm from the field of computer vision and pattern recognition to learnthe features of the missing yield parameters from the historically implied dataof the instruments traded in the chosen liquid market. The results of thetrained machine learning algorithm are compared with the outputs of a point in-time 2 dimensional interpolation algorithm known as the Thin Plate Spline.Finally, the performances of the two algorithms are compared.

Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)