Machine Learning Predictions of Credit and Equity Risk Premia

The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in out-of-sample. Parallel measures estimated from firms' stocks fail to predict risk premiums, indicating that credit-market-based risk measures contain valuable information for forecasting firms' risk premia in both markets. The innovative high-volume high-frequency trading has not alleviated short-horizon pricing deviations across firms' equity and credit markets, an epitome of latent arbitrage in the market for credit risk.

• Publication year

  2021

• Venue

  Social Science Research Network

• Publication date

  2021-03-08

• Fields of study

  Business, Computer Science, Economics

• Identifiers
  DOI 10.2139/SSRN.3800205
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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