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Machine learning predictions of credit and equity risk premia

Machine learning predictions of credit and equity risk premia
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.
Kita, Arben
fd98ff4d-435a-4b69-8a7f-13171bf5c1fc
Kita, Arben
fd98ff4d-435a-4b69-8a7f-13171bf5c1fc

Kita, Arben (2021) Machine learning predictions of credit and equity risk premia (doi:10.2139/ssrn.3800205).

Record type: Monograph (Working Paper)

Abstract

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.

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Published date: 8 March 2021

Identifiers

Local EPrints ID: 448951
URI: http://eprints.soton.ac.uk/id/eprint/448951
PURE UUID: e39da905-4dbd-421b-a353-51b3efbf93bb

Catalogue record

Date deposited: 11 May 2021 17:11
Last modified: 12 Dec 2021 13:49

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