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Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach
Using a comprehensive return data set and an array of 27 macroeconomic, stock, and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability generated by the iterated combination is both statistically and economically significant. Stock market and macroeconomic variables play an important role in forming expected bond returns. Return forecasts are closely linked to the evolution of real economy. Corporate bond premia have strong predictive power for business cycle, and the primary source of this predictive power is from the low-grade bond premium.
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Preferred citation
Lin, H., Wu, C. & Zhou, G. (2017). Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach. Management Science, 64(9), 1-21. https://doi.org/10.1287/mnsc.2017.2734Publisher DOI
Journal title
Management ScienceVolume
64Issue
9Publication date
2017-05-10Pagination
1-21Publisher
Institute for Operations Research and the Management Sciences (INFORMS)Publication status
PublishedContribution type
ArticleOnline publication date
2017-05-10ISSN
0025-1909eISSN
1526-5501Language
enUsage metrics
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No categories selectedKeywords
Social SciencesScience & TechnologyTechnologyManagementOperations Research & Management ScienceBusiness & Economicspredictabilitycorporate bondsiterated combinationout-of-sample forecastsutility gainsEQUITY PREMIUM PREDICTIONEXPECTED STOCK RETURNSBOOK-TO-MARKETBUSINESS CONDITIONSDIVIDEND YIELDSEXCESS RETURNSRISK PREMIAPREDICTABILITYPERFORMANCEINFLATIONInformation and Computing SciencesCommerce, Management, Tourism and ServicesOperations Research