{"title":"债券回报的可预测性:宏观因素和机器学习方法","authors":"Ying Jiang, Xiaoquan Liu, Yirong Liu, Fumin Zhu","doi":"10.1111/eufm.12483","DOIUrl":null,"url":null,"abstract":"<p>We investigate the impact of macroeconomic variables on bond risk premia prediction via machine learning techniques. On the basis of Chinese treasury bonds from March 2006 to December 2022, we show that adding macroeconomic factors improves bond return forecasts and generates higher economic benefits to investors. This is achieved when the nonlinear relationship between macroeconomic variables and bond returns is modelled via machine learning methods. Furthermore, the importance of macroeconomic determinants changes along the yield curve. Our study sheds new light on the information contained in macroeconomic variables for treasury bond valuation and highlights the importance of utilizing appropriate machine learning methods.</p>","PeriodicalId":47815,"journal":{"name":"European Financial Management","volume":"30 5","pages":"2596-2627"},"PeriodicalIF":2.1000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bond return predictability: Macro factors and machine learning methods\",\"authors\":\"Ying Jiang, Xiaoquan Liu, Yirong Liu, Fumin Zhu\",\"doi\":\"10.1111/eufm.12483\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We investigate the impact of macroeconomic variables on bond risk premia prediction via machine learning techniques. On the basis of Chinese treasury bonds from March 2006 to December 2022, we show that adding macroeconomic factors improves bond return forecasts and generates higher economic benefits to investors. This is achieved when the nonlinear relationship between macroeconomic variables and bond returns is modelled via machine learning methods. Furthermore, the importance of macroeconomic determinants changes along the yield curve. Our study sheds new light on the information contained in macroeconomic variables for treasury bond valuation and highlights the importance of utilizing appropriate machine learning methods.</p>\",\"PeriodicalId\":47815,\"journal\":{\"name\":\"European Financial Management\",\"volume\":\"30 5\",\"pages\":\"2596-2627\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Financial Management\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/eufm.12483\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Financial Management","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/eufm.12483","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Bond return predictability: Macro factors and machine learning methods
We investigate the impact of macroeconomic variables on bond risk premia prediction via machine learning techniques. On the basis of Chinese treasury bonds from March 2006 to December 2022, we show that adding macroeconomic factors improves bond return forecasts and generates higher economic benefits to investors. This is achieved when the nonlinear relationship between macroeconomic variables and bond returns is modelled via machine learning methods. Furthermore, the importance of macroeconomic determinants changes along the yield curve. Our study sheds new light on the information contained in macroeconomic variables for treasury bond valuation and highlights the importance of utilizing appropriate machine learning methods.
期刊介绍:
European Financial Management publishes the best research from around the world, providing a forum for both academics and practitioners concerned with the financial management of modern corporation and financial institutions. The journal publishes signficant new finance research on timely issues and highlights key trends in Europe in a clear and accessible way, with articles covering international research and practice that have direct or indirect bearing on Europe.