We provide a model-free test for asymmetric correlations in which stocks move more often with the market when the market goes down than when it goes up, and also provide such tests for asymmetric betas and covariances. When stocks are sorted by size, book-to-market, and momentum, we find strong evidence of asymmetries for both size and momentum portfolios, but no evidence for book-to-market portfolios. Moreover, we evaluate the economic significance of incorporating asymmetries into investment decisions, and find that they can be of substantial economic importance for an investor with a disappointment aversion (DA) preference as described by Ang, Bekaert, and Liu (2005). , Oxford University Press.
{"title":"Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation","authors":"Yongmiao Hong, Guofu Zhou, Jun Tu","doi":"10.2139/ssrn.486092","DOIUrl":"https://doi.org/10.2139/ssrn.486092","url":null,"abstract":"We provide a model-free test for asymmetric correlations in which stocks move more often with the market when the market goes down than when it goes up, and also provide such tests for asymmetric betas and covariances. When stocks are sorted by size, book-to-market, and momentum, we find strong evidence of asymmetries for both size and momentum portfolios, but no evidence for book-to-market portfolios. Moreover, we evaluate the economic significance of incorporating asymmetries into investment decisions, and find that they can be of substantial economic importance for an investor with a disappointment aversion (DA) preference as described by Ang, Bekaert, and Liu (2005). , Oxford University Press.","PeriodicalId":250500,"journal":{"name":"Financial Economics & Accounting (FEA) Conferences (Kelley School of Business)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies model uncertainty associated with predictive regressions in asset return predictability research. We comprehensively investigate the performance of Bayesian model averaging (BMA), first introduced to the literature by Avramov (2002) and Cremers (2002), when applied to linear predictive regressions using simulation approaches. We find that, in simple settings, BMA performs fairly satisfactorily even when the true model is not in the model set. It can always identify the powerful predictors and constantly outperform other variable selection methods. The results are robust with respect to non-linearity and prior selections. We confirm that BMA attains best performance when model uncerainty is large, which indicates that it is easier to capture short-run predictability using BMA. However, when we add more structure to the data generating process (DGP), BMA performs less well both insample and out-of-sample. BMA mistakens noise variables for true predictors. This is especially the case when there is a lot of noise in the model set. For out-of-sample prediction, BMA overall model shows little advantage over a no-predictability model, and it tends to under predict. A possible cause could be the complex structure we imposed on the DGP.
{"title":"Asset Return Predictability and Bayesian Model Averaging","authors":"Dragon Yongjun Tang","doi":"10.2139/ssrn.488185","DOIUrl":"https://doi.org/10.2139/ssrn.488185","url":null,"abstract":"This paper studies model uncertainty associated with predictive regressions in asset return predictability research. We comprehensively investigate the performance of Bayesian model averaging (BMA), first introduced to the literature by Avramov (2002) and Cremers (2002), when applied to linear predictive regressions using simulation approaches. We find that, in simple settings, BMA performs fairly satisfactorily even when the true model is not in the model set. It can always identify the powerful predictors and constantly outperform other variable selection methods. The results are robust with respect to non-linearity and prior selections. We confirm that BMA attains best performance when model uncerainty is large, which indicates that it is easier to capture short-run predictability using BMA. However, when we add more structure to the data generating process (DGP), BMA performs less well both insample and out-of-sample. BMA mistakens noise variables for true predictors. This is especially the case when there is a lot of noise in the model set. For out-of-sample prediction, BMA overall model shows little advantage over a no-predictability model, and it tends to under predict. A possible cause could be the complex structure we imposed on the DGP.","PeriodicalId":250500,"journal":{"name":"Financial Economics & Accounting (FEA) Conferences (Kelley School of Business)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116705366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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