{"title":"Time-varying relative risk aversion: Theoretical mechanism and empirical evidence","authors":"Xuan Liu , Haiyong Liu , Zongwu Cai","doi":"10.1016/j.jempfin.2024.101535","DOIUrl":"10.1016/j.jempfin.2024.101535","url":null,"abstract":"<div><p>This paper explores the issue of understanding time-varying relative risk aversion with household-level data on two classical portfolio choice problems. First, we derive an analytic form solution to a parsimonious portfolio choice model with the preference given by Greenwood, Hercowitz and Huffman (1988, GHH), and then, the solution identifies four partial equilibrium effects in our model with the GHH preference on risky shares through two channels and two net effects whose signs hinge on the value of a key structural parameter. Based on household-level data, our empirical results from both mean and quantile regression models show clearly that wealth negatively affects risky shares and the estimated effects are statistically significant and robust, which is in line with the theory. Finally, we show that the GHH preference alone is not sufficient in explaining how risky shares respond to labor income in the household-level data.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101535"},"PeriodicalIF":2.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yufeng Han , Yueliang (Jacques) Lu , Weike Xu , Guofu Zhou
{"title":"Mispricing and Anomalies: An Exogenous Shock to Short Selling from JGTRRA","authors":"Yufeng Han , Yueliang (Jacques) Lu , Weike Xu , Guofu Zhou","doi":"10.1016/j.jempfin.2024.101537","DOIUrl":"10.1016/j.jempfin.2024.101537","url":null,"abstract":"<div><p>We investigate the causal impact of short-sale constraints on market anomalies by analyzing a comprehensive set of 182 anomalies. Our approach leverages a persistent, robust, and plausibly exogenous shock to short-selling supply caused by the dividend tax law change in the Job and Growth Tax Relief Reconciliation Act (JGTRRA) of 2003. Our findings reveal that anomalies decline after JGTRRA. However, this tax law change impedes arbitrageurs’ ability to correct mispricing, resulting in anomalies decaying less following dividend record months compared to other months post-JGTRRA. Furthermore, this effect is concentrated on overpriced stocks as opposed to underpriced stocks. Interestingly, while this shock significantly affects most types of anomalies, valuation anomalies remain unaffected.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101537"},"PeriodicalIF":2.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The risk–return tradeoff among equity factors","authors":"Pedro Barroso , Paulo Maio","doi":"10.1016/j.jempfin.2024.101518","DOIUrl":"10.1016/j.jempfin.2024.101518","url":null,"abstract":"<div><p>We examine the time-series risk–return tradeoff among equity factors. We obtain a positive tradeoff for profitability and investment factors, which is consistent with the APT. Such relationship subsists when we control by the covariance with the market factor, which represents consistency with Merton’s ICAPM. Critically, we obtain an insignificant risk–return relationship for the market and other factors. The tradeoff is weaker among international equity markets. The out-of-sample forecasting power tends to be economically significant for the investment and profitability factors. Our results suggest that the risk–return tradeoff is stronger within segments of the stock market than for the whole.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101518"},"PeriodicalIF":2.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using the Bayesian sampling method to estimate corporate loss given default distribution","authors":"Xiaofei Zhang, Xinlei Zhao","doi":"10.1016/j.jempfin.2024.101540","DOIUrl":"10.1016/j.jempfin.2024.101540","url":null,"abstract":"<div><p>We use Markov chain Monte Carlo (MCMC) sampling to draw model coefficients to generate LGD distributions. We find that applying this Bayesian method on a sophisticated model, such as the zero-one-inflated beta (ZOIB) model, that accounts for the bi-modal distribution of the LGDs can generate LGD distributions that mimic the observed distributions well. By contrast, applying this Bayesian sampling approach on a simple model such as Tobit cannot capture the bi-modal LGD distributions accurately. Finally, we argue that this Bayesian sampling approach to generate LGD distributions is better fit for the stress testing purpose than the typical approach to estimate LGD model coefficients and then stress the macro variables. The latter approach yields stressed LGDs that may not be conservative enough, even if the macro variables are stressed to their worst historical values.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"79 ","pages":"Article 101540"},"PeriodicalIF":2.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimation and inference in low frequency factor model regressions with overlapping observations","authors":"Asad Dossani","doi":"10.1016/j.jempfin.2024.101536","DOIUrl":"10.1016/j.jempfin.2024.101536","url":null,"abstract":"<div><p>A low frequency factor model regression uses changes or returns computed at a lower frequency than data available. Using overlapping observations to estimate low frequency factor model regressions results in more efficient estimates of OLS coefficients and standard errors, relative to using independent observations or high frequency estimates. I derive the relevant inference and propose a new method to correct for the induced autocorrelation. I present a series of simulations and empirical examples to support the theoretical results. In tests of asset pricing models, using overlapping observations results in lower pricing errors, compared to existing alternatives.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101536"},"PeriodicalIF":2.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tone or term: Machine-learning text analysis, featured vocabulary extraction, and evidence from bond pricing in China","authors":"Yueqian Peng , Li Shi , Xiaojun Shi , Songtao Tan","doi":"10.1016/j.jempfin.2024.101534","DOIUrl":"10.1016/j.jempfin.2024.101534","url":null,"abstract":"<div><p>We apply the machine-learning technique proposed by Zhou et al. (2024) to analyze credit rating reports in China’s bond markets, identifying featured vocabulary and generating text analysis scores. Compared with the traditional bag-of-words text analysis, evidence suggests three advantages of machine-learning scoring. Firstly, it covers featured vocabulary that compensates for missing information; secondly, it reduces misclassification of words’ sentiments; moreover, it mitigates the problem of equal weighting inherent in the bag-of-words method. Our findings indicate that the featured vocabulary neglected in the bag-of-words method plays a crucial role in text analysis and significantly contributes to bond pricing. Additionally, we find that machine-learning text analysis can address AAA rating inflation within China’s bond markets to some extent. In contrast, the bag-of-words method exhibits limited efficacy in mitigating this issue.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101534"},"PeriodicalIF":2.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Persistent and transient variance components in option pricing models with variance-dependent Kernel","authors":"Hamed Ghanbari","doi":"10.1016/j.jempfin.2024.101531","DOIUrl":"10.1016/j.jempfin.2024.101531","url":null,"abstract":"<div><p>This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"79 ","pages":"Article 101531"},"PeriodicalIF":2.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927539824000665/pdfft?md5=8487280d2ffab15b3ad43290e53104ee&pid=1-s2.0-S0927539824000665-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonas Bartl , Denefa Bostandzic , Felix Irresberger , Gregor Weiß , Ruomei Yang
{"title":"The 2008 short-selling ban’s impact on tail risk","authors":"Jonas Bartl , Denefa Bostandzic , Felix Irresberger , Gregor Weiß , Ruomei Yang","doi":"10.1016/j.jempfin.2024.101532","DOIUrl":"10.1016/j.jempfin.2024.101532","url":null,"abstract":"<div><p>We examine how the 2008 U.S. short-selling ban on the stocks of financial institutions impacted their equity tail risk. Using propensity score matching and difference-in-difference regressions, we show that the ban was not effective in restoring financial stability as measured by the stocks’ dynamic Marginal Expected Shortfall. In contrast, especially large institutions, those who were most vulnerable to market downturns in the preban period, as well as those equities with associated put option contracts, experienced sharp increases in their exposure to market downturns during the ban period, contrary to regulators’ intentions.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101532"},"PeriodicalIF":2.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927539824000677/pdfft?md5=84fba35aebc9f925ab99427461c04e0b&pid=1-s2.0-S0927539824000677-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain","authors":"Simon Trimborn , Hanqiu Peng , Ying Chen","doi":"10.1016/j.jempfin.2024.101529","DOIUrl":"10.1016/j.jempfin.2024.101529","url":null,"abstract":"<div><p>Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101529"},"PeriodicalIF":2.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927539824000641/pdfft?md5=4096f472519c9f96a00ba2d6ac6cbda5&pid=1-s2.0-S0927539824000641-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Big portfolio selection by graph-based conditional moments method","authors":"Zhoufan Zhu , Ningning Zhang , Ke Zhu","doi":"10.1016/j.jempfin.2024.101533","DOIUrl":"10.1016/j.jempfin.2024.101533","url":null,"abstract":"<div><p>This paper proposes a new <u>gra</u>ph-based <u>c</u>onditional mom<u>e</u>nts (GRACE) method to do portfolio selection based on thousands of stocks or even more. The GRACE method first learns the conditional quantiles and mean of stock returns via a factor-augmented temporal graph convolutional network, which is guided by the set of stock-to-stock relations as well as the set of factor-to-stock relations. Next, the GRACE method learns the conditional variance, skewness, and kurtosis of stock returns from the learned conditional quantiles via the quantiled conditional moment method. Finally, the GRACE method uses the learned conditional mean, variance, skewness, and kurtosis to construct several performance measures, which are criteria to sort the stocks to proceed the portfolio selection in the well-known 10-decile framework. An application to NASDAQ and NYSE stock markets shows that the GRACE method performs much better than its competitors, particularly when the performance measures are comprised of conditional variance, skewness, and kurtosis.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101533"},"PeriodicalIF":2.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}