What can we learn from firm-level jump-induced tail risk around earnings announcements?

被引:0
|
作者
Liu, Mengxi [4 ]
Chan, Kam Fong [1 ]
Faff, Robert [2 ,3 ]
机构
[1] Univ Western Australia, Perth, Australia
[2] Bond Univ, Gold Coast, Australia
[3] Univ Queensland, Brisbane, Australia
[4] InterFinancial Corp Finance Ltd, Brisbane City, Australia
关键词
Tail risk; Jump-implied variance contribution index; Earnings announcements; Implied moments; CROSS-SECTION; SECURITY RETURNS; STOCK; INFORMATION; VOLATILITY; SKEWNESS; PERSISTENCE; EFFICIENCY; DYNAMICS; NEWS;
D O I
10.1016/j.jbankfin.2022.106409
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
In this study, we provide empirical evidence that firm-level jump-induced tail risk (measured by a jump implied variance contribution index [JIVX]) prospectively predicts cross-sectional stock returns around earnings announcements. The effect size is nontrivial. A practical trading strategy that buys announcers with high pre-news JIVX values and sells announcers with low pre-news JIVX values, earns a net risk adjusted average return of 82 basis points (bps) three days after the news release. Notably, the empirical success of the JIVX predictor is distinct from model-free implied skewness and kurtosis measures and withstands a battery of robustness checks. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Social and environmental determinants of diabetes: What we can learn from national to community level data
    Saydah, S.
    Lochner, K. A.
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2008, 167 (11) : S43 - S43
  • [32] Void and precipitate strengthening in α-iron:: what can we learn from atomic-level modelling?
    Osetsky, YN
    Bacon, DJ
    [J]. JOURNAL OF NUCLEAR MATERIALS, 2003, 323 (2-3) : 268 - 280
  • [33] What can we learn from what a machine has learned? Interpreting credit risk machine learning models
    Bharodia, Nehalkumar
    Chen, Wei
    [J]. JOURNAL OF RISK MODEL VALIDATION, 2021, 15 (02): : 1 - 22
  • [34] Assessing breast cancer risk in primary care: What can we learn from cardiovascular disease?
    Phillips, Kelly-Anne
    Keogh, Louise A.
    Steel, Emma
    Collins, Ian M.
    Emery, Jon
    Pirotta, Marie
    Mann, Bruce
    Butow, Phyllis
    Trainer, Alison
    Moreton, Jane
    Antoniou, Antonis C.
    Cuzick, Jack M.
    Hopper, John L.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2013, 31 (15)
  • [35] What Can we Learn from Stock Prices?: Cash Flow, Risk, and Shareholder Welfare Comment
    Choi, Stephen J.
    [J]. JOURNAL OF INSTITUTIONAL AND THEORETICAL ECONOMICS-ZEITSCHRIFT FUR DIE GESAMTE STAATSWISSENSCHAFT, 2019, 175 (01): : 196 - 199
  • [36] What can we learn from the association between adolescent alcohol consumption and breast cancer risk?
    Alimujiang, Aliya
    Colditz, Graham A.
    [J]. EXPERT REVIEW OF ANTICANCER THERAPY, 2019, 19 (04) : 287 - 289
  • [37] Marijuana and the Risk of Fatal Car Crashes: What Can We Learn from FARS and NRS Data?
    Romano, Eduardo
    Torres-Saavedra, Pedro
    Voas, Robert B.
    Lacey, John H.
    [J]. JOURNAL OF PRIMARY PREVENTION, 2017, 38 (03): : 315 - 328
  • [38] Marijuana and the Risk of Fatal Car Crashes: What Can We Learn from FARS and NRS Data?
    Eduardo Romano
    Pedro Torres-Saavedra
    Robert B. Voas
    John H. Lacey
    [J]. The Journal of Primary Prevention, 2017, 38 : 315 - 328
  • [39] What Can We Learn From Common Genetic Variants With Weak Effects on Cardiovascular Disease Risk?
    Newton-Cheh, Christopher
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2019, 73 (23) : 2943 - 2945
  • [40] Assessing breast cancer risk in primary care: What can we learn from cardiovascular disease?
    Collins, Ian M.
    Keogh, Louise A.
    Steel, Emma
    Emery, Jon
    Pirotta, Marie
    Mann, Bruce
    Butow, Phyllis
    Trainer, Alison
    Moreton, Jane
    Antoniou, Antonis C.
    Cuzick, Jack M.
    Hopper, John L.
    Phillips, Kelly-Anne
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2013, 31 (26)