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Tax Expense Momentum

Citations

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Cited by:

  1. Sol Kim & Geul Lee & Hyoung‐Goo Kang, 2021. "Risk management and corporate social responsibility," Strategic Management Journal, Wiley Blackwell, vol. 42(1), pages 202-230, January.
  2. Brooks, Chris & Godfrey, Chris & Hillenbrand, Carola & Money, Kevin, 2016. "Do investors care about corporate taxes?," Journal of Corporate Finance, Elsevier, vol. 38(C), pages 218-248.
  3. Paul Demeré, 2023. "Is tax return information useful to equity investors?," Review of Accounting Studies, Springer, vol. 28(3), pages 1413-1465, September.
  4. Zhimin (Jimmy) Yu, 2023. "Cross-Section of Returns, Predictors Credibility, and Method Issues," JRFM, MDPI, vol. 16(1), pages 1-12, January.
  5. Peng-Chia Chiu & Timothy D. Haight, 2020. "Investor learning, earnings signals, and stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 671-698, February.
  6. Bradley Blaylock & Bradley P. Lawson & Michael A. Mayberry, 2020. "Taxable income, future profitability, and stock returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 47(7-8), pages 858-881, July.
  7. Bok Baik & Kyonghee Kim & Richard Morton & Yongoh Roh, 2016. "Analysts’ pre-tax income forecasts and the tax expense anomaly," Review of Accounting Studies, Springer, vol. 21(2), pages 559-595, June.
  8. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
  9. Kewei Hou & Haitao Mo & Chen Xue & Lu Zhang, 2019. "Which Factors?," Review of Finance, European Finance Association, vol. 23(1), pages 1-35.
  10. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
  11. Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?," IEAS Working Paper : academic research 17-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  12. Wang, Feifei & Yan, Xuemin Sterling, 2021. "Downside risk and the performance of volatility-managed portfolios," Journal of Banking & Finance, Elsevier, vol. 131(C).
  13. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
  14. Müller, Raphael & Spengel, Christoph & Vay, Heiko, 2020. "On the determinants and effects of corporate tax transparency: Review of an emerging literature," ZEW Discussion Papers 20-063, ZEW - Leibniz Centre for European Economic Research.
  15. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
  16. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
  17. Choy, Siu Kai & Lewis, Craig & Tan, Yongxian, 2023. "Can the changes in fundamentals explain the attenuation of anomalies?," Journal of Financial Economics, Elsevier, vol. 149(2), pages 142-160.
  18. Olivier Ledoit & Michael Wolf & Zhao Zhao, 2016. "Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies," ECON - Working Papers 238, Department of Economics - University of Zurich, revised May 2018.
  19. Blaufus, Kay & Chirvi, Malte & Huber, Hans-Peter & Maiterth, Ralf & Sureth-Slaone, Caren, 2020. "Tax misperception and its effects on decision making: A literature review," arqus Discussion Papers in Quantitative Tax Research 261, arqus - Arbeitskreis Quantitative Steuerlehre.
  20. Zhang, Han & Guo, Bin & Liu, Lanbiao, 2022. "The time-varying bond risk premia in China," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 51-76.
  21. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
  22. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
  23. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
  24. Hediger, Simon & Michel, Loris & Näf, Jeffrey, 2022. "On the use of random forest for two-sample testing," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  25. Taran, Alina & Simga-Mugan, Can & Mironiuc, Marilena, 2021. "Country-segment disclosure of foreign operations from Central and Eastern Europe: Country-level determinants and value relevance," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
  26. Kerr, Jon N., 2019. "The value relevance of taxes: International evidence on the proxy for profitability role of tax surprise," Journal of Accounting and Economics, Elsevier, vol. 67(2), pages 297-305.
  27. Hou, Kewei & Xue, Chen & Zhang, Lu, 2017. "Replicating Anomalies," Working Paper Series 2017-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  28. Lingwei Li & Huai Zhang, 2021. "The devil is in the detail? Investors’ mispricing of proxy voting outcomes on M&A deals," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(3-4), pages 692-717, March.
  29. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
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