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Does Revenue Momentum Drive or Ride Earnings or Price Momentum?

In: HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING

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  • Hong-Yi Chen
  • Sheng-Syan Chen
  • Chin-Wen Hsin
  • Cheng Few Lee

Abstract

This chapter examines the profits of revenue, earnings, and price momentum strategies in an attempt to understand investor reactions when facing multiple information of firm performance in various scenarios. We first offer evidence that there is no dominating momentum strategy among the revenue, earnings, and price momentums, suggesting that revenue surprises, earnings surprises, and prior returns each carry some exclusive unpriced information content. We next show that the profits of momentum driven by firm fundamental performance information (revenue or earnings) depend upon the accompanying firm market performance information (price), and vice versa. The robust monotonicity in multivariate momentum returns is consistent with the argument that the market does not only underestimate the individual information but also the joint implications of multiple information on firm performance, particularly when they point in the same direction. A three-way combined momentum strategy may offer monthly return as high as 1.44%. The information conveyed by revenue surprises and earnings surprises combined account for about 19% of price momentum effects, which finding adds to the large literature on tracing the sources of price momentum.

Suggested Citation

  • Hong-Yi Chen & Sheng-Syan Chen & Chin-Wen Hsin & Cheng Few Lee, 2020. "Does Revenue Momentum Drive or Ride Earnings or Price Momentum?," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 94, pages 3263-3318, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789811202391_0094
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    1. is not listed on IDEAS
    2. Zi-Mei Wang & Donald Lien, 2022. "Is maximum daily return a lottery? Evidence from monthly revenue announcements," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 545-600, August.
    3. Chen, Hong-Yi & Yang, Sharon S., 2020. "Do Investors exaggerate corporate ESG information? Evidence of the ESG momentum effect in the Taiwanese market," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
    4. Lin, Mei-Chen, 2023. "Time-varying MAX preference: Evidence from revenue announcements," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    5. Hong-Yi Chen & Cheng Few Lee & Wei-Kang Shih, 2020. "Technical, Fundamental, and Combined Information for Separating Winners from Losers," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 95, pages 3319-3365, World Scientific Publishing Co. Pte. Ltd..
    6. Hong, KiHoon & Wu, Eliza, 2016. "The roles of past returns and firm fundamentals in driving US stock price movements," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 62-75.
    7. Li, Wanli & Chen, Junrui & Yuan, Kaibin, 2025. "Changes in corporate employment under climate risk," Journal of International Money and Finance, Elsevier, vol. 157(C).
    8. Eero J. Pätäri & Timo H. Leivo & Sheraz Ahmed, 2022. "Can the FSCORE add value to anomaly-based portfolios? A reality check in the German stock market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(3), pages 321-367, September.
    9. Jiong Gong & Ping Jiang & Shu Tian, 2016. "Contractual mutual fund governance: the case of China," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 543-567, April.
    10. KiHoon Jimmy Hong & Eliza Wu, 2014. "Can Momentum Factors Be Used to Enhance Accounting Information based Fundamental Analysis in Explaining Stock Price Movements?," Research Paper Series 346, Quantitative Finance Research Centre, University of Technology, Sydney.
    11. repec:grz:wpsses:2020-04 is not listed on IDEAS
    12. Chen, Hong-Yi & Hsieh, Chia-Hsun & Lee, Cheng-Few, 2023. "Revisiting the momentum effect in Taiwan: The role of persistency," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    13. Fink, Josef, 2021. "A review of the Post-Earnings-Announcement Drift," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    14. Che‐Chia Chang & Chao‐Chun Chen & Pin‐Yu Huang, 2025. "Informational Content of Warrant Trading Prior to Interim Monthly‐Revenue Report: Evidence From the Taiwan Warrant Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(10), pages 1616-1635, October.
    15. Tarunika Jain Agrawal & Sanjay Sehgal & Vibhuti Vasishth, 2020. "Firm Attributes, Corporate Fundamentals and Investment Strategies: An Empirical Study for Indian Stock Market," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(3), pages 366-387, August.
    16. Hong‐Yi Chen & Pin‐Huang Chou & Chia‐Hsun Hsieh, 2018. "Persistency of the momentum effect," European Financial Management, European Financial Management Association, vol. 24(5), pages 856-892, November.
    17. Ramzi Boussaidi & Majed Ibrahim AlSaggaf, 2024. "Post-Earnings Announcement Drift, Momentum, and Contrarian Strategies in the Saudi Stock Market: Risk Explanation vs. Behavioral Explanation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 13622-13653, September.
    18. Jiayu Huang & Yifan Wang & Yaojun Fan & Hexuan Li, 2022. "Gauging the effect of investor overconfidence on trading volume from the perspective of the relationship between lagged stock returns and current trading volume," International Finance, Wiley Blackwell, vol. 25(1), pages 103-123, April.
    19. Jiaqi Guo & Peng Li & Youwei Li, 2022. "What Can Explain Momentum? Evidence from Decomposition," Management Science, INFORMS, vol. 68(8), pages 6184-6218, August.
    20. Schnaubelt, Matthias & Seifert, Oleg, 2020. "Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements?," FAU Discussion Papers in Economics 04/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    21. Lan, Qiujun & Xie, Yuxuan & Mi, Xianhua & Zhang, Chunyu, 2024. "Post earnings announcement drift: A simple earnings surprise measure, the medium effect of investor attention and investing strategy," International Review of Financial Analysis, Elsevier, vol. 95(PB).

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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