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Digesting unrealized gains and losses in China

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  • Xiong, Tao
  • Wang, Peng

Abstract

Existing researches indicate a V-shaped relationship between unrealized returns and future returns. We propose that investors’ T-strategies change the relationship to be W-shaped. The artificial unrealized return is derived from the turnover rate and stock price. This method splits the return of one investor employing T-strategies into many small increments of profit, resulting in the W-shaped pattern. Applying the tree-based conditional sort to predict future returns, artificial unrealized loss becomes the most important predictor, and the importance of artificial unrealized gain is comparable to other well-known pricing factors. Portfolios based on the predictions can generate an average monthly return of 3.84%, with an adjusted t-value of 3.43. Sharpe ratios, Fama–French alphas, and three kinds of bootstrap simulations confirm the robustness of excess returns.

Suggested Citation

  • Xiong, Tao & Wang, Peng, 2025. "Digesting unrealized gains and losses in China," Finance Research Letters, Elsevier, vol. 86(PE).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pe:s1544612325020197
    DOI: 10.1016/j.frl.2025.108765
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    References listed on IDEAS

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    1. Amit Seru & Tyler Shumway & Noah Stoffman, 2010. "Learning by Trading," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 705-739, February.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    4. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    5. Barber, Brad M. & Odean, Terrance, 2013. "The Behavior of Individual Investors," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1533-1570, Elsevier.
    6. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    7. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2019. "Size and value in China," Journal of Financial Economics, Elsevier, vol. 134(1), pages 48-69.
    8. Broussard, John Paul & Vaihekoski, Mika, 2012. "Profitability of pairs trading strategy in an illiquid market with multiple share classes," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1188-1201.
    9. Wang, Nianling & Zhang, Mingzhi & Zhang, Yuan, 2024. "Return prediction: A tree-based conditional sort approach with firm characteristics," Finance Research Letters, Elsevier, vol. 60(C).
    10. Chen Su & Hanxiong Zhang, 2020. "A Time-Series Bootstrapping Simulation Method to Distinguish Sell-Side Analysts’ Skill from Luck," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 55, pages 2011-2052, World Scientific Publishing Co. Pte. Ltd..
    11. Itzhak Ben-David & David Hirshleifer, 2012. "Are Investors Really Reluctant to Realize Their Losses? Trading Responses to Past Returns and the Disposition Effect," The Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2485-2532.
    12. GholamReza Keshavarz Haddad & Hassan Talebi, 2023. "The profitability of pair trading strategy in stock markets: Evidence from Toronto stock exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 193-207, January.
    13. Grinblatt, Mark & Han, Bing, 2005. "Prospect theory, mental accounting, and momentum," Journal of Financial Economics, Elsevier, vol. 78(2), pages 311-339, November.
    14. Gallagher, David R. & Gardner, Peter A. & Swan, Peter L., 2013. "Governance through Trading: Institutional Swing Trades and Subsequent Firm Performance," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 427-458, April.
    15. Eugene F. Fama & Kenneth R. French, 2010. "Luck versus Skill in the Cross‐Section of Mutual Fund Returns," Journal of Finance, American Finance Association, vol. 65(5), pages 1915-1947, October.
    16. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    17. Chu, Xiaojun & Gu, Zherong & Zhou, Haigang, 2019. "Intraday momentum and reversal in Chinese stock market," Finance Research Letters, Elsevier, vol. 30(C), pages 83-88.
    18. Hanxiong Zhang & Andrew Urquhart, 2019. "Pairs trading across Mainland China and Hong Kong stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 698-726, April.
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