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News-driven peer co-movement in crypto markets

Author

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  • Schwenkler, G.
  • Zheng, H.

Abstract

This paper develops a novel methodology to identify peer linkages among cryptocurrencies using natural language processing applied to financial news. We document a distinct pattern of conditional co-movement among peer assets: when a cryptocurrency experiences a large idiosyncratic shock, its peers — identified through news co-mentions — exhibit abnormal returns of the opposite sign. This mis-pricing persists for several weeks and enables profitable trading strategies. Our findings suggest that investor overreaction to news drives these dynamics, highlighting the role of financial media in shaping prices. The proposed methodology extends beyond crypto, offering a generalizable approach to studying peer effects and news-driven pricing distortions.

Suggested Citation

  • Schwenkler, G. & Zheng, H., 2025. "News-driven peer co-movement in crypto markets," Journal of Corporate Finance, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:corfin:v:93:y:2025:i:c:s0929119925000409
    DOI: 10.1016/j.jcorpfin.2025.102772
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    as
    1. Benedetti, Hugo & Kostovetsky, Leonard, 2021. "Digital Tulips? Returns to investors in initial coin offerings," Journal of Corporate Finance, Elsevier, vol. 66(C).
    2. Kewei Hou, 2007. "Industry Information Diffusion and the Lead-lag Effect in Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 20(4), pages 1113-1138.
    3. Cao, Jie & Chordia, Tarun & Lin, Chen, 2016. "Alliances and Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(5), pages 1689-1717, October.
    4. Lee, Charles M.C. & Ma, Paul & Wang, Charles C.Y., 2015. "Search-based peer firms: Aggregating investor perceptions through internet co-searches," Journal of Financial Economics, Elsevier, vol. 116(2), pages 410-431.
    5. Tarun Chordia & Bhaskaran Swaminathan, 2000. "Trading Volume and Cross‐Autocorrelations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(2), pages 913-935, April.
    6. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    7. Jingxing (Rowena) Gan & Gerry Tsoukalas & Serguei Netessine, 2021. "Initial Coin Offerings, Speculation, and Asset Tokenization," Management Science, INFORMS, vol. 67(2), pages 914-931, February.
    8. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    9. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    10. Kenneth R. Ahern & Denis Sosyura, 2015. "Rumor Has It: Sensationalism in Financial Media," The Review of Financial Studies, Society for Financial Studies, vol. 28(7), pages 2050-2093.
    11. Camelia M. Kuhnen, 2015. "Asymmetric Learning from Financial Information," Journal of Finance, American Finance Association, vol. 70(5), pages 2029-2062, October.
    12. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    13. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    14. Bianchi, Daniele & Babiak, Mykola, 2022. "On the performance of cryptocurrency funds," Journal of Banking & Finance, Elsevier, vol. 138(C).
    15. Lauren Cohen & Andrea Frazzini, 2008. "Economic Links and Predictable Returns," Journal of Finance, American Finance Association, vol. 63(4), pages 1977-2011, August.
    16. Tetiana Davydiuk & Deeksha Gupta & Samuel Rosen, 2023. "De-Crypto-ing Signals in Initial Coin Offerings: Evidence of Rational Token Retention," Management Science, INFORMS, vol. 69(11), pages 6584-6624, November.
    17. Lior Menzly & Oguzhan Ozbas, 2010. "Market Segmentation and Cross‐predictability of Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1555-1580, August.
    18. Hasler, Michael & Ornthanalai, Chayawat, 2018. "Fluctuating attention and financial contagion," Journal of Monetary Economics, Elsevier, vol. 99(C), pages 106-123.
    19. Joel Peress, 2014. "The Media and the Diffusion of Information in Financial Markets: Evidence from Newspaper Strikes," Journal of Finance, American Finance Association, vol. 69(5), pages 2007-2043, October.
    20. Hung‐Chia Hsu & Adam V. Reed & Jörg Rocholl, 2010. "The New Game in Town: Competitive Effects of IPOs," Journal of Finance, American Finance Association, vol. 65(2), pages 495-528, April.
    21. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    22. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    23. Jiri Chod & Evgeny Lyandres, 2021. "A Theory of ICOs: Diversification, Agency, and Information Asymmetry," Management Science, INFORMS, vol. 67(10), pages 5969-5989, October.
    24. Kwan, Simon H., 1996. "Firm-specific information and the correlation between individual stocks and bonds," Journal of Financial Economics, Elsevier, vol. 40(1), pages 63-80, January.
    25. Jorion, Philippe & Zhang, Gaiyan, 2007. "Good and bad credit contagion: Evidence from credit default swaps," Journal of Financial Economics, Elsevier, vol. 84(3), pages 860-883, June.
    26. Jean-Noël Barrot & Julien Sauvagnat, 2016. "Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(3), pages 1543-1592.
    27. Ryan Chahrour & Kristoffer Nimark & Stefan Pitschner, 2021. "Sectoral Media Focus and Aggregate Fluctuations," American Economic Review, American Economic Association, vol. 111(12), pages 3872-3922, December.
    28. Tristan Fitzgerald & Benjamin Balsmeier & Lee Fleming & Gustavo Manso, 2021. "Innovation Search Strategy and Predictable Returns," Management Science, INFORMS, vol. 67(2), pages 1109-1137, February.
    29. Lin William Cong & Ye Li & Neng Wang, 2021. "Tokenomics: Dynamic Adoption and Valuation [The demand of liquid assets with uncertain lumpy expenditures]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1105-1155.
    30. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    31. Boone, Audra L. & Ivanov, Vladimir I., 2012. "Bankruptcy spillover effects on strategic alliance partners," Journal of Financial Economics, Elsevier, vol. 103(3), pages 551-569.
    32. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    33. Li, Qian & Wang, Jiamin & Bao, Liang, 2018. "Do institutions trade ahead of false news? Evidence from an emerging market," Journal of Financial Stability, Elsevier, vol. 36(C), pages 98-113.
    34. Cong, Lin William & Li, Ye & Wang, Neng, 2022. "Token-based platform finance," Journal of Financial Economics, Elsevier, vol. 144(3), pages 972-991.
    35. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    36. Gande, Amar & Parsley, David C., 2005. "News spillovers in the sovereign debt market," Journal of Financial Economics, Elsevier, vol. 75(3), pages 691-734, March.
    37. Joseph E. Engelberg & Christopher A. Parsons, 2011. "The Causal Impact of Media in Financial Markets," Journal of Finance, American Finance Association, vol. 66(1), pages 67-97, February.
    38. Brennan, Michael J & Jegadeesh, Narasimhan & Swaminathan, Bhaskaran, 1993. "Investment Analysis and the Adjustment of Stock Prices to Common Information," The Review of Financial Studies, Society for Financial Studies, vol. 6(4), pages 799-824.
    39. Philippe Jorion & Gaiyan Zhang, 2009. "Credit Contagion from Counterparty Risk," Journal of Finance, American Finance Association, vol. 64(5), pages 2053-2087, October.
    40. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    41. Christopher A Parsons & Riccardo Sabbatucci & Sheridan Titman, 2020. "Geographic Lead-Lag Effects," The Review of Financial Studies, Society for Financial Studies, vol. 33(10), pages 4721-4770.
    42. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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