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Capital Market Efficiency and Arbitrage Efficacy

Author

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  • Akbas, Ferhat
  • Armstrong, Will J.
  • Sorescu, Sorin
  • Subrahmanyam, Avanidhar

Abstract

Efficiency in the capital markets requires that capital flows are sufficient to arbitrage anomalies away. We examine the relation between flows to a quantitative (quant) strategy that is based on capital market anomalies and the subsequent performance of this strategy. When these flows are high, quant funds are able to implement arbitrage strategies more effectively, which in turn leads to lower profitability of market anomalies in the future, and vice versa. Thus, the degree of cross-sectional equity market efficiency varies across time with the availability of arbitrage capital.

Suggested Citation

  • Akbas, Ferhat & Armstrong, Will J. & Sorescu, Sorin & Subrahmanyam, Avanidhar, 2016. "Capital Market Efficiency and Arbitrage Efficacy," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(2), pages 387-413, April.
    • Handle: RePEc:cup:jfinqa:v:51:y:2016:i:02:p:387-413_00
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    Citations

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    Cited by:

    1. Jingzhi Chen & Charlie X. Cai & Robert Faff & Yongcheol Shin, 2022. "Nonlinear limits to arbitrage," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1084-1113, June.
    2. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
    3. Xi Dong & Shu Feng & Ronnie Sadka, 2019. "Liquidity Risk and Mutual Fund Performance," Management Science, INFORMS, vol. 65(3), pages 1020-1041, March.
    4. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    5. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    6. Ihor Kendiukhov, 2024. "Present Value of the Future Consumer Goods Multiplier," Papers 2402.01938, arXiv.org.
    7. Peress, Joël & Dong, Xi & KANG, NAMHO, 2020. "Fast and Slow Arbitrage: Fund Flows and Mispricing in the Frequency Domain," CEPR Discussion Papers 15235, C.E.P.R. Discussion Papers.
    8. Mills, Brian M. & Salaga, Steven, 2018. "A natural experiment for efficient markets: Information quality and influential agents," Journal of Financial Markets, Elsevier, vol. 40(C), pages 23-39.
    9. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    10. Long, Huaigang & Zaremba, Adam & Zhou, Wenyu & Bouri, Elie, 2022. "Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns," Journal of Financial Markets, Elsevier, vol. 61(C).
    11. Hanauer, Matthias X. & Lesnevski, Pavel & Smajlbegovic, Esad, 2023. "Surprise in short interest," Journal of Financial Markets, Elsevier, vol. 65(C).
    12. Adam Zaremba & Jan Jakub Szczygielski, 2019. "Limits to arbitrage, investor sentiment, and factor returns in international government bond markets," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 1727-1743, January.
    13. Frijns, Bart & Indriawan, Ivan & Tourani-Rad, Alireza & Zhang, Hengbin, 2023. "The effect of equity market uncertainty on informational efficiency: Cross-sectional evidence," Global Finance Journal, Elsevier, vol. 57(C).
    14. Liao Xu & Xiangkang Yin & Jing Zhao, 2022. "Are the flows of exchange‐traded funds informative?," Financial Management, Financial Management Association International, vol. 51(4), pages 1165-1200, December.
    15. Chen, Chih-Nan & Lin, Chien-Hsiu, 2020. "The sources of pricing factors underlying the cross-section of currency returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 250-265.
    16. Feng, Frank Yulin & Kang, Wenjin & Zhang, Huiping, 2023. "Liquidity shocks and the negative premium of liquidity volatility around the world," Journal of International Money and Finance, Elsevier, vol. 139(C).
    17. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.

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