IDEAS home Printed from https://ideas.repec.org/a/spr/eaiere/v16y2019i1d10.1007_s40844-018-0102-0.html
   My bibliography  Save this article

Mechanism by which active funds make market efficient investigated with agent-based model

Author

Listed:
  • Takanobu Mizuta

    (SPARX Asset Management Co., Ltd.)

  • Sadayuki Horie

    (Osaka University of Economics)

Abstract

Since active managed funds, in which a manager chooses stocks expected to rise in price, invest on the basis of the intrinsic fundamental value of companies, they discover the fundamental price and make market prices converge with the fundamental price (make a market efficient); therefore, they play an important role in allocating capital, which is an important function in capitalism. A previous empirical study showed active funds that trade infrequently, “patient” active funds, earn more. At first glance, what patient active funds trade infrequently seems inconsistent with making a market efficient. In this study, we modeled agents who reflect the characteristics of patient active funds that trade infrequently and “impatient” active funds that trade frequently. We succeeded in figuring out the mechanism of how patient and impatient funds impacted market prices and in proving that what patient active funds trade infrequently is not inconsistent with making a market efficient. Concretely, the simulation results indicated that patient active funds trade frequently only in the rare situation that a market became unstable and inefficient. These trades, occurring only at a necessary time, impact market prices and lead them to converge with the fundamental price. The results also indicated that patient active funds earn less not so much because of a more efficient market, but because the market is too inefficient, so changes in price formation due to trades of impatient active funds reduce the chance that patient active funds will realize profit.

Suggested Citation

  • Takanobu Mizuta & Sadayuki Horie, 2019. "Mechanism by which active funds make market efficient investigated with agent-based model," Evolutionary and Institutional Economics Review, Springer, vol. 16(1), pages 43-63, June.
  • Handle: RePEc:spr:eaiere:v:16:y:2019:i:1:d:10.1007_s40844-018-0102-0
    DOI: 10.1007/s40844-018-0102-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40844-018-0102-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40844-018-0102-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kenneth R. French, 2008. "Presidential Address: The Cost of Active Investing," Journal of Finance, American Finance Association, vol. 63(4), pages 1537-1573, August.
    2. repec:hal:spmain:info:hdl:2441/3utlh0ehcn860pus6p2p683ade is not listed on IDEAS
    3. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    4. Albagli, Elias, 2015. "Investment horizons and asset prices under asymmetric information," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 787-837.
    5. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    6. Thomas A. Hanson, 2016. "High frequency traders in a simulated market," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(3), pages 329-351, August.
    7. Braun-Munzinger, Karen & Liu, Zijun & Turrell, Arthur, 2016. "An agent-based model of dynamics in corporate bond trading," Bank of England working papers 592, Bank of England.
    8. Martijn Cremers & Ankur Pareek, 2015. "Short-Term Trading and Stock Return Anomalies: Momentum, Reversal, and Share Issuance," Review of Finance, European Finance Association, vol. 19(4), pages 1649-1701.
    9. Kotaro Miwa, 2018. "Effective extension of trading hours," Evolutionary and Institutional Economics Review, Springer, vol. 15(1), pages 139-166, June.
    10. T. Verheyden & L. De Moor & F. Van Den Bossche, 2013. "A Tale of Market Efficiency," Review of Business and Economic Literature, Intersentia, vol. 58(2), pages 140-158, June.
    11. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    12. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    13. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    14. Cremers, Martijn & Pareek, Ankur, 2016. "Patient capital outperformance: The investment skill of high active share managers who trade infrequently," Journal of Financial Economics, Elsevier, vol. 122(2), pages 288-306.
    15. Fichtner, Jan & Heemskerk, Eelke M. & Garcia-Bernardo, Javier, 2017. "Hidden power of the Big Three? Passive index funds, re-concentration of corporate ownership, and new financial riskâ€," Business and Politics, Cambridge University Press, vol. 19(2), pages 298-326, June.
    16. Thomas Stöckl & Jürgen Huber & Michael Kirchler, 2010. "Bubble measures in experimental asset markets," Experimental Economics, Springer;Economic Science Association, vol. 13(3), pages 284-298, September.
    17. Jeffrey Wurgler, 2010. "On the Economic Consequences of Index-Linked Investing," NBER Working Papers 16376, National Bureau of Economic Research, Inc.
    18. Isao Yagi & Atsushi Nozaki & Takanobu Mizuta, 2017. "Investigation of the rule for investment diversification at the time of a market crash using an artificial market simulation," Evolutionary and Institutional Economics Review, Springer, vol. 14(2), pages 451-465, December.
    19. Toshiyuki Sakiyama & Tetsuya Yamada, 2016. "Market Liquidity and Systemic Risk in Government Bond Markets: A Network Analysis and Agent-Based Model Approach," IMES Discussion Paper Series 16-E-13, Institute for Monetary and Economic Studies, Bank of Japan.
    20. Tetsuo Kurosaki & Yusuke Kumano & Kota Okabe & Teppei Nagano, 2015. "Liquidity in JGB Markets: An Evaluation from Transaction Data," Bank of Japan Working Paper Series 15-E-2, Bank of Japan.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qixuan Luo & Shijia Song & Handong Li, 2023. "Research on the Effects of Liquidation Strategies in the Multi-asset Artificial Market," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1721-1750, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Isao Yagi & Yuji Masuda & Takanobu Mizuta, 2020. "Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity," Papers 2010.13038, arXiv.org.
    2. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    3. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    4. Poledna, Sebastian & Thurner, Stefan & Farmer, J. Doyne & Geanakoplos, John, 2014. "Leverage-induced systemic risk under Basle II and other credit risk policies," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 199-212.
    5. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    6. Agarwal, Vikas & Ren, Honglin & Shen, Ke & Zhao, Haibei, 2021. "Redemption in kind and mutual fund liquidity management," CFR Working Papers 21-11, University of Cologne, Centre for Financial Research (CFR).
    7. Hazan, Aurélien, 2017. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 589-602.
    8. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    10. Yanan Li & Wenjun Wang, 2022. "Company visits and mutual fund performance: new evidence on managerial skills," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 504-521, October.
    11. Guerini, Mattia & Napoletano, Mauro & Roventini, Andrea, 2018. "No man is an Island: The impact of heterogeneity and local interactions on macroeconomic dynamics," Economic Modelling, Elsevier, vol. 68(C), pages 82-95.
    12. Gunther Capelle-Blancard, 2018. "What is the Point of (the Hundreds of Thousands of Billions of) Stock Transactions?," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 60(1), pages 15-33, March.
    13. Te Bao & Cars Hommes & Tomasz Makarewicz, 2017. "Bubble Formation and (In)Efficient Markets in Learning‐to‐forecast and optimise Experiments," Economic Journal, Royal Economic Society, vol. 127(605), pages 581-609, October.
    14. Xintong Wang & Christopher Hoang & Yevgeniy Vorobeychik & Michael P. Wellman, 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis," Games, MDPI, vol. 12(2), pages 1-43, May.
    15. Stein, Julian Alexander Cornelius & Braun, Dieter, 2019. "Stability of a time-homogeneous system of money and antimoney in an agent-based random economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 232-249.
    16. Dosi, Giovanni & Roventini, Andrea & Russo, Emanuele, 2019. "Endogenous growth and global divergence in a multi-country agent-based model," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 101-129.
    17. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    18. Zhang, Jinhua & Wang, Guipu & Yan, Cheng, 2020. "Can foreign equity funds outperform their benchmarks? New evidence from fund-holding data for China," Economic Modelling, Elsevier, vol. 90(C), pages 11-20.
    19. Merl, Robert & Stöckl, Thomas & Palan, Stefan, 2023. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions," Journal of Banking & Finance, Elsevier, vol. 154(C).
    20. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Joseph E. Stiglitz & Tania Treibich, 2020. "Rational Heuristics? Expectations And Behaviors In Evolving Economies With Heterogeneous Interacting Agents," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1487-1516, July.

    More about this item

    Keywords

    Agent-based model; Multi-agent simulation; Artificial market simulation; Active managed fund; Passive managed fund; Index fund; Market efficiency; Price discovery function;
    All these keywords.

    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G19 - Financial Economics - - General Financial Markets - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eaiere:v:16:y:2019:i:1:d:10.1007_s40844-018-0102-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.