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Listening to the Noise in Financial Markets

Author

Listed:
  • Lutz G. Arnold
  • David Russ

Abstract

Do all types of information benefit the efficiency of prices in the sense that they drive them closer to fundamentals compared to the situation where information does not exist? Looking at the competitive noisy rational expectations framework, the clear answer of the literature is: yes. It suggests that rational traders use all available types of information to submit more sophisticated market orders, thereby boosting price efficiency. In this paper, however, we propose a contradiction to this traditional view. We show that there exist types of non-fundamental information that are detrimental to price eciency, as they lead traders to rationally trade with rather than against noise. We develop an analytically tractable framework with public non-fundamental information and prove that this type of information can harm price efficiency, i.e., prices would be closer to fundamentals if public non-fundamental information did not exist.

Suggested Citation

  • Lutz G. Arnold & David Russ, 2020. "Listening to the Noise in Financial Markets," Working Papers 203, Bavarian Graduate Program in Economics (BGPE).
  • Handle: RePEc:bav:wpaper:203_arnoldruss
    as

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    File URL: http://www.bgpe.de/texte/DP/203_Arnold_Russ.pdf
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    References listed on IDEAS

    as
    1. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Pingyang Gao, 2008. "Keynesian Beauty Contest, Accounting Disclosure, and Market Efficiency," Journal of Accounting Research, Wiley Blackwell, vol. 46(4), pages 785-807, September.
    4. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    5. Manzano, Carolina & Vives, Xavier, 2011. "Public and private learning from prices, strategic substitutability and complementarity, and equilibrium multiplicity," Journal of Mathematical Economics, Elsevier, vol. 47(3), pages 346-369.
    6. Arnold, Lutz G. & Brunner, Stephan, 2015. "The economics of rational speculation in the presence of positive feedback trading," The Quarterly Review of Economics and Finance, Elsevier, vol. 57(C), pages 161-174.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Rational Expectations Equilibrium; Market Eciency; Non-Fundamental Information; Destabilizing Rational Speculation.;
    All these keywords.

    JEL classification:

    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G40 - Financial Economics - - Behavioral Finance - - - General

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