Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-22T00:33:27.315Z Has data issue: false hasContentIssue false

20 - The Information Content of Prices

from PART VIII - MARKET DYNAMICS AT THE MESO-SCALE

Published online by Cambridge University Press:  26 February 2018

Jean-Philippe Bouchaud ,
Julius Bonart ,
Jonathan Donier  and
Martin Gould
Jean-Philippe Bouchaud
Affiliation:
Capital Fund Management, Paris
Julius Bonart
Affiliation:
University College London
Jonathan Donier
Affiliation:
Capital Fund Management
Martin Gould
Affiliation:
CFM - Imperial Institute of Quantitative Finance
Get access

Summary

Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as the result of animal spirits – a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities.

(John Maynard Keynes)

After our deep-dive into the microstructural foundations of price dynamics, the time is ripe to return to one of the most important (and one of the most contentious!) questions in financial economics: what information is contained in prices and price moves? This question has surfaced in various shapes and forms throughout the book, and we feel that it is important to devote a full chapter to summarise and clarify the issues at stake. We briefly touched on some of these points in Section 2.3. Now that we have a better handle on how markets really work at the micro-scale, we return to address this topic in detail.

The Efficient-Market View

Traditionally, market prices are regarded to reflect the fundamental value (of a stock, currency, commodity, etc.), up to small and short-lived mispricings. In this way, a financial market is regarded as a measurement apparatus that aggregates all private estimates of an asset's true (but hidden) value and, after a quick and efficient digestion process, provides an output price. In this view, private estimates should only evolve because of the release of a new piece of information that objectively changes the value of the asset. Prices are then martingales because (by definition) new information cannot be anticipated or predicted. In this context, neither microstructural effects nor the process of trading itself can affect prices, except perhaps on very short time scales, due to discretisation effects like the tick size.

Major Puzzles

This Platonian view of markets is fraught with a wide range of difficulties that have been the subject of thousands of academic papers in the last 30 years (including those with renewed insights from the perspective of market microstructure). The most well known of these puzzles are:

  • • The excess-trading puzzle: If prices really reflect value and are unpredictable, why are there still so many people obstinately trying to eke out profits from trading? […]

    • Type
    Chapter
    Information
    Trades, Quotes and Prices
    Financial Markets Under the Microscope
     , pp. 366 - 380
    Publisher: Cambridge University Press
    Print publication year: 2018

    Access options

    Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

    References

    Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70(3), 393–408.Google Scholar
    Summers, L. H. (1986). Does the stock market rationally reflect fundamental values? The Journal of Finance, 41(3), 591–601.CrossRefGoogle Scholar
    Shiller, R. J. (1990). Speculative prices and popular models. The Journal of Economic Perspectives, 4(2), 55–65.Google Scholar
    Shleifer, A., & Vishny, R.W. (1997). The limits of arbitrage. The Journal of Finance, 52(1), 35–55.CrossRefGoogle Scholar
    Brunnermeier, M. K. (2001). Asset pricing under asymmetric information: Bubbles, crashes, technical analysis, and herding. Oxford University Press on Demand.CrossRefGoogle Scholar
    Schwert, G. W. (2003). Anomalies and market efficiency. In G. M, Constantinides M., Harris, & R. M., Stulz (Eds.), Handbook of the Economics of Finance (Vol. 1, pp. 939–974). Elsevier.Google Scholar
    Farmer, J. D., & Geanakoplos, J. (2009). The virtues and vices of equilibrium and the future of financial economics. Complexity, 14(3), 11–38.CrossRefGoogle Scholar
    Kirman, A. (2010). Complex economics: Individual and collective rationality. Routledge.Google Scholar
    Ang, A., Goetzmann, W. N., & Schaefer, S. M. (2011). The efficient market theory and evidence: Implications for active investment management. Foundations and Trends in Finance, 5(3), 157–242.Google Scholar
    Alajbeg, D., Bubas, Z., & Sonje, V. (2012). The efficient market hypothesis: Problems with interpretations of empirical tests. Financial Theory and Practice, 36(1), 53–72.CrossRefGoogle Scholar
    Lo, A. W. (2017). Adaptive markets. Princeton University Press.CrossRefGoogle Scholar
    Shiller, R. J. (1980). Do stock prices move too much to be justified by subsequent changes in dividends? American Economic Review, 71, 421–436.Google Scholar
    LeRoy, S. F., & Porter, R. D. (1981). The present-value relation: Tests based on implied variance bounds. Econometrica: Journal of the Econometric Society, 49, 555–574.CrossRefGoogle Scholar
    French, K. R., & Roll, R. (1986). Stock return variances: The arrival of information and the reaction of traders. Journal of Financial Economics, 17(1), 5–26.CrossRefGoogle Scholar
    Cutler, D. M., Poterba, J. M., & Summers, L. H. (1989). What moves stock prices? The Journal of Portfolio Management, 15(3), 4–12.CrossRefGoogle Scholar
    Fair, R. C. (2002). Events that shook the market. The Journal of Business, 75(4), 713–731.CrossRefGoogle Scholar
    Joulin, A., Lefevre, A., Grunberg, D., & Bouchaud, J. P. (2008). Stock price jumps: News and volume play a minor role. Wilmott Magazine, September/October, 1–7.
    Dichev, I. D., Huang, K., & Zhou, D. (2014). The dark side of trading. Journal of Accounting, Auditing & Finance, 29(4), 492–518.CrossRefGoogle Scholar
    Black, F. (1986). Noise. The Journal of Finance, 41(3), 528–543.
    Shleifer, A., & Summers, L. H. (1990). The noise trader approach to finance. The Journal of Economic Perspectives, 4(2), 19–33.Google Scholar
    Barber, B.M., & Odean, T. (1999). Do investors trade too much? American Economic Review, 89(5), 1279–1298.Google Scholar
    Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance, 55(2), 773–806.CrossRefGoogle Scholar
    Bouchaud, J.-P., Ciliberti, C., Majewski, A., Seager, P., & Sin-Ronia, K. (2017). Black was right: price is within a factor 2 of value, arXiv:1711.04717.
    Hasbrouck, J. (1991). The summary informativeness of stock trades: An econometric analysis. Review of Financial Studies, 4(3), 571–595.CrossRefGoogle Scholar
    Lyons, R. (2001). The microstructure approach to foreign exchange rates. MIT Press.Google Scholar
    Evans, M. D., & Lyons, R. K. (2002). Order flow and exchange rate dynamics. Journal of Political Economy, 110(1), 170–180.CrossRefGoogle Scholar
    Chordia, T., & Subrahmanyam, A. (2004). Order imbalance and individual stock returns: Theory and evidence. Journal of Financial Economics, 72(3), 485–518.CrossRefGoogle Scholar
    Hopman, C. (2007). Do supply and demand drive stock prices? Quantitative Finance, 7, 37–53.CrossRefGoogle Scholar
    Bouchaud, J. P., Farmer, J. D., & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Hens, T. & Schenk-Hoppe, K. R. (Eds.), Handbook of financial markets: Dynamics and evolution. North-Holland, Elsevier.Google Scholar
    Challet, D., Marsili, M., & Zhang, Y. C. (2013). Minority games: Interacting agents in financial markets. OUP Catalogue.Google Scholar
    Sornette, D., Malevergne, Y., & Muzy, J. F. (2004). Volatility fingerprints of large shocks: Endogenous versus exogenous. In Takayasu, H. (Ed.), The application of econophysics (pp. 91–102). Springer Japan.Google Scholar
    Sornette, D. (2006). Endogenous versus exogenous origins of crises. In Albeverio, S., Jentsch, V., & Kantz, H. (Eds.), Extreme events in nature and society (pp. 95–119). Springer, Berlin-Heidelberg.Google Scholar
    Wyart, M., & Bouchaud, J. P. (2007). Self-referential behaviour, overreaction and conventions in financial markets. Journal of Economic Behavior & Organisation, 63(1), 1–24.CrossRefGoogle Scholar
    Bouchaud, J. P. (2011). The endogenous dynamics of markets: Price impact, feedback loops and instabilities. In Berd, A. M. (Ed.), Lessons from the Credit Crisis. Risk Publications.Google Scholar
    Harras, G., Tessone, C. J., & Sornette, D. (2012). Noise-induced volatility of collective dynamics. Physical Review E, 85(1), 011150.CrossRefGoogle ScholarPubMed
    Thurner, S., Farmer, J. D., & Geanakoplos, J. (2012). Leverage causes fat tails and clustered volatility. Quantitative Finance, 12(5), 695–707.CrossRefGoogle Scholar
    Bouchaud, J. P. (2013). Crises and collective socio-economic phenomena: Simple models and challenges. Journal of Statistical Physics, 151(3–4), 567–606.CrossRef
    Galla, T., & Farmer, J. D. (2013). Complex dynamics in learning complicated games. Proceedings of the National Academy of Sciences, 110(4), 1232–1236.CrossRefGoogle ScholarPubMed
    Caccioli, F., Shrestha, M., Moore, C., & Farmer, J. D. (2014). Stability analysis of financial contagion due to overlapping portfolios. Journal of Banking & Finance, 46, 233–245.CrossRefGoogle Scholar
    Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. In Engle, R. & McFadden, D. (Eds.), Handbook of econometrics (Vol. 4, pp. 2959–3038). North-Holland.
    Filimonov, V., & Sornette, D. (2012). Quantifying reflexivity in financial markets: Toward a prediction of flash crashes. Physical Review E, 85(5), 056108.CrossRefGoogle Scholar
    Hardiman, S., Bercot, N., & Bouchaud, J. P. (2013). Critical reflexivity in financial markets: A Hawkes process analysis. The European Physical Journal B, 86, 442–447.CrossRefGoogle Scholar
    Chicheportiche, R., & Bouchaud, J. P. (2014). The fine-structure of volatility feedback I: Multi-scale self-reflexivity. Physica A: Statistical Mechanics and Its Applications, 410, 174–195.CrossRefGoogle Scholar
    Bacry, E., Mastromatteo, I., & Muzy, J. F. (2015). Hawkes processes in finance. Market Microstructure and Liquidity, 1(01), 1550005.CrossRefGoogle Scholar
    Blanc, P., Donier, J., & Bouchaud, J. P. (2016). Quadratic Hawkes processes for financial prices. Quantitative Finance, 17, 1–18.Google Scholar
    De Bondt, W. P. (1993). Betting on trends: Intuitive forecasts of financial risk and return. International Journal of Forecasting, 9(3), 355–371.CrossRefGoogle Scholar
    Jegadeesh, N., & Titman, S. (2011). Momentum. Annual Review of Financial Economics, 3(1), 493–509.CrossRefGoogle Scholar
    Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228–250.CrossRefGoogle Scholar
    Lempérière, Y., Deremble, C., Seager, P., Potters, M., & Bouchaud, J. P. (2014). Two centuries of trend following. Journal of Investing Strategies, 3, 41–61.Google Scholar
    Barberis, N., Greenwood, R., Jin, L., & Shleifer, A. (2015). X-CAPM: An extrapolative capital asset pricing model. Journal of Financial Economics, 115(1), 1–24.CrossRefGoogle Scholar
    Geczy, C., & Samonov, M. (2015). 215 years of global multi-asset momentum: 1800–2014 (equities, sectors, currencies, bonds, commodities and stocks). https://papers.ssrn.com/sol3/papers.cfm?abstract id=2607730.
    Covel, M. (2017). Trend following: How to make a fortune in bull, bear and black swan markets. Wiley.Google Scholar
    Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica: Journal of the Econometric Society, 56, 1119–1151.CrossRefGoogle Scholar
    Kagel, J. H., & Roth, A. E. (1995). The handbook of experimental economics (pp. 111–194). Princeton University Press.Google Scholar
    Nagel, R. (1995). Unraveling in guessing games: An experimental study. The American Economic Review, 85(5), 1313–1326.Google Scholar
    Hommes, C. (2013). Behavioral rationality and heterogeneous expectations in complex economic systems. Cambridge University Press.CrossRefGoogle Scholar
    Batista, J. D. G., Massaro, D., Bouchaud, J. P., Challet, D., & Hommes, C. (2017). Do investors trade too much? A laboratory experiment. Journal of Economic Behavior and Organization, 140, 18–34.Google Scholar

    Save book to Kindle

    To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

    Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Available formats
    ×

    Save book to Dropbox

    To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

    Available formats
    ×

    Save book to Google Drive

    To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

    Available formats
    ×