IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v175y2019icp92-96.html
   My bibliography  Save this article

Measuring excess-predictability of asset returns and market efficiency over time

Author

Listed:
  • Levich, Richard
  • Conlon, Thomas
  • Potì, Valerio

Abstract

We build on the predictability bounds of Huang and Zhou (2017) and Potì (2018) to develop an index of informational market inefficiency. This index takes values given by the levels of relative risk aversion (RRA) of the marginal investor such that, net of sampling error at a given confidence level, the observed predictability does not exceed the predictability bound. We demonstrate the usefulness of our index in a study of the predictability of forward exchange rates of currencies of emerging and developed economies from 1994 to 2016, to shed light on how the efficiency of currency markets has evolved over this time. We find widespread evidence of excess-predictability, hence currency market inefficiency, in the early part of the sample period and then at specific times, such as the recent global financial crisis. In the more recent part of the sample period, the evidence of excess-predictability is largely limited to emerging market currencies.

Suggested Citation

  • Levich, Richard & Conlon, Thomas & Potì, Valerio, 2019. "Measuring excess-predictability of asset returns and market efficiency over time," Economics Letters, Elsevier, vol. 175(C), pages 92-96.
  • Handle: RePEc:eee:ecolet:v:175:y:2019:i:c:p:92-96
    DOI: 10.1016/j.econlet.2018.12.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176518305147
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2018.12.022?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. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    2. Potì, Valerio, 2018. "A new tight and general bound on return predictability," Economics Letters, Elsevier, vol. 162(C), pages 140-145.
    3. Kuang, P. & Schröder, M. & Wang, Q., 2014. "Illusory profitability of technical analysis in emerging foreign exchange markets," International Journal of Forecasting, Elsevier, vol. 30(2), pages 192-205.
    4. Dominik M. Rösch & Avanidhar Subrahmanyam & Mathijs A. van Dijk, 2017. "The Dynamics of Market Efficiency," Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1151-1187.
    5. Levich, Richard M. & Potì, Valerio, 2015. "Predictability and ‘good deals’ in currency markets," International Journal of Forecasting, Elsevier, vol. 31(2), pages 454-472.
    6. Huang, Dashan & Zhou, Guofu, 2017. "Upper Bounds on Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(2), pages 401-425, April.
    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. Bošnjak Mile & Novak Ivan & Vlajčić Davor, 2021. "Market Efficiency of Euro Exchange Rates and Trading Strategies," Naše gospodarstvo/Our economy, Sciendo, vol. 67(2), pages 10-19, June.
    2. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Analysis and comparison of the multifractality and efficiency of Chinese stock market: Evidence from dynamics of major indexes in different boards," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C), pages 1-1.
    4. Potì, Valerio & Levich, Richard & Conlon, Thomas, 2020. "Predictability and pricing efficiency in forward and spot, developed and emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 107(C).
    5. Serna, Gregorio, 2023. "On the predictive ability of conditional market skewness," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 186-191.
    6. Azzam, Islam & El-Masry, Ahmed A. & Yamani, Ehab, 2023. "Foreign exchange market efficiency during COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 717-730.
    7. Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Comparative analysis of the multifractality and efficiency of exchange markets: Evidence from exchange rates dynamics of major world currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

    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. Potì, Valerio & Levich, Richard & Conlon, Thomas, 2020. "Predictability and pricing efficiency in forward and spot, developed and emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 107(C).
    2. Potì, Valerio, 2018. "A new tight and general bound on return predictability," Economics Letters, Elsevier, vol. 162(C), pages 140-145.
    3. Andrew Urquhart, 2017. "How predictable are precious metal returns?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(14), pages 1390-1413, November.
    4. Ding, Wenjie & Mazouz, Khelifa & Wang, Qingwei, 2021. "Volatility timing, sentiment, and the short-term profitability of VIX-based cross-sectional trading strategies," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 42-56.
    5. Neely, Christopher J., 2022. "How persistent are unconventional monetary policy effects?," Journal of International Money and Finance, Elsevier, vol. 126(C).
    6. Potì, Valerio & Levich, Richard M. & Pattitoni, Pierpaolo & Cucurachi, Paolo, 2014. "Predictability, trading rule profitability and learning in currency markets," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 117-129.
    7. Lioui, Abraham & Poncet, Patrice, 2019. "Long horizon predictability: An asset allocation perspective," European Journal of Operational Research, Elsevier, vol. 278(3), pages 961-975.
    8. Tajaddini, Reza & Crack, Timothy Falcon, 2012. "Do momentum-based trading strategies work in emerging currency markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 521-537.
    9. Yamani, Ehab, 2021. "Foreign exchange market efficiency and the global financial crisis: Fundamental versus technical information," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 74-89.
    10. Dockery, Everton & Todorov, Ivan, 2023. "Further evidence on the returns to technical trading rules: Insights from fourteen currencies," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    11. Xiaoye Jin, 2022. "Evaluating the predictive power of intraday technical trading in China's crude oil market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1416-1432, November.
    12. Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.
    13. Lin Liu & Qiguang Chen, 2020. "How to compare market efficiency? The Sharpe ratio based on the ARMA-GARCH forecast," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
    14. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    15. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    16. Pojarliev, Momtchil & Levich, Richard M., 2010. "Trades of the living dead: Style differences, style persistence and performance of currency fund managers," Journal of International Money and Finance, Elsevier, vol. 29(8), pages 1752-1775, December.
    17. Accominotti, Olivier & Chambers, David, 2016. "If You're So Smart: John Maynard Keynes and Currency Speculation in the Interwar Years," The Journal of Economic History, Cambridge University Press, vol. 76(2), pages 342-386, June.
    18. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    19. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
    20. Yaojie Zhang & Feng Ma & Chao Liang & Yi Zhang, 2021. "Good variance, bad variance, and stock return predictability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4410-4423, July.

    More about this item

    Keywords

    Return predictability; Market efficiency; Predictability bounds;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G1 - Financial Economics - - General Financial Markets

    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:eee:ecolet:v:175:y:2019:i:c:p:92-96. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.