IDEAS home Printed from https://ideas.repec.org/p/gue/guelph/2010-02..html
   My bibliography  Save this paper

Testing Weak Form Efficiency on the Toronto Stock Exchange

Author

Listed:
  • Vitali Alexeev

    (Department of Economics, University of Guelph, Canada.)

  • Francis Tapon

    (Department of Economics, University of Guelph, Canada.)

Abstract

We believe that in order to test for weak form efficiency in the market a vast pool of individual stocks must be analyzed rather than a stock market index. In this paper, we use a model-based bootstrap to generate a series of simulated trials and apply a modified chart pattern recognition algorithm to all stocks listed on the Toronto Stock Exchange (TSX). We compare the number of patterns detected in the original price series with the number of patterns found in the simulated series. By simulating the price path we eliminate specific time dependencies present in real data, making price changes purely random. Patterns, if consistently identified, carry information which adds value to the investment process, however, this informativeness does not guarantee profitability. We draw conclusions on the relative efficiency of some sectors of the economy. Although, we fail to reject the null hypothesis of weak form efficiency on the TSX, some sectors of the Canadian economy appear to be less efficient than others. In addition, we find negative dependency of pattern frequencies on the two moments of return distributions, variance and kurtosis.

Suggested Citation

  • Vitali Alexeev & Francis Tapon, 2010. "Testing Weak Form Efficiency on the Toronto Stock Exchange," Working Papers 1002, University of Guelph, Department of Economics and Finance.
  • Handle: RePEc:gue:guelph:2010-02.
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability of Technical Analysis: A Review," AgMAS Project Research Reports 37487, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    4. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    5. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    6. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    7. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    10. Lui, Yu-Hon & Mole, David, 1998. "The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 535-545, June.
    11. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    12. Edward R Dawson & James M. Steeley, 2003. "On the Existence of Visual Technical Patterns in the UK Stock Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(1‐2), pages 263-293, January.
    13. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    14. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    15. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March.
    16. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    17. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    18. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    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. Maciel, Leandro, 2021. "A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 38-56.
    2. Dinabandhu Bag & Saurabh Goel, 2023. "Weak Form of Call Auction Prices: Simulation Using Monte Carlo Variants," Capital Markets Review, Malaysian Finance Association, vol. 31(1), pages 59-71.
    3. Juan Benjamín Duarte Duarte & Katherine Julieth Sierra Suárez & Víctor Alfonso Rueda Ortiz, 2015. "Análisis comparativo de eficiencia entre Brasil, México y Estados Unidos," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 7(2), pages 341-357, July.
    4. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Mensi, Walid & Kumar, Ronald Ravinesh, 2017. "Examining the efficiency and interdependence of US credit and stock markets through MF-DFA and MF-DXA approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 351-363.
    5. KiHoon Jimmy Hong & Eliza Wu, 2014. "Can Momentum Factors Be Used to Enhance Accounting Information based Fundamental Analysis in Explaining Stock Price Movements?," Research Paper Series 346, Quantitative Finance Research Centre, University of Technology, Sydney.
    6. Erdas Mehmet Levent, 2019. "Validity of Weak-Form Market Efficiency in Central and Eastern European Countries (CEECs): Evidence from Linear and Nonlinear Unit Root Tests," Review of Economic Perspectives, Sciendo, vol. 19(4), pages 399-428, December.
    7. Senarathne Chamil W., 2020. "Are Religious Believers Irrational: A Direct Test from an Efficient Market Hypothesis," Financial Sciences. Nauki o Finansach, Sciendo, vol. 25(1), pages 35-53, March.
    8. Misheck Mutize & Sean J. Gossel, 2019. "Sovereign Credit Rating Announcement Effects on Foreign Currency Denominated Bond and Equity Markets in Africa," Journal of African Business, Taylor & Francis Journals, vol. 20(1), pages 135-152, January.
    9. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    10. 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.
    11. Sepehr Ghazinoory & Amir Khorasani & Ali Asghar Anvari Rostamy & Ghazaleh Taheriattar & Mona Rashidirad, 2016. "Performance appraisals of ICT companies in the Tehran stock market: contradiction with the global trend," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 29(1), pages 529-544, January.
    12. José A. Roldán-Casas & Mª B. García-Moreno García, 2022. "A procedure for testing the hypothesis of weak efficiency in financial markets: a Monte Carlo simulation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1289-1327, December.
    13. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    14. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi, November.
    15. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
    16. Mamdouh Abdulaziz Saleh Al-Faryan & Everton Dockery, 2021. "Testing for efficiency in the Saudi stock market: does corporate governance change matter?," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 61-90, July.
    17. Mehmet Altuntaş & Emre Kılıç & Şevket Pazarcı & Alican Umut, 2022. "Borsa İstanbul Alt Endekslerinde Etkin Piyasa Hipotezinin Test Edilmesi: Fourier Kırılmalı ve Doğrusal Olmayan Birim Kök Testlerinden Kanıtlar," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(1), pages 169-185.
    18. Aloui, Chaker & Shahzad, Syed Jawad Hussain & Jammazi, Rania, 2018. "Dynamic efficiency of European credit sectors: A rolling-window multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 337-349.
    19. Janet Jyothi Dsouza & T. Mallikarjunappa, 2015. "Does the Indian Stock Market Exhibit Random Walk?," Paradigm, , vol. 19(1), pages 1-20, June.
    20. Thanh Trung Le & Anh Tram Luong, 2020. "A Test of Return Predictability in the Vietnamese Stock Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(2), pages 390-404, April.
    21. Strobel, Marcus & Auer, Benjamin R., 2018. "Does the predictive power of variable moving average rules vanish over time and can we explain such tendencies?," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 168-184.
    22. Gozbasi, Onur & Kucukkaplan, Ilhan & Nazlioglu, Saban, 2014. "Re-examining the Turkish stock market efficiency: Evidence from nonlinear unit root tests," Economic Modelling, Elsevier, vol. 38(C), pages 381-384.
    23. Graham, Michael & Peltomäki, Jarkko & Sturludóttir, Hildur, 2015. "Do capital controls affect stock market efficiency? Lessons from Iceland," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 82-88.
    24. Seungho Baek & Kwan Yong Lee & Merih Uctum & Seok Hee Oh, 2020. "Robo-Advisors: Machine Learning in Trend-Following ETF Investments," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    25. dos Santos Maciel, Leandro, 2023. "Brazilian stock-market efficiency before and after COVID-19: The roles of fractality and predictability," Global Finance Journal, Elsevier, vol. 58(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. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    2. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    3. Chen, Cheng-Wei & Huang, Chin-Sheng & Lai, Hung-Wei, 2009. "The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets," Journal of Asian Economics, Elsevier, vol. 20(5), pages 580-591, September.
    4. Batchelor, Roy & Kwan, Tai Yeong, 2007. "Judgemental bootstrapping of technical traders in the bond market," International Journal of Forecasting, Elsevier, vol. 23(3), pages 427-445.
    5. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Does intraday technical analysis in the U.S. equity market have value?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 199-210, March.
    6. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    7. Ülkü, Numan & Prodan, Eugeniu, 2013. "Drivers of technical trend-following rules' profitability in world stock markets," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 214-229.
    8. 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).
    9. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability Of Technical Trading Rules In Us Futures Markets: A Data Snooping Free Test," 2004 Conference, April 19-20, 2004, St. Louis, Missouri 19011, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    10. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    11. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    12. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    13. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    14. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    15. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan, 2016. "Technical trading: Is it still beating the foreign exchange market?," Journal of International Economics, Elsevier, vol. 102(C), pages 188-208.
    16. Strobel, Marcus & Auer, Benjamin R., 2018. "Does the predictive power of variable moving average rules vanish over time and can we explain such tendencies?," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 168-184.
    17. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857.
    18. Kun Xing & Honggang Li, 2024. "The profitability of interacting trading strategies from an ecological perspective," Annals of Finance, Springer, vol. 20(3), pages 377-394, September.
    19. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    20. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Can commodity futures be profitably traded with quantitative market timing strategies?," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1810-1819, September.

    More about this item

    Keywords

    Market efficiency; weak form market efficiency; Canada; Toronto Stock Exchange;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:gue:guelph:2010-02.. 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: Stephen Kosempel (email available below). General contact details of provider: https://edirc.repec.org/data/degueca.html .

    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.