IDEAS home Printed from https://ideas.repec.org/a/kap/fmktpm/v34y2020i2d10.1007_s11408-020-00350-8.html
   My bibliography  Save this article

Portfolio creation using artificial neural networks and classification probabilities: a Canadian study

Author

Listed:
  • Tania Morris

    (Université de Moncton)

  • Jules Comeau

    (Université de Moncton)

Abstract

This study aims to verify whether using artificial neural networks (ANNs) to establish classification probabilities generates portfolios with higher excess returns than using ANNs in their traditional role of predicting portfolio returns. Our sample includes all companies listed on the Toronto Stock Exchange from 1994 to 2014 with a monthly average of 16,324 company-month observations. Results indicate that portfolios based on the classification probabilities yield mean returns ranging from 7.81 to 14.40% annually over a 16-year period and that portfolios based on both predicted returns and classification probabilities generate returns that are superior to the market index. In addition, there is evidence that ranking securities based on their probability of beating the market has some benefit.

Suggested Citation

  • Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
    • Handle: RePEc:kap:fmktpm:v:34:y:2020:i:2:d:10.1007_s11408-020-00350-8
    DOI: 10.1007/s11408-020-00350-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11408-020-00350-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11408-020-00350-8?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. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Nelson, Charles R, 1976. "Inflation and Rates of Return on Common Stocks," Journal of Finance, American Finance Association, vol. 31(2), pages 471-483, May.
    3. Hudson, Robert & Dempsey, Michael & Keasey, Kevin, 1996. "A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935 to 1994," Journal of Banking & Finance, Elsevier, vol. 20(6), pages 1121-1132, July.
    4. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    5. Mills, Terence C, 1997. "Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 319-331, October.
    6. Basu, Sanjoy, 1983. "The relationship between earnings' yield, market value and return for NYSE common stocks : Further evidence," Journal of Financial Economics, Elsevier, vol. 12(1), pages 129-156, June.
    7. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, vol. 44(2), pages 169-203, May.
    8. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    9. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-565, September.
    10. Eakins, Stanley G. & Stansell, Stanley R., 2003. "Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 83-97.
    11. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    12. Geske, Robert & Roll, Richard, 1983. "The Fiscal and Monetary Linkage between Stock Returns and Inflation," Journal of Finance, American Finance Association, vol. 38(1), pages 1-33, March.
    13. Israel, Ronen & Moskowitz, Tobias J., 2013. "The role of shorting, firm size, and time on market anomalies," Journal of Financial Economics, Elsevier, vol. 108(2), pages 275-301.
    14. Orawan Ratanapakorn & Subhash Sharma, 2007. "Dynamic analysis between the US stock returns and the macroeconomic variables," Applied Financial Economics, Taylor & Francis Journals, vol. 17(5), pages 369-377.
    15. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    16. Tarun K. Mukherjee & Atsuyuki Naka, 1995. "Dynamic Relations Between Macroeconomic Variables And The Japanese Stock Market: An Application Of A Vector Error Correction Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 18(2), pages 223-237, June.
    17. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    18. Banz, Rolf W., 1981. "The relationship between return and market value of common stocks," Journal of Financial Economics, Elsevier, vol. 9(1), pages 3-18, March.
    19. Fama, Eugene F. & Schwert, G. William, 1977. "Human capital and capital market equilibrium," Journal of Financial Economics, Elsevier, vol. 4(1), pages 95-125, January.
    20. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    21. Kanas, Angelos, 2001. "Neural Network Linear Forecasts for Stock Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 245-254, July.
    22. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
    23. Raj Aggarwal & Andrea L. Demaskey, 1997. "Using derivatives in major currencies for cross‐hedging currency risks in Asian emergency markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 17(7), pages 781-796, October.
    24. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    25. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    26. McMillan, David G., 2001. "Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 353-368, December.
    27. Basu, S, 1977. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis," Journal of Finance, American Finance Association, vol. 32(3), pages 663-682, June.
    28. Terence Tai-Leung Chong & Wing-Kam Ng, 2008. "Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30," Applied Economics Letters, Taylor & Francis Journals, vol. 15(14), pages 1111-1114.
    29. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    30. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    31. Mukherjee, Tarun K & Naka, Atsuyuki, 1995. "Dynamic Relations between Macroeconomic Variables and the Japanese Stock Market: An Application of a Vector Error Correction Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 18(2), pages 223-237, Summer.
    32. Teo Jasic & Douglas Wood, 2004. "The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 285-297.
    33. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    34. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    35. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    36. Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
    37. Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
    38. Balvers, Ronald J & Cosimano, Thomas F & McDonald, Bill, 1990. "Predicting Stock Returns in an Efficient Market," Journal of Finance, American Finance Association, vol. 45(4), pages 1109-1128, September.
    39. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    40. 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.
    41. Ferson, Wayne E & Harvey, Campbell R, 1993. "The Risk and Predictability of International Equity Returns," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 527-566.
    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. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).

    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. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    2. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
    3. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    4. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    5. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    6. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    7. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    8. Zura Kakushadze, 2014. "4-Factor Model for Overnight Returns," Papers 1410.5513, arXiv.org, revised Jun 2015.
    9. Fernando Rubio, 2005. "Estrategias Cuantitativas De Valor Y Retornos Por Accion De Largo," Finance 0503029, University Library of Munich, Germany.
    10. Schwert, G. William, 2003. "Anomalies and market efficiency," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974, Elsevier.
    11. Javid, Attiya Yasmin & Ahmad, Eatzaz, 2008. "Testing multifactor capital asset pricing model in case of Pakistani market," MPRA Paper 37341, University Library of Munich, Germany.
    12. Angela J. Black & David G. McMillan, 2004. "Non‐linear Predictability of Value and Growth Stocks and Economic Activity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(3‐4), pages 439-474, April.
    13. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, August.
    14. ALAM Nafis & TAN Ee Chain, 2012. "Impact Of Financial Crisis On Stock Returns: Evidence From Singapore," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 7(2), pages 5-19, August.
    15. Zura Kakushadze & Jim Kyung-Soo Liew, 2015. "Custom v. Standardized Risk Models," Risks, MDPI, vol. 3(2), pages 1-27, May.
    16. John Y. Campbell, 2000. "Asset Pricing at the Millennium," Journal of Finance, American Finance Association, vol. 55(4), pages 1515-1567, August.
    17. Ericsson, Johan & Karlsson, Sune, 2003. "Choosing Factors in a Multifactor Asset Pricing Model: A Bayesian Approach," SSE/EFI Working Paper Series in Economics and Finance 524, Stockholm School of Economics, revised 12 Feb 2004.
    18. Y. Malevergne & D. Sornette, 2007. "A two-Factor Asset Pricing Model and the Fat Tail Distribution of Firm Sizes," Papers physics/0702027, arXiv.org.
    19. Zura Kakushadze, 2014. "Russian-Doll Risk Models," Papers 1412.4342, arXiv.org, revised Nov 2017.
    20. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.

    More about this item

    Keywords

    Risk-adjusted excess return; Artificial neural network; Stock return prediction; Classification probabilities; Portfolio creation;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:kap:fmktpm:v:34:y:2020:i:2:d:10.1007_s11408-020-00350-8. 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.