IDEAS home Printed from https://ideas.repec.org/a/ijb/journl/v5y2006i2p111-127.html
   My bibliography  Save this article

A Bayesian Model Averaging Approach to Enhance Value Investment

Author

Listed:
  • Ron Bird

    (School of Finance and Economics, University of Technology, Sydney, Australia)

  • Richard Gerlach

    (Econometrics and Business Statistics, University of Sydney, Australia)

Abstract

Simple financial ratios such as book-to-market are often used to identify value stocks. This paper examines the extent to which fundamental accounting information can be used to better identify truly undervalued value stocks to enhance profit in a simple value strategy. Gibbs sampling and model averaging are used in a logistic regression setting, employing fundamental accounting information as explanatory variables, in the design of an implementable investment strategy applied to markets in the US, the UK and Australia.

Suggested Citation

  • Ron Bird & Richard Gerlach, 2006. "A Bayesian Model Averaging Approach to Enhance Value Investment," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 5(2), pages 111-127, August.
    • Handle: RePEc:ijb:journl:v:5:y:2006:i:2:p:111-127
    as

    Download full text from publisher

    File URL: https://ijbe.fcu.edu.tw/assets/ijbe/past_issue/No.05-2/pdf/vol_5-2-2.pdf
    Download Restriction: no
    File URL: https://ijbe.fcu.edu.tw/assets/ijbe/past_issue/No.05-2/abstract/02.html
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    2. Chan, Louis K C & Hamao, Yasushi & Lakonishok, Josef, 1991. "Fundamentals and Stock Returns in Japan," Journal of Finance, American Finance Association, vol. 46(5), pages 1739-1764, December.
    3. Garrat, A. & Lee, K. & Pesaran, M.H. & Shin, Y., 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," Cambridge Working Papers in Economics 0004, Faculty of Economics, University of Cambridge.
    4. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    5. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    6. K. Geert Rouwenhorst, 1999. "Local Return Factors and Turnover in Emerging Stock Markets," Journal of Finance, American Finance Association, vol. 54(4), pages 1439-1464, August.
    7. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    8. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    9. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    10. 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.
    11. Anthony Garratt & Kevin Lee & M Hashem Peseran & Yongcheol Shin, 2000. "Forecast Uncertainties in Macroeconometric Models: An Application to the UK Economy," Discussion Papers in Economics 00/4, Division of Economics, School of Business, University of Leicester.
    12. Richard Gerlach & Ron Bird & Anthony D. Hall, 2000. "A Bayesian Approach to Variable Selection in Logistic Regression with Application to Predicting Earnings Direction from Accounting Information," Research Paper Series 47, Quantitative Finance Research Centre, University of Technology, Sydney.
    13. Victor Bernard & Jacob Thomas & James Wahlen, 1997. "Accounting†Based Stock Price Anomalies: Separating Market Inefficiencies from Risk," Contemporary Accounting Research, John Wiley & Sons, vol. 14(2), pages 89-136, June.
    14. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, 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. 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.

    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. 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.
    3. Gikas Hardouvelis & George Papanastasopoulos & Dimitrios D. Thomakos & Tao Wang, 2007. "Accruals, Net Stock Issues and Value-Glamour Anomalies: New Evidence on their Relation," Working Paper series 47_07, Rimini Centre for Economic Analysis.
    4. Nguyen, Pascal, 2005. "Market underreaction and predictability in the cross-section of Japanese stock returns," Journal of Multinational Financial Management, Elsevier, vol. 15(3), pages 193-210, July.
    5. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
    6. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
    7. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    8. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    9. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    10. Leonid Kogan & Mary Tian, 2012. "Firm characteristics and empirical factor models: a data-mining experiment," International Finance Discussion Papers 1070, Board of Governors of the Federal Reserve System (U.S.).
    11. Dewandaru, Ginanjar & Masih, Rumi & Bacha, Obiyathulla Ismath & Masih, A. Mansur. M., 2015. "Combining momentum, value, and quality for the Islamic equity portfolio: Multi-style rotation strategies using augmented Black Litterman factor model," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 205-232.
    12. Gary Koop & Simon M. Potter & Rodney W. Strachan, 2008. "Re-Examining the Consumption-Wealth Relationship: The Role of Model Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 341-367, March.
    13. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    14. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    15. 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.
    16. Hoang, Khoa & Huang, Ronghong & Truong, Helen, 2023. "Resurrecting the market factor: A case of data mining across international markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    17. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    18. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
    19. Monika Witkowska, 2006. "Fundamentals and stock returns on the Warsaw Stock Exchange. The application of panel data models," Working Papers 11, Department of Applied Econometrics, Warsaw School of Economics.
    20. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.

    More about this item

    Keywords

    model uncertainty; slice sampler; valuation ratio; forecasting; value investing;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:ijb:journl:v:5:y:2006:i:2:p:111-127. 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: Szu-Hsien Ho (email available below). General contact details of provider: https://edirc.repec.org/data/cbfcutw.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.