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A Bayesian Approach to Variable Selection in Logistic Regression with Application to Predicting Earnings Direction from Accounting Information

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Abstract

This paper presents a Bayesian technique for the estimation of a logistic regression model including variable selection. The model is used, as in Ou and Penman (1989), to predict the direction of company earnings, one year ahead of time, from a large set of accounting variables from financial statements. We present a Markov chain Monte Carlo sampling scheme, that includes the variable selection technique of Smith and Kohn (1996) and the non-Gaussian estimation method of Mira and Tierney (1997), to estimate the model. The technique is applied to companies in the United States, United Kingdom and Australia. This extends the analysis of Ou and Penman (1989) who studied United States companies only. The results obtained compare favourably to the technique used in Ou and Penamn (1989) for all three regions.

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  • 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.
    • Handle: RePEc:uts:rpaper:47
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    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp47.pdf
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    Cited by:

    1. 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.
    2. Timerga, Genanew & Gotu, Butte & Alem, Yegnanew, 2011. "Statistical analysis of saving habits of employees: a case study at Debre Birhan Town in North Shoa, Ethiopia," MPRA Paper 42301, University Library of Munich, Germany.
    3. Satkartar K. Kinney & David B. Dunson, 2007. "Fixed and Random Effects Selection in Linear and Logistic Models," Biometrics, The International Biometric Society, vol. 63(3), pages 690-698, September.
    4. Powers, Stephanie & Gerlach, Richard & Stamey, James, 2010. "Bayesian variable selection for Poisson regression with underreported responses," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3289-3299, December.

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