Applying Information Technology to Financial Statement Analysis for Market Capitalization Prediction
Affiliation(s)
Department of Information Systems, University of Maryland Baltimore County, Baltimore, USA.
Department of Information Systems, University of Maryland Baltimore County, Baltimore, USA.
ABSTRACT
Determining which attributes may be employed for predicting the market capitalization of a business firm is a challenging task which may benefit from research intersecting principles of accounting and finance with information technology. In our approach, information technology in the form of decision trees and genetic algorithms is applied to fundamental financial statement data in order to support the decision making process for predicting the direction of the value of a company with value defined as the market capitalization. The decision process differs from year to year; however, the amount of variation is crucial to a successful decision making process. The research question posed is “how much variation occurs between years?” We hypothesize the amount of variation is smaller than half the number of financial statement attributes that may be employed in the decision making process. We develop a system which tests the amount of variation between years measured as the amount of generations required to reach a target level of fitness. The hypothesis is tested using data filtered from Compustat’s global database. The results support the research hypothesis and advance us toward answering the research question. The implications of this research are the possibility to improve the decision process when employing financial statement analysis as applied to the market capitalization and financial valuation of business firms.
Determining which attributes may be employed for predicting the market capitalization of a business firm is a challenging task which may benefit from research intersecting principles of accounting and finance with information technology. In our approach, information technology in the form of decision trees and genetic algorithms is applied to fundamental financial statement data in order to support the decision making process for predicting the direction of the value of a company with value defined as the market capitalization. The decision process differs from year to year; however, the amount of variation is crucial to a successful decision making process. The research question posed is “how much variation occurs between years?” We hypothesize the amount of variation is smaller than half the number of financial statement attributes that may be employed in the decision making process. We develop a system which tests the amount of variation between years measured as the amount of generations required to reach a target level of fitness. The hypothesis is tested using data filtered from Compustat’s global database. The results support the research hypothesis and advance us toward answering the research question. The implications of this research are the possibility to improve the decision process when employing financial statement analysis as applied to the market capitalization and financial valuation of business firms.
Cite this paper
H. Wimmer and R. Rada, "Applying Information Technology to Financial Statement Analysis for Market Capitalization Prediction," Open Journal of Accounting, Vol.2 No.1, 2013, pp. 1-3. doi: 10.4236/ojacct.2013.21001.
H. Wimmer and R. Rada, "Applying Information Technology to Financial Statement Analysis for Market Capitalization Prediction," Open Journal of Accounting, Vol.2 No.1, 2013, pp. 1-3. doi: 10.4236/ojacct.2013.21001.
References
[1] E. Fama, “Efficient Capital Markets: II,” Journal of Finance, Vol. 46, No. 5, 1991, pp. 1575-1618. doi:10.1111/j.1540-6261.1991.tb04636.x [2] A. Lo, “The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective,” The Journal of Portfolio Management, Vol. 30, No. 5, 2004, pp. 15-29. doi:10.3905/jpm.2004.442611 [3] A. Lo, “Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis,” Journal of Investment Consulting, Vol. 7, No. 2, 2009, pp. 21-44. http://ssrn.com/abstract=728864. [4] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1, 1986, pp. 81-106. doi:10.1007/BF00116251 [5] D. E. Goldberg, “Genetic Algorithms in Optimization, Search and Machine Learning,” Addison-Wesley, Reading, 1989. [6] M. D. Beneish, C. M. C. Lee and R. L. Tarpley, “Contextual Fundamental Analysis through the Prediction of Extreme Returns,” Review of Accounting Studies, Vol. 6, No. 2-3, 2001, pp. 165-189. doi:10.1023/A:1011654624255 [7] J. D. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers,” Journal of Accounting Research, Vol. 38, 2000, pp. 1-41. doi:10.2307/2672906 [8] P. Mohanram, “Separating Winners from Losers among Low Book-To-Market Stocks Using Financial Statement Analysis,” Review of Accounting Studies, Vol. 10, No. 2-3, 2005, pp. 133-170. doi:10.1007/s11142-005-1526-4 [9] F. Galdi and S. Hermesmeyer, “The Use of Genetic Algorithms to Obtain Higher Returns on Investment Strategies Based on Financial Statement Analysis: A Test in Brazil,” 2010 American Accounting Annual Meeting and Conference on Teaching and Learning Accounting, San Francisco, 31 July 2010. http:http://aaahq.org/AM2010/abstract.cfm?submissionID=1090 [10] Z. Fu, “Using Genetic Algorithm-Based Approach for Better Decision Trees: A Computational Study,” Springer-Verlag, Berlin, 2002. [11] M. Moradi, M. Salehi, H. Yazdi and M. Gorgani, “Going Concern Prediction of Iranian Companies by Using Fuzzy C-Means,” Open Journal of Accounting, Vol. 1, No. 2, 2012, pp. 38-46. doi:10.4236/ojacct.2012.12005 [12] Standard & Poor’s, “Standard & Poor’s Compustat Xpress-feed: Understanding the Data,” McGraw-Hill, New York, 2011.
[1] E. Fama, “Efficient Capital Markets: II,” Journal of Finance, Vol. 46, No. 5, 1991, pp. 1575-1618. doi:10.1111/j.1540-6261.1991.tb04636.x [2] A. Lo, “The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective,” The Journal of Portfolio Management, Vol. 30, No. 5, 2004, pp. 15-29. doi:10.3905/jpm.2004.442611 [3] A. Lo, “Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis,” Journal of Investment Consulting, Vol. 7, No. 2, 2009, pp. 21-44. http://ssrn.com/abstract=728864. [4] J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1, 1986, pp. 81-106. doi:10.1007/BF00116251 [5] D. E. Goldberg, “Genetic Algorithms in Optimization, Search and Machine Learning,” Addison-Wesley, Reading, 1989. [6] M. D. Beneish, C. M. C. Lee and R. L. Tarpley, “Contextual Fundamental Analysis through the Prediction of Extreme Returns,” Review of Accounting Studies, Vol. 6, No. 2-3, 2001, pp. 165-189. doi:10.1023/A:1011654624255 [7] J. D. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers,” Journal of Accounting Research, Vol. 38, 2000, pp. 1-41. doi:10.2307/2672906 [8] P. Mohanram, “Separating Winners from Losers among Low Book-To-Market Stocks Using Financial Statement Analysis,” Review of Accounting Studies, Vol. 10, No. 2-3, 2005, pp. 133-170. doi:10.1007/s11142-005-1526-4 [9] F. Galdi and S. Hermesmeyer, “The Use of Genetic Algorithms to Obtain Higher Returns on Investment Strategies Based on Financial Statement Analysis: A Test in Brazil,” 2010 American Accounting Annual Meeting and Conference on Teaching and Learning Accounting, San Francisco, 31 July 2010. http:http://aaahq.org/AM2010/abstract.cfm?submissionID=1090 [10] Z. Fu, “Using Genetic Algorithm-Based Approach for Better Decision Trees: A Computational Study,” Springer-Verlag, Berlin, 2002. [11] M. Moradi, M. Salehi, H. Yazdi and M. Gorgani, “Going Concern Prediction of Iranian Companies by Using Fuzzy C-Means,” Open Journal of Accounting, Vol. 1, No. 2, 2012, pp. 38-46. doi:10.4236/ojacct.2012.12005 [12] Standard & Poor’s, “Standard & Poor’s Compustat Xpress-feed: Understanding the Data,” McGraw-Hill, New York, 2011.