Applying Information Technology to Financial Statement Analysis for Market Capitalization Prediction

Abstract

Determining which attributes may be employed for predicting the market capitalization of a business firm is a chal-lenging task which may benefit from research intersecting principles of accounting and finance with information tech-nology. In our approach, information technology in the form of decision trees and genetic algorithms is applied to fun-damental 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 hypothe-sis and advance us toward answering the research question. The implications of this research are the possibility to im-prove the decision process when employing financial statement analysis as applied to the market capitalization and fi-nancial valuation of business firms.