Open Journal of Accounting, 2013, 2, 1-3
http://dx.doi.org/10.4236/ojacct.2013.21001 Published Online January 2013 (http://www.scirp.org/journal/ojacct)
Applying Information Technology to Financial Statement
Analysis for Market Capitalization Prediction
Hayden Wimmer, Roy Rada
Department of Information Systems, University of Maryland Baltimore County,
Baltimore, USA
Received November 1, 2012; revised December 5, 2012; accepted December 13, 2012
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, inf ormation technology in the form of decision trees and genetic alg orithms 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 da ta filtered from Compustat’s global database. Th e resu lts s upport th e 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 f irms.
Keywords: Fundamental Analysis; Market Capitalization; Accountin g Information Systems
1. Introduction
The purpose of this research is to explore the amount of
variation between years of which financial statement
attributes are most critical for determining if the market
capitalization of a business firm increases or decreases.
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. This is
tested by applying an information technology based ap-
proach to determining which attributes are most critical
in the decision process for valuating future market capi-
talization.
Market capitalization is defined as the total outstand-
ing shares of a company multiplied by the stock’s price.
The market capitalization—or market cap—is considered
the public’s valuation of a company. Determining which
attributes from fundamental financial statement analysis
are valuable in predicting the performance of the stocks
is a critical step in any investment strategy. It has long
been accepted that financial markets are informationally
efficient. This is referred to as the Efficient Market Hy-
pothesis [1]. In other words, it is not possible to predict
market performance or determine which financial state-
ment attributes are the most critical in the valuation of a
business firm. In contrast to the efficient market hy-
pothesis, the adaptive market hypothesis [2,3] states that
markets evolve based on competition, natural selection,
and adaptation. This theory of evolution may be applied
to determining which financial statement attributes are
most important from year to year.
Decision trees are graph like structures which may be
extracted into rules for the decision making process. De-
cision trees are common in many business domains such
as accounting, finance, and operations management. A
decision tree may be extracted into a set of rules by fol-
lowing a terminating node of the tree to the root node of
the tree. The more levels in a tree the more complex the
rule base that may be derived from the decision tree. The
decision tree’s rules may be extracted and used as input
for an expert system or decision support system. Com-
puter scientists have developed techniques which incur-
porate machine learning into decision trees. This allows
the decision trees to be trained on a dataset without hu-
man intervention. One of the most popular is the ID3 and
the C4.5 decision tree algorithms [4]. The C4.5 is merely
an extension of the widely popular ID3 algorithm. The
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H. WIMMER, R. RADA
2
machine learning algorithm examines the dataset and
employs a strong hill climbing technique and the co ncept
of information gain to determine which attributes are
most important in classifying the dataset.
Genetic algorithms are computer algorithms which are
frequently applied to optimization problems [5]. An or-
ganism can be thought of as a set of genes and a gene
may be either a single attribute or a combination of at-
tributes depending on the implementation. A genetic al-
gorithm starts with a population or collection of one or
more organisms. The algorithm then makes changes to
the population’s organisms and tests for fitness. Fitness is
defined as how well the organism solves a particular
problem. Each change of the population’s organism(s)
results in a new generation.
There is a body of literature related to predicting fu ture
returns. One such example is using fundamental financial
analysis to predict higher than average returns [6]. An-
other example is what has come to be known as the
Piotroski score [7]. This method identified 9 specific ra-
tios that could be used to pr edict abov e av erage returns in
firms with high book to market values. This work was
extended to employ financial statement analysis to pre-
dict returns in high book to market firms [8]. Next, an
information technology approach was developed by ap-
plying and a genetic algorithm which applied and modi-
fied weights to each of the 9 financial ratios and applied
to the Brazilian stock market [9]. While decision trees
have been employed in accounting, finance, an opera-
tions management applying genetic algorithms to in-
crease the accuracy of decision trees was conducted by
[10]. Research has also applied an information technol-
ogy approach to financial distress prediction [11].
2. Methodology
The null and research hypotheses to be tested in this re-
search are:
1) H0 = Based on the efficient market hypothesis the
attributes required for valuation will differ from year to
year by at least half the total number of attributes.
2) H1 = Based on the adaptive market hypothesis the
attributes will naturally evolve and will differ from year
to year by less than half the total number of attributes.
The dataset was selected from Compustat’s global da-
tabase. The data was then filtered to include data from
the years 2000 th rough 2006 . Only compan ies from GBR
were selected in the dataset to avoid anomalies arising
from local variations such as currency exchange rates.
Only companies that remained active were selected.
From this 66 Compustat [12] attributes were retrieved
which could be extracted from financial statements and
computed for each stock with each stock identified by a
unique identifier. The attribute which most accurately
reflects the performance of a business firm for the pur-
poses of this study is called “pricediv” which is com-
puted as the price + dividends. The reason this attribute
was chosen as the target is price + dividends have a large
effect on the market cap of a business firm. The datasets
contained a minimum of 676 records and a maximum of
1129 rec ords with an ave rage of 877 records. The reason
for the differences was an increase in publically traded
companies during the range of years (2000-2006).
The C4.5 decision tree algorithm was trained on data
from year k to predict the pricediv from year k + 1. For
example, attributes from year 2000 were used to predict
the pricediv of year 2001. This d ecision tree was the sin-
gle organism in the population for a genetic algorithm.
The genetic algorithm then randomly mutated this deci-
sion tree. The resulting decision tree was then tested
against attribute data from year k + 1 to predict the
p1pricediv for year k + 2. For this experiment fitness is
defined as percent classification accuracy. The best pos-
si bl e fitness would be from a C4.5 decision tree created on
data from year k + 1 to predict pricediv from year k + 2.
The genetic algorithm then ran until fitness was achieved.
The number of generations and therefore mutations/changes
was recorded in order to test the hypothesis.
3. Results
The results of the experiment show that less than 9 gen-
erations were necessary to reach fitness. This was much
less than the null hypothesis which stated an average of
50% of the total attributes would be required to reach
fitness. To reiterate, fitness in our experiment is th e clas-
sification accuracy of a decision tree built with the C4.5
machine learning algorithm for the target year. Based on
the results the null hypothesis is rejected and the alterna-
tive hypothesis accepted. Table 1 illustrates the genera-
tions required to reach fitness (classification accuracy).
As stated, the results indicate the level of volatility is
less than one may have expected. There are many factors
that may have influenced such a conclusion. First, it is
Table 1. Average generations to achieve fitness.
Data
Year Prediction Year Generation of Fitness Classification Accuracy/Fitness
2000 2001 1 69
2001 2002 9 56
2002 2003 12 82
2003 2004 10 80
2004 2005 1 75
2005 2006 20 77
Average Generations 8.83

H. WIMMER, R. RADA
3
possible that many financial attributes do not aid in clas-
sification of stocks as a whole. These attributes may be
better suited to classifying a specific industry. Second, it
is possible that only certain attributes are actually useful
when classifying stocks utilizing a decision tree. There
are certain financ ial attributes that have long been recog-
nized as good metrics of a company’s performance such
as EBIDA. Third, it is conceivable that classifications
may be high ly influenced by macroecono mic factors such
as inflation or international monetary fund attributes.
These macroeconomic factors may influence which at-
tributes are helpful or be strongly correlated with certain
attributes thereby causing attributes correlated with the
IMF to become valuable attributes in stock classification.
4. Conclusions and Future Directions
Determining which attributes from financial statement
analysis for predicting the direction of market capitaliza-
tion is a daunting task. The efficient market hypothesis
would lead us to believe that there is a large variation
between years on which attributes are important in pre-
dicting market capitalization. We have demonstrated that
the variation between years is smaller than half the total
number of financial statement attributes available for
determining future market capitalization. In fact, there
was a variation of less than 9%. This study is limited by
the amount of attributes applied to this task. Future re-
search will address this limitation. Additionally, the
dataset employed in this study was limited to a single
country in an established market. Future research will
employ both additional countries as well as emerging
markets in order to draw conclusions between established
versus emerging markets as well as between countries in
the same category. Finally, this study was limited by the
number of years incorporated into th e datasets which will
be addressed in extensions to this work. The implica tions
of this research apply to a broad audience who are inter-
ested in fundamental financial statement analysis and
market capitalization valuation.
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