Applying Information Technology to Financial Statement Analysis for Market Capitalization Prediction

doi:10.4236/ojacct.2013.21001

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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

Email: hwimmer1@umbc.edu, [email protected]

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

H. WIMMER, R. RADA

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

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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