AbstractThis paper offers a novel methodology using several new ratios and comparison approaches to investigate public software companies’ financial activities and condition. The methodology focuses on time-series data mining, monitoring and analyzing. The dataset is based on 100 U.S. software companies with least ten-year SEC verified income statement, balance sheets and cash flow statement. The contribution of this paper is creating and applying several new financial ratios combined with traditional approach to detect companies’ financial structure changes and accounts manipulation. For cash flow statement operating section, our proposed major account to operating net cash inflow and outflow ratios provide a better visualization of the cash sources and usage, which help analysts to observe major cash flow structure changes and make predication. For investing section, our proposed investing cash flow growth contribution ratio is used to identify irregular investment behavior. Combining with the traditional financial ratio tests, we believe that our approach significantly facilitates early detection on suspicious financial activities and the evaluation of its financial status

Trafalis, Theodore B.

Zhang, Zhen

Elsevier - Publisher Connector 

 Procedia Computer Science  20 ( 2013 )  466 – 471 
1877-0509 © 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Missouri University of Science and Technology
doi: 10.1016/j.procs.2013.09.304 
ScienceDirect
Available online at www.sciencedirect.com
Complex Adaptive Systems, Publication 3 
Cihan H. Dagli, Editor in Chief 
Conference Organized by Missouri University of Science and Technology 
2013- Baltimore, MD 
Time-series Analysis for Detecting Structure Changes and Suspicious 
Accounting Activities in Public Software Companies 
Zhen Zhanga, ,Theodore B. Trafalisb 
aSchool of Industrial and System Engineering, University of Oklahoma, Norman, Oklahoma 73019 
bSchool of Industrial and System Engineering, University of Oklahoma, Norman, Oklahoma 73019 
Abstract 
This paper offers a novel methodology using several new ratios and comparison approaches to investigate public software 
activities and condition.  The methodology focuses on time-series data mining, monitoring and analyzing. 
The dataset is based on 100 U.S. software companies with least ten-year SEC verified income statement, balance sheets and cash 
flow statement. The contribution of this paper is creating and applying several new financial ratios combined with traditional 
approach to detect 
our proposed major account to operating net cash inflow and outflow ratios provide a better visualization of the cash sources and 
usage, which help analysts to observe major cash flow structure changes and make predication. For investing section, our 
proposed investing cash flow growth contribution ratio is used to identify irregular investment behavior. Combining with the 
traditional financial ratio tests, we believe that our approach significantly facilitates early detection on suspicious financial 
activities and the evaluation of its financial status. 
 
Keywords: Data mining; Structure changes; Accounts manipulation; Classification; Financial ratios; Cash flow; Text information; Time series 
analysis 
1. Traditional Financial Ratio Tests 
Based on Pw  [1] and SEC verified financial statements, we selected and 
investigated 40 companies which were pre-categorized into high, middle and lower group by its combined licence, 
maintenance and support revenue. With traditional financial ratios tests, we found that Paychex Inc. and Convergys 
displayed unusually favorable signs even both companies were originally sorted into the low rank group according 
to Pw survey. Table 1 shows that Paychex Inc. has a fantastic marginal operational ratio (MOP) which is close to 
or even higher than many high rank large companies. Combing with its decent revenue figure, it indicates that 
Paychex Inc. does not only have a strong earning power but also operates in a highly efficient way. Similarly, 
Convergys displays a higher net income to revenue ratio than the MOP ratio, indicating it owns some other 
significant income sources other than its main business. However, both companies were put at the bottom of the 
PwC ranking list.  we carried our in-
depth investigation by treating nt team as a decision maker and focusing on its income 
sources, operating efficiency, financial structure and resource allocation. 
© 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Missouri University of Science and Technology 
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
467 Zhen Zhang and Theodore B. Trafalis /  Procedia Computer Science  20 ( 2013 )  466 – 471 
Table 1.  Selected financial ratio test results 
Traditional Financial 
Ratios 
Oracle ORCL   
May 31,2011 
EMC          
Dec 31,2011 
INTUIT        
Jul 31, 2011 
Adobe    
Dec 02 
2011 
Netsuite       
Dec 31, 
2011 
PayChex      
May 
31,2011 
Convergys     
Dec 31.2011 
MOP=OPER/Revenue 33.78% 17.21% 27.49% 26.04% -12.71% 37.72% 5.70% 
Net Income/Revenue 23.99% 13.04% 17.29% 19.73% -13.56% 24.71% 14.61% 
OPER Expense/Revenue 66.22% 82.79% 72.51% 73.96% 112.71% 62.28% 94.30% 
RoR = Earnings on 
Capital 28.69% 16.26% -69.12% 11.25% -23.26% 52.92% 31.26% 
Times Interest Charges 14.122525 18.24706 16.3 15.43284 -145.341 - 27.4 
Times Interest Charges 
&  dividend 14.122525 18.24706 16.3 15.43284 -145.341 - 27.4 
EPS Basic $1.69 $1.20 $2.07 $1.67 ($0.48) $1.42 $2.79 
EPS Diluted $1.67 $1.10 $2.00 $1.65 - $1.42 $2.72 
Depreciation/Sales 8.02% 14.16% 14.87% 12.52% 9.32% 14.83% 17.79% 
Inventory Turnover 11756.44% 1980.99% - - - - 2214.43% 
Cost of sales/Inventory 2771.62% 776.14% - - - - 1516.72% 
Credit Policy 68 54 17 55 60 33 58 
Bond Capitalization 28.59% 15.30% -240.14% 14.07% 0.00% 0.00% 8.26% 
Book Value/Share 8 9 8 11 - 4 11 
CA/CL 2.8 1.1 1.2 3.0 1.5 1.1 2.6 
(CA-Inventories)/CL 2.7 1.0 1.2 3.0 1.5 1.1 2.3 
1.1. A brief introduction of the company Paychex Inc. 
Paychex Inc. was founded in Delaware, 1979. Revenue of this company is consisted of two parts: service 
revenues and investment revenues. Service revenues are based on two segments: Payroll and Human Resource 
Services-Professional Employer Organization after 1990s multiple acquisitions and expansion.  
The company claims its consecutive revenues growth resulted from a growing client base, the sales of ancillary 
services and the decreasing operating expenses. Meanwhile, intensive competition faced by Paychex Inc. includes 
manual payroll systems, in-house computer capability, and one large payroll processing company targeting mainly 
on big companies.  
Specifically, Payroll segment is responsible for preparing paychecks, accounting records, all tax returns, 
collection and remittance of payroll obligations. It utilizes Taxpay through Electronic Network Services (ENS) to 
collect and pay taxes to the authorities. The company also makes a great amount of investment by taking advantage 
 
Investment revenues face both credit risks and interest rate risk, therefore the company tried to limit risks by 
investing mainly in good rating securities, short- to intermediate- term instruments which are less sensitive to 
interest rate, and avoiding investment concentration on one or few securities.   
The other important source of revenue is Human Resource Services (HRS-PBS). HRS includes 401 (k) 
recordkeeping services, premium only plan and employee handbook. Besides, PBS is a subsidiary which provides 
outsourcing solution for small to medium sized business to deal with complicated employer service [2].  
1.2. Eleven years basic financial data analysis 
      Table 2 shows its eleven year continuous service growth rate (490%) outpaced the selling general and 
administration expenses (SGAE) and operating costs growth rate (371%), generating a decent operating income 
growth rate (1229%). HRS-PEO stably advanced and gradually become a significant source of total service revenues 
(1%-9%). SGAE increased much more rapidly (401%) than operating costs (327%) and since it started with a higher 
468   Zhen Zhang and Theodore B. Trafalis /  Procedia Computer Science  20 ( 2013 )  466 – 471 
base, so it is the major offset factor to the fast growing revenue (59%-63%). The average net income growth rate is 
39% and investment income took stable portion as 6%-7% of the total net income. 
HRS-PEO surfaced as a new source of income. While analyzing income sources, we tend to focus on the main, 
the recurring and stable one.  If great future profits were expected to be raised from new projects or different 
 our doubt on it [3]. Especially, by 
launching a new product or service line, a significant client growth rate will appear at first then drop significantly. 
However, due to HRS-PEO initial rapid increase, even this segment displayed a decreasing growth rate over the 
following years, it still gradually become a significant revenue resources of the total service revenues. 
Table 2. Eleven year growth rate and growth contribution  
Item 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 11-year 
Growth Rate:             
Payroll 20% 23% 19% 22% 18% 17% 17% 17% 13% 18%  443% 
HRS-PEO 35% 25% 30% 32% 60% 47% 47% 60% 61% 207%  7715% 
Total Service 
Revenues 
21% 24% 20% 22% 20% 18% 18% 18% 14% 19%  490% 
Operating Costs 15% 15% 14% 24% 17% 13% 15% 7% 11% 27%  327% 
SGAE 13% 21% 16% 17% 16% 16% 15% 21% 17% 22%  401% 
Operating Income 25% 27% 23% 24% 23% 21% 21% 20% 14% 15%  581% 
IBIT 25% 27% 23% 25% 23% 21% 21% 20% 13% 15%  587% 
Net Income 36% 36% 37% 36% 41% 42% 47% 44% 12% -9%  1373% 
* HRS-PEO % of 
Total Service 
Revenues 
9% 8% 8% 7% 7% 5% 4% 3% 2% 2% 1%  
Growth 
Contribution: 
            
Payroll'/ Total 
Service Revenues' 
87% 92% 89% 91% 85% 90% 92% 92% 93% 93%   
HRS-PEO'/Total 
Service Revenues' 
13% 8% 11% 9% 15% 10% 8% 8% 7% 7%   
OPER'/Revenues' 20% 18% 21% 32% 26% 23% 27% 15% 30% 50%   
SGAE'/Revenues' 29% 42% 39% 39% 41% 47% 45% 63% 64% 61%  371% 
Investment 
Income'/IBIT' 
2% 2% 2% 2% 2% 2% 1% 0% -2% 1%   
 
2. In-depth Segment Analysis 
     We then focused on 1993-1999 segment information since the company expanded aggressively, 
making multiple acquisitions and several three for two stock splits in that period.  
 
2.1.  Structure change observation and detection: 
       
Before 1997, Electronic Network Services (ENS) funds generated from Taxpay and Direct Deposit were actually 
used by the company in short-term investment and not reported in balance sheet. Since 1997, the company was 
requested to restate and reveal this practice by law enforcement. We use several traditional financial ratios to display 
and evaluate this transition. Table 3 clearly shows that there was a major financial structure variation followed by 
the change of reporting method: First of all, the ratio of current assets (CA) to current liabilities (CL) significantly 
dropped from 5 to 1, so did the ratio of total assets (TA) to total liabilities (TL) changed from 6 to 1. Both indexes 
are 
and preferred stock holders, how capable it is to get fast cash and deal with immediate liabilities. The minimum 
469 Zhen Zhang and Theodore B. Trafalis /  Procedia Computer Science  20 ( 2013 )  466 – 471 
requirement is two times and the higher the safer the entity may be. Second, after combining a great amount of ENS 
funds into current assets and current liabilities, the ratio of current assets to assets significantly increased from 74% 
to 93% even when current assets before ENS growth rate kept steadily. 
Table 3. Structure changes  
% of Total Property and 
Equipment 1999 1998 1997* 1996* 1995 1994 
Total Current Assets/Total 
Assets 96% 95% 95% 93% 74% 67% 
CA before ENS         
growth rate 33% 33% 38% 33% 43%  
Total CA                  
growth rate 21% 30% 47% 33% 43%  
Total CL                  
growth rate 18% 28% 48% 6% 41%  
Total L                   
growth rate 18% 28% 48% 3% 34%  
CL before ENS/Total 
Liabilities 5% 5% 5% 6% 94% 89% 
(CL + ENS)/Total 
Liabilities 100% 100% 100% 100%   
Others/Total Assets 0% 0% 0% 0% 6% 11% 
CA-CL $360,784 $263,118 $194,614 $138,639 $97,558 $68,031 
CA/CL 1 1 1 1 5 5 
Total A-Total L $435,800 $329,607 $251,542 $191,072 $139,932 $108,508 
Total A/Total L 1 1 1 1 6 6 
 
2.2. Growth contribution to net cash flow 
Cash flow 
influenced by net income, depreciation and amortization charges. The net cash flow is used to support normal 
business operations, the expansion of property and equipment, and the payment of dividend. A cash flow analysis 
al and financial activities [4]. A 2006 cross-country 
dataset covers more than 30 countries and 13,000 firms with 9 year records, and the results made researchers firmly 
believe that financial development matters the most to the financial capability [5]. Specifically, Frino and Heaney 
(2005) found out that Australian retail superannuation companies  past performance is positively related with 
cur
earnings based on cash flow information [7]. Garrett and Priestley (2012) claims that cash flow news significantly 
affect dividend growth and used cash flow betas to explain value and size effects [8]. On the other side, Guo, Wang 
and Zhang (2013) concentrated on manipulation issues, claimed that non state-owned enterprises in China are more 
prone to manipulate their cash flow statements in order to attract outside supportive funds [9].  
Therefore, how to accurately analyze and . 
the 
source of information, Pae and Yoon (2013) reinforced that a specific company oriented cash flow predication 
display much more accuracy than the generalized industrial-wide one [10]. Multiple approaches have been explored 
to forecast future cash flow, such as artificial intelligence hybrid system based EFSIM method [11], fuzzy set 
theory, risk sensitivity analysis [12], neural networks and regression approach [13].  
tegorized cash 
flow components analysis significantly increase future cash flow forecasting accuracy [14]. Similarly, Ahmadi,A 
esearch further demonstrate that the current operating cash flow is a more accurate index 
for future cash flow prediction than others [15]. Moreover, according to their short-and long- term balance sheet 
470   Zhen Zhang and Theodore B. Trafalis /  Procedia Computer Science  20 ( 2013 )  466 – 471 
observation, Dasgupta, Noe & Wang (2011) found out that besides investment, purchase of property and equipment, 
other accounts undertake the responsibility to absorb short-term cash flow shocks [16].  In sum, with primary focus 
on monitoring the constitution and changes of cash inflow and outflow, we believe that our segmented cash flow 
analysis is helpful for cash flow analysis and future cash flow predication.  
Payroll Inc.'s main cash flow sources were made up of net income, depreciation and amortization (D&A), 
amortization of premiums and discounts on securities. We categorize all operating cash flow accounts into two 
categories: Operating Cash Inflow (OCI) and Outflow (OCO). By monitoring new ratios of Net Income/OCI, 
D&A/OCI, we found out that trades payable accounts actually played as an important "back-up" role in cash flow 
adjustment. It may testify that the entity has a great ability to control its cash flow by adjusting the payment to 
vendors or liabilities to others. 
For instance, table 4 shows that 
Meanwhile trade payable account became a significant cash inflow source, which took a 15% of total cash inflow.  
Table 4.  Sources of operating cash inflow 
Year OPER             Cash inflow 
Net Income/ OPER 
Inflow D&A/ OPER Inflow MAJOR 
Trade payable/OPER 
Inflow 
1993 $34,738 57% 31% 88% 7% 
1994 $41,831 67% 27% 94% 0% 
1995 $65,017 62% 17% 79% 14% 
1996 $78,610 70% 18% 88% 6% 
1997 $112,475 67% 14% 80% 12% 
1998 $154,112 66% 12% 79% 15% 
1999 $195,743 71% 11% 82% 11% 
 
As table 5 shows that traditional growth rate of each account only reflects its own increasing or decreasing trend 
and pace, without clearly showing how much influence the account actually generated to the net result. 
 
Table 5. Traditional growth rate  
 
Traditional Growth Rate 1999 1998 1997* 1996 * 1995 1994 
Net Income 36% 36% 37% 36% 44% 41% 
Depreciation and amortization 18% 22% 10% 26% -1% 5% 
Amortization of premiums and discounts on 
securities 27% 39% 90% 33%   
Trade accounts payable and other current 
liabilities -5% 64% 186% -46% 9011% -96% 
Net cash provided by operating activities 27% 34% 50% 27% 57% 16% 
*If without trade accounts payable and 
other current liabilities: 34% 30% 40% 43% 30% 26% 
 
Therefore, we established a new ratio to measure in percentage, how much growth contribution an account 
actually made to the annual net cash flow. Table 6 clearly shows that trade accounts payable and other account 
liabilities fluctuated irregularly (In percentage, the contribution to the cash inflow increases: -31% ,39%,-33%), 
playing as a supportive role when net income and other major cash inflow accounts underperformed. We did similar 
tests in investing section which is beneficial in identifying irregular investment behavior. 
 
Current Year Account Value - Previous Year Account ValueGrowth Contribution=
Current Year Cash Inflow  - Previous Year Cash inflow                                                               (1) 
 
471 Zhen Zhang and Theodore B. Trafalis /  Procedia Computer Science  20 ( 2013 )  466 – 471 
c p
A A
f c p
i i
i i
V VGC
C C
                                                                                                                                                (2) 
 
 Table 6. Growth contribution to the total cash flow and the net cash inflow 
 
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In Percentage, the Contribution to Total Cash Flow 
Increases: 1999 1998 1997 1996 1995 1994 
Trade accounts payable and other current liabilities -3% 26% 27% -29% 47% -50% 
Depreciation and amortization 9% 10% 4% 20% -1% 11% 
Amortization of premiums and discounts on securities 6% 7% 8% 5% 13% 0% 
Net Income 99% 77% 59% 100% 64% 174% 
       
In Percentage, the Contribution to the Cash Inflow 
Increases:       
Trade accounts payable and other current liabilities -3% 22% 27% -31% 39% -33% 
Depreciation and amortization 8% 8% 4% 21% -1% 7% 
Amortization of premiums and discounts on securities 6% 6% 9% 6% 10% 0% 
Net Income 89% 65% 59% 108% 53% 114% 


Time-series Analysis for Detecting Structure Changes and Suspicious Accounting Activities in Public Software Companies 

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Time-series Analysis for Detecting Structure Changes and Suspicious Accounting Activities in Public Software Companies

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