Review of

1-18-1= 0_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVE AND VALIDITY

10-1-1= 0_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVES AND VALIDITY

10-1-10_doc_Predictive-M= odeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_Slope

10-1-10_rtf_Predictive-M= odeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_Slope

10-1-1= 0_mht_Predictive-Modeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_= Slope

10-1-10_mhtm_Predictive-= Modeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_Slope_R-Sq= uare_InterCept_ATS_Source-Code_Multiple_Platforms: Trade Station®, Ninja Trader®, Stock Finder®, eSignal®, M= etastock®, Wealth-Lab®, Neuroshell Trader®, Amibroker®, Stratasearch®= ;, Worden Brothers Blocks®, Aspen Graphics®, Workstation Tradingsolutions®, Tradecision®, Trade Navigator®, Ensign Software®, VT Trader®, Swingtrader®, TD Ameritrade®, & Strategydesk®

Review of

Automa= ted Portfolio Trading Systems Forensic Forecast Correction Perspective- Linea= r, Polynomial & Auto Regressive Integrated Moving Average (ARIMA) Regres= sion Models Assumptions, Narratives & Validity

&= nbsp;

Avi an= d Sara Rushinek, University of Miami, arush@miami.edu arushine@aol.com (305)666-= 7890

8/2= 7/2010

http://www.bus.miami.edu/fac= ulty-and-research/faculty-directory/computer-information-systems/rushinek/i= ndex.html

8-27-10_Mon_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRA= TIVE AND VALIDITY

9-6-10_Mon_doc_COST FUNCTION MODEL ASSUMPTIONS NARRATI= VE AND VALIDITY

Automated Portfolio Trading Systems Forensic Perspective- Linear, Polynomial & Auto Regressive Integrated Moving Average (ARIMA) Regression Models Assumptions, Narratives & Validity = -Trading systems strategies frequently deal with forecasting the profitability of = the trade using regression analysis. Yet many of the elementary accounting textbooks authors confuse ve= ry basic issues related to regression analysis. Forensic accounting tests such confusions in the courts and tries to clarify the as expert witnesses try= to estimate the damages using a "but-if" regression analysis. This paper takes some of these l= essons and tries to evaluate them to see whether they apply to automated international portfolio trading systems strategies. In an automated portf= olio trading system strategies of mixed securities such as currencies, futures, equities and derivatives a common denominator of predictor variable and predicted variables are important as the systems combine pips, yens and dollars, in a variety of time frames of daily, hourly, and tick by tick quotes. Trading platforms a= nd software applications frequently use different methods to describe time, which is frequently the predictor variable of many profits forecasting method. A case in point is Microsoft Excel®, which is using the Julian date, as a measure of time. Since some textbooks confuse such issues, the challenge of correctly auditing & validating= an automated trading strategy on multiple computational and trading platforms becomes more difficult to handle. Therefore, it may be beneficial to identify such confusions as ear= ly as possible in the design of such trading systems to save time and improve performance as soon as possible. The authors will test such systems on commercial trading platforms such as Trade Station®, Ninja Trader®, Stock Finder®, eSignal®, using mixed international securities, and brokers, such as = GainCapital®, TradeStation®, in a variety of markets.*

------------------------------------------------------= ---------------------------------------------------------------

* The authors have omitted some details to save space = and shorten the discussion; such additional details will be provided by the aut= hors upon written requests.

1-18-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVE AND VALIDITY

C:\Users\a\Documents

8-27-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVE AND VALIDITY

Automated Portfolio Trading Systems Forensic Perspecti= ve- Linear, Polynomial & Auto Regressive Integrated Moving Average (ARIMA) Regression Models Assumptions, Narratives & Validity

Avi and Sara Rushinek, University of Miami, arush@miami.edu arushine@aol.com (305)0666-7890

http://www.bus.miami.edu/fac= ulty-and-research/faculty-directory/computer-information-systems/rushinek/i= ndex.html

Automated Portfolio Trading Systems Forensic Perspecti= ve- Linear, Polynomial & Auto Regressive Integrated Moving Average (ARIMA) Regression Models Assumptions, Narratives & Validity

Abstract

Trading systems strategies frequently deal with foreca= sting the profitability of the trade using regression analysis. Yet many of the elementary account= ing textbooks authors confuse very basic issues related to regression analysis.= Forensic accounting tests such confusions in the courts and tries to clarify the as expert witnesses try to estimate the damages using a "but-if" regression analysis. This paper takes some of these les= sons and tries to evaluate them to see whether they apply to automated internati= onal portfolio trading systems strategies. In an automated portfolio trading sys= tem strategies of mixed securities such as currencies, futures, equities and derivatives a common denominator of predictor variable and predicted variab= les are important as the systems combine pips, yens and dollars, in a variety of time frames of daily, hourly, and tick by tick quotes. Trading platforms and software applications frequently use different methods to describe time, which is frequently the predictor variable of many profits forecasting method. A case in point is Microsoft Excel= ®, which is using the Julian date,= as a measure of time. Since some textbooks confuse such issues, the challenge of correctly auditing & validating an automated trading strategy on multiple computational and trad= ing platforms becomes more difficult to handle. Therefore, it may be beneficial to identify such confusions as early as possible in the design of such trading systems to save time and improve performance as soon as possible. The authors will test such systems= on commercial trading platforms such as Trade Station®, Ninja Trader®= ;, Stock Finder®, eSignal®, using mixed international secur= ities, and brokers, such as GainCapital®, Trade Station®, in a variety= of markets.*

------------------------------------------------------= ---------------------------------------------------------------

* The authors have omitted some details to save space = and shorten the discussion; such additional details will be provided by the aut= hors upon written requests.

COST FUNCTION MODEL ASSUMPTIONS NARRATIVE AND VALIDITY- A LINEAR AND POLYNOMIAL REGRESSION GOODNESS OF FIT FOR FORENSIC ACCOUNTING, EXPERT WITNESS TESTIMONY & COMPUTER LITIGATION SUPPORT

Overview

This paper deals with forensic studies in accounting a= nd Business. It focuses on the i= ntegration of academic and educational issues of fraud and forensic applications of management accounting theories of the “relevant range” and vali= dity of the cost functions goodness of fit assumptions. The paper tracks the improvement in the narrative of explaining the cost function in educational context of fraud and forensic accounting.&= nbsp; Using pedagogical techniques, such as case presentations, empirical studies, mock trials, as well as the practice of expert witness testimony a= nd computer litigation support, in both the higher education and the continuing professional education environments. = This paper present techniques and technologies of investigation involving real-world business problems of forecasting cost as a function of revenue using computers for litigation support. It describes collaboration between practitioners and academics in deploying new forensic methods, in theoretic= al, empirical and experimental approaches.

Programmed trading systems that combine fundamen= tal and technical analysis, and multiple time frame time series, such as quarte= rly, daily, hourly, minute and tick data present a variety of challenges, especi= ally whenever they combine multiple models, such as linear and polynomial, with trading rules that deal with the Regression Channel, Intercept, Slope, &= ; R-Squared. Some trading platforms, such as Ninja-Trader and TradeStation provide such calculations as built-in indicat= ors, leading trading systems to apply them with disregard to the violations of s= ome basic regression assumptions. The questions that this study raises are how can the trading systems account for the departure from such assumptions, measure their adverse impact, and adju= st or minimize such effects to maximize trading profits.

One of the many challenges includes the dependencies o= f each observation on the previous observations, and the violation of the regressi= on assumption of independent observations. This study demonstrates a way for automated trading systems to calculate the impact of the departure from such assumptions, without reprogramming the indicators. One way of measuring the departure= from the assumptions that this study is proposing is transforming the raw undiff= erenced quarterly time series into differenced time series and calculating the drop= s in R-Square and Slopes of the linear Ordinary Least Squares (OLS) regression model.

Traders frequently use R-Square as a measure of “As you may have noticed, a numbe= r of rather complicated indicators are available to measure trend

Strength. ... Indicator of= trend strength, the r² value from linear regression

analysis. [1]“Likewise, it proceeds as follows: "run a regression line (line A in Figure 4) through the last 20 days of data and record the R2.

Then ... extend the line o= ne day further back in time and recalculate the R2. If it stayed the same or increased, then the added day was consistent with the previous 20-day trend= and he would go back another day to repeat the calculation. Stepping backward in this fashion (line B in Figure 4), he'd eventually find a point where the R2 decreased (the gap in Figure 4); an indication that the latest added price = was inconsistent with the most recent trend. All these straight-line methods are explications of o= ur intuitive

sense of expressing trend as direction, even if only horizontal."

"... having a thresho= ld level of change in the R2, using the slope of the regression line to define breakout, or even checking beyond the point of declining R2 for prices that would return the regression's coefficient to its previous values, are easy = to imagine. It also ingeniously solves the time issue: how long must a trend persist to be a trend?" (Tushar S. Chande, 2009) (= http://old.artun.ee/~momentum/ebook/STOCKS%20&%20COMMODITIES/Stocks%20&= amp;%20Commodities%20-%20various%20Trend%20Trading%20Articles.pdf) If the departure from the assumpti= ons distorts the R-Squared, then programmed trading may also be distorted leadi= ng to "loosing" decisions, and eventually to the bankruptcy of a trader. The objective of this= study is to provide a measure to the distortion, so programmed trading can be adj= usted, according to the distortion risk. The drop in R-Square due to the departure from the assumptions is su= ch a measure.

Quarterly Updated RegARIMA (Regression Auto Regressive Integrated Moving Average) Models For Seasonal Adjustment

Beginning with the 2007Q4 data release, updated RegARI= MA models for seasonal adjustment were implemented to improve the identification of seasonal patterns in unadjusted net sales and net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing = is used as our raw data, and raw data model, as opposed to the differenced data that had been transformed by calculating the change or quarterly sales gains (+) or losses (-) from year to year. = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; &= nbsp;

= &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp;

Linear To Polynomial Comparison Of Undifferenced Raw Quarterly Time Series Data Of Linear And Polynomial R² & Function<= /p>

(http://www.census.gov/econ/qfr/index.html)

&nbs= p;

Raw Data Only- Comparing Poly to Linear

Trader realize that exponential maybe superior to linear as follows "True refinements are French curves and fitted curves such as Bezier and regressi= ons to exponential curves (à la Tom Kimball of Florida, of newsle= tter fame). Here, regression could also be used, perhaps searching through an en= tire family of potential curves for consistent fit over a given period of time. Again, price action is reduced to a simple line, but it's meant to be more indicative of the market's action. To wrap up, we usually identify trend af= ter it's started by the = slope of the lines we draw on our charts, whether straight or curved. However, if trend is persistence in price movement over time, trading range activity is= a trend of sorts." But, th= ey fall short of quantifying the difference, so a simple programmed trade can reduce forecast errors, and losses, which is what the current study does.

The Linear to Polynomial Comparison of the Undifferenc= ed Raw Quarterly Time Series Data starts with the Raw Data, describing the R²= and the models' Polynomial and Linear regression Functions. 1st is the Raw Data, Polynomial wi= th an R-Squared of = 90.12% and a function equation of y =3D 40.516x6 - 1338.7x5 + 15734x4 - 77264x3 + 135313x2 + 32234x + 903575. 2= nd is the Raw Data Linear R-Squared of 63.96% and its equation function of y =3D = 43464x + 969209. 3rd is the Raw Data= R**2 Drop of 26.16%, that shows the superiority of the polynomial goodness of fit compared to the linear goodness of fit, for the raw undifferenced data. This drop of R-Squared measured the departure from the assumption that financial time series are normally not linear, and tends to be polynomial in nature. Yet most accounting textbooks assu= me that they are linear and fail to mention the reduced goodness of fit due to= the assumption that it is linear within the "relevant range," and ways to measure the loss = of goodness of fit, and forecasting errors that may follow and the added risk associated with them. &nbs= p; &= nbsp; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp;

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp;

Differenced Quarterly Time Series Data=3DSales Gain2002=3DSales2002-Sales2001: Linear To Polynomial Comparison Of Differen= ced Raw Quarterly Time Series Data Of Linear And Polynomial R² & Funct= ion

Differenced Data Only- Comparing Poly to Linear

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; =

This part of the study repeats the previous discussion, accept that it is using the Differenced Quarterly Time Series Data=3DSales Gain2002=3DSales2002-Sales2001, instead of the raw undifferenced data, to a= ccount for the dependence of the observations in the reported historical industrial financial time series, using the "Linear To Polynomial Comparison Of Differenced Raw Quarterly Time Series Data Of Linear And Polynomial R² & Function."

1st it describes the +Gain or -Loss of the transformed= raw time series data into the Differenced time series data and the calculation = of R² and the Function respectively. It starts with the Differenced Polynomial R-Squared of 64.58%, and its functio= n of y =3D 169.86x6 - 5772.9x5 + 76527x4 - 503319x3 + 2E+06x2 - 3E+06x + 2E+06.<= span style=3D'mso-spacerun:yes'> Then the Differenced Linear R-Squa= red of 1.15% follows with the its function equation of y =3D -5173.8x + 67738, and= the Differenced R**2 Drop 63.43% = that is due to the departure of the independence of observations assumption, nam= ely regression auto correlated time series observations, as if they where indep= endent. &= nbsp; &nbs= p; &= nbsp; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp;

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp;

Raw and Differenced Data Combinations- Comparing Raw to Differenced time series R-Squared for Poly and R-Square and Slope for Linea= r

This discussion starts with Raw to Differenced Compari= sons of R² Function of the Ra= w Data Polynomial R-Squared of 90.12% and its mathematical function of y =3D 40.51= 6x6 - 1338.7x5 + 15734x4 - 77264x3 + 135313x2 + 32234x + 903575, compared to the = Differenced Polynomial R-Squared of 64.58%, and its equation function of y =3D 169.86x6= - 5772.9x5 + 76527x4 - 503319x3 + 2E+06x2 - 3E+06x + 2E+06. With a 25.54% R-Squared drop due to eliminating the dependencies from period to period and observation to obser= vation. Thus, the lower R-Square maybe a m= ore realistic measure of the true goodness of fit, of 65%, with a risk premium = of a penalty for departing from the independent observations assumption. &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &nbs= p;

2nd this analysis describes the R² and Slope Function of the Raw Data Linear R-S= quare 63.96% and its slope of $969,= 208, as well as its equation function of y =3D 43464x + 969209, less the Differe= nced Linear R-Squared of 1.15%, and its slope of $67,737, as well as its equation of y = =3D -5173.8x + 67738. The same pa= ttern appears again, as expected, with the R-Squared drop of 62.81%, and its slope drop of $901,471 due to using only differences instead of the much larger raw value= s.

&= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp;

Table 0

R² Goodness of Fit Differences Between Differenced and = Raw data of the Polynomial of the 6rd Degree and the Linear Model<= /span>

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp;

&nbs= p; (Microsoft Excel Object)

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; =

&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp;

Literature Review

Forecasted Annual Reports and the Role of the Cost Fun= ction and the Relevant Range

Forecasted Annual Reports has been researched extensively in the literature, across multiple countries. "The Construction of an International Investors' Perception Model for Corporate Published Forecasted Financial Reports for the United States, Great Britain, and New Zealand,&qu= ot; (Rushinek, A., Rushinek, S., Chang, L. and Most, K., 1983)[2]= [3]= for Managerial And Decision Economics (MDE), is one such example. In the center of such studies are = the cost function and the “Relevant Range.” Once the accountants forecast the = Sales, they can forecast the costs, after they have calculated the cost function.<= span style=3D'mso-spacerun:yes'> For this purpose, they have to fig= ure out what is the cost function, and that depends on whether the activity lev= el is within the relevant range, in which traditionally accountants assume that the total cost is a Total Mixed Cost (TMC) function equal to a Total Variab= le Cost (TVC) plus a Total Fixed Cost. Most forecasters use some variation of a regression analysis for such purposes.

Another common application of forecasting financial performance is "Using Financial Ratios to Predict Insolvency." (Rushinek and Rushinek, 1987)[4]= In such a case, the idea is to forecasts the cost, the cash outflows that t= hey produce, and the point at which the net cash outflows exceed the enterprise cash reserves, driving it into bankruptcy.= The common methodology of such an analysis is a discriminant analysi= s, which is similar to the regression analysis, in that the user has to decide whether the function is linear or not linear. Most users assume that it is linea= r and proceed without additional testing. The problem is that the linearity assumption may not be valid, and therefore the forecasts may not be as accurate, due to the low goodness of = fit between the actual data and the mathematical function that describes the da= ta.

Using Experts for Detecting & Litigating Forecast Errors and Frauds for Oil and Gas Industry

The reasons for some of such bankruptcies i= s due to the deficiencies in "Evaluating Asset Safeguarding and Data Integrity,” especially “in the EDP Audit" process. (Rushin= ek and Rushinek, 1989)[5] For that purpose companies have been "Using Experts for Detecting & Litigating Computer Crime", in Managerial Auditing application. (Rushi= nek and Rushinek, 1993)[6] Such "Forensic Audits = of Stock Market Forecasts” frequently rely on experts in a certain indus= try and even in a specific company such as “Texaco & The International Oil and Gas Industry,” looking at “Financial Models” for specific cases of “Online Broker Litigation." (Rushinek and Rushinek, 2003)[7] For such cases, an expert may have= to rely on consolidated industry financial reports, such as the reports produc= ed by the US government.

For example in "Tax Fraud Forecastin= g for the Petroleum Refining Industry,” experts compare the “Financial Ratios” of individual companies to their industry standards. They use “Reversed Accounting Theory Applied to” a specific company, such as “Shell Oil"= to figure out what their taxes should have been based on industry standards. Then, one can determine whether the differences between what the taxes should have been and what taxes have been reported, appears to raise the red flags of tax fraud. (Rushinek and Rushin= ek, 1997)[8]=

Some times “Managerial auditors mixed cost forecasting assumption departure error” lead to “litigation and professional liability,” e= ven if it is not fraud but lack of due diligence. Such errors may increase the “risk” of material misstatements of financial reports. Again the reduction of such errors= and their risk depends on using consolidated industry data; in this case it is “the electronics industry.” (Rushinek and Rushinek, 1997)[9]= For example, auditors may assume t= hat the cost function is linear when in fact it is not linear. Therefore, the cost estimate based= on forecasting errors are off, leading to enormous cost over-run, litigations,= and other additional difficulties. For this reason it is important to raise the goodness of fit on the cost functi= ons and the data that they represent.

For this reason researchers have been “Rating and Ranking Best Practices” in certain companies and industries, such as the “British Petroleum Oil Company and the Oil & Gas Industry.” That is especially important in the “Sales to Cash Forecasting” areas, “Univariate Regression Trend Analysis and Computer Modeling” to correctly estimate the future cash needs and avoid bankruptcies. Especially the volatile Oil and Energy industries. (Rushinek and Rushinek, 1997)[10]

As experts repeat their wo= rk, running a similar computer model over and over again, they tend to automate such an analysis. A case of ”Litigation in Pennzoil and the Petroleum Refining Industry,” u= ses “A Financial Ratio Automated Peer Review Analysis of Variance” using “Internet Software Development” to automate the analysis = of the “OIL, GAS & ENERGY” industry. (Rushinek and Rushinek, 1= 998)[11]

Internet Software Development of Expert Sys= tems (ES)

A regression analysis is basically a correlation betwe= en the dependant variable and the independent variable. It is similar to”Correlating Investors’ Preferences to Stock Characteristics.” That is “An Algorithm for In= ternet Software Decision Support System Development in the Crude Oil & Refining Industry.” (Rushinek and Rushinek, 1999)[12] The computer program tends to make decisions in a much more structured and methodical way than experts do. The idea is to emulate, or simulat= e a human expert as much as possible using a computer system in addition to a h= uman expert. Similar methodology c= an be used for ”Oil & Gas Market, Sector & Company Relative Strength Congruity Trading,” assisting an expert stock trader using “Internet Automation Approach” to speed his trades and make the stock market forecasts more accurate and less risky, when applied to a cert= ain company and a certain industry, such as “Chevron Corporation’s Stocks” and the “OIL, GAS & ENERGY” industry. (Rushinek and Rushinek, 2000)[13]

Forensic experts a= nd accounting professors can use “Internet Fraud Auditing” methodology in teaching by applying “A Simulated Health Care Industry” case st= udy to teach the jury and the students the concepts of calculating the abnormal profits that result from phony sales and/or understated costs. An expert, an ES, or a professor c= an regress an individual health care providing company on consolidated industry financial reports to identify red flags of fraud, and help “un-cook t= he books.” (Rushinek and Rushinek, 2000)[14]

<= /span>

“The Role of= the Forensic Accountant in Calculating the Damages Using the ‘But-if̵= 7; Analysis” depends on the validity of several assumptions about the co= st function. (Rushinek and Rushinek, 2001)[15] Such assumptions include the clear definition and the position within the “Relevant Range.” Furthermore, another assumption deals with the linearity of the regression model. The advantages of linearity include but are not limited to the mathematical and statistical simplicity. Since, the linear function is simp= ler than the nonlinear functions, it is easier to program it into the Expert Sy= stem (ES), and it is much easier for the user to understand it, as well as easier for an expert to explain it to a jury, or for a instructor to teach it to students. To program it into = an ES, the expert has to provide the ES with decision rules, to decide whether the cost function is linear or not. This paper provide a methodology of doing just that, in a way that is very intuitive, easy to understand, easy to teach, and very easy to demonst= rate manually in a classroom or courtroom environment (By check marking a couple= of boxes in the regression wizard in Microsoft’s Excel or similar spreadsheet programs). <= /o:p>

<= /span>

This method simply calculates both functions, compares the R-Square differences in the Goodnes= s of Fit, and selects the linear function, if the gains of using the nonlinear functions are small enough to be negligent (less than .1% for example). To further test the validity of the function, the expert and/or the expert system applies it not only to the company in question but also to the industry. The inclusion of the industry illustrates that such a cost function is valid not only in the company in question, but in the entire industry. This is particularly important for forensic situation where the comp= any data is not available, unreliable, incomplete, or simply does not contain enough observations to conduct a reliable regression analysis. Therefore, in the last case that we reviewed, the expert had to rely on such an analysis to calculate the damag= es due to a day trader from his broker, since the broker stopped the trade cau= sing the trader significant losses.

<= /span>

Continuing Professional Education (CPE) Teaching Accou= nting Professors about Model Selection

Continuing Professional Education (CPE) courses play a= very prominent role in testing new theories about regression methods that should= be simpler to use and understand, concerning selection of the model with the b= est goodness of fit, which is also the simplest. Such CPE about Model Selection hav= e been focusing recently about integration of new technologies into forensic accounting expert witness testimony and computer litigation support. This courses demonstrate to accoun= ting professors, expert witnesses, and Lawyers as Social Media Us= ers, using such networking sites such as LinkedIn® and Facebook®, Myspace®, Blogs, and Wikis, while dealing with “Sarba= nes Oxley Microsoft Accelerator Forensic Accounting, Expert Witness Testimony, & Computer Litigation Support” (Rushinek and Rushinek, 2005)[16] Teaching s= uch CPE workshops for organizations such as the American Accounting Association (AAA) shows that it is an up and coming new social media networ= king technology. Since it is relat= ively new, it is hard to evaluate it. Therefore, researchers develop new assessment techniques to evaluate= it. (Rushinek and Rushinek, 2007)[17] Such "Assessment” of CPE Video Podcasts and Blogs requires some "Hands-on" experience to learn how to “Consult, Publish &am= p; Teach Forensic Auditing, Expert Witness Testimony” using new software such as Microsoft® Office Accounting” for Computer Litigation Support. It demonstrates how = to transcribe the lecture or testimony and automatically publish the narrative, the video, the supporting documents on a peer-reviewed online journal, such= as the IT (Information Technology) Journal.[18]

Due to the popularity of internet video forensic exper= ts are learning how to deploy it in their witness testimony, in the courtroom and = in the classroom (Rushinek and Rushinek, 2= 008) In the case of brokers’ misconduct; the surveillance video can show h= ow the broker approaches the day trader pulling out his computers power plug in middle of the trade. This is = an example of “How to Use New Media and Technology to Repurpose Classroom Instruction for the Accounting and Business Educator”, in an “International Fraud and Forensic Accounting Education Conference,= 221; in Las Vegas, Nevada, where the most sophisticated repurposed forensic surveillance systems play a prominent role in auditing, expert witness test= imony and litigation support. As the expert explains the concept of the regression goodness of fit, on can obser= ve the students, and/or the jury in real time and assess their reaction.<= /o:p>

<= /span>

Another way to measure the students and jury reaction to a new method is by a mock trial. (Rushinek and Rushinek, 2008, 2009)[19] "Mock Trial & Forensic Faculty” can deploy the “WWW Marketplace, Jury, Judge & Attorneys” exposing them to the new me= thod in the "Courtroom & Classroom” using new “Instructional Technology” to evaluate their reaction. So far such reaction has been positive, so the authors proceed with this research direction.

Common Confusions in Manag= ement Accounting Textbooks and Accountants Communication

Mixing Up Total and Unit Costs and Applying= the KISS & GAAP principle

Total Mixed Cost =3D Fixed Cost Element+ Va= riable Cost Element

The typical Total Mixed Cost (TMC) equation frequently omits important terms and confuses the differences between total= and unit costs (Warner and Jones, 2009 p 167). A more consistent and accura= te statement of the TMC equation will be the following:

Total Mixed Cost (TMC) =3D Total Fixed Cost= (TFC) + Total Variable Cost (TVC)

Adding the prefix “Total” in fr= ont and dropping the suffix “Component” at the end improve the dist= inguish more clearly between the concepts of Total Costs (TC) and Unit Cost (UC). Furthermore, it may be more consis= tent to stick to one term such as components or element, rather than adding too = many terms to discuss the same thing, as the Generally Accepted Accounting (GAAP) principle of consistency suggests. Likewise, it may be less confusing to the reader and the students. If the work “components̶= 1; works, why not use the “KISS principle and Keep It Simple Stupid,R= 21; or “if it works, don’t fix it.”

Abbreviating the variable names as soon as possible maybe another improvement as it makes it more familiar to the students, when they have to deal with so many abbreviations later on, as the mathematical derivations become more complicated.

Adding Currencies as Activity Measures to t= he Physical Units of Activity Measurement

Typical authors of Management Accounting te= xtbook tend to describe the cost function as a “Scatter Graph for … Co= st and Miles Driven” or a similar physical unit of activity measurement. Its advantage is= that it is simpler but tends to be limited to situations with only a single acti= vity to measure cost. Most compani= es, most costs, and most activities involve more than a single physical unit. Therefore, it is important to supp= lement the examples of currencies, such as Dollars of Sales, as an activity unit of measurement, to physical units, such as Miles Driven, or Packages Delivered= . As one consolidates multiple activities, for example for a package delivery company, combining Miles Driven and Packages Delivered, on can use the Doll= ar Values of the revenues to measure the activities, in addition to the physic= al values. Therefore, the present paper focuses on such supplemental example of decomposing the components of= TMC as a function of Dollars of Sales. That is even more important for consolidated financial statements, w= hen multiple subsidiaries have totally different products and lines of business, such as IBM Corporation that sells computers and consulting services. This is even more important when applying the same process to an entire industry, such as the manufacturing industries, or the consolidated “U.S. Manufacturing, Mining, And Wholesale Trade Corporations.” (US Census Bureau, Dec. 2010)

A lot of these confusions, seem too trivial= to accountants, while they are communicating among themselves, the trouble is = that many times, accountants are communicating with the public of jurors, as forensic expert witnesses, dealing with the public, who is trying to read t= he financial reports, and make some sense out of them. At such times, it is quite difficu= lt for the public to figure out what and where is the relevant range, and why it i= s so relevant to cost forecasting and to a forensic “But If” type of analysis, as well as to teaching management accounting and similar topics.<= o:p>

Data Collection, Access, Reliability, and Sampling Errors

The US Census Bureau provided= the data in “The quarterly publication, Quarterly Financial Report (Q= FR) for Manufacturing, Mining, and Trade Corporations – Third quarter 2009 (QFR/09-Q3), December 14, 2009, US Census Bureau Report. (US Census Bureau, 2009)

<= /p>

The data describes the “After-Tax Profits and Sales, Third Quarter 2009 - Seasonally Adjusted,” using ARIMA (Auto Regressive Integrated Moving Average) me= thod. It focuses on Manufacturing Corporations. U.S. manufactur= ing corporations’ seasonally adjusted after-tax profits in the third quar= ter of 2009 totaled $88.5 billion, up $36.4 (±0.5) billion from the after-tax profits of $52.1 billion recorded in the second quarter of 2009, = but down $29.3 (±1.6) billion from the $117.8 billion after-tax profits recorded in the third quarter of 2008. = ;

While the data describes both Durable Goods Manufacturers, and none-Durable. Our model focuses on none durable = manufacturers, such as the Oil and Gas industries, due to the importance of forecasting correctly and the high volatility of the industry as the political scene and the financial markets fluctuate.

Mining Corporations &= nbsp; are part of the Oil and Gas industries and may need some special attention. “Third quarter 2009 unadjust= ed after-tax profits for mining corporations with assets of $50 million and ov= er totaled $4.7 billion, down $26.7 (±0.1) billion from the $31.4 billi= on after-tax profits recorded in the third quarter of 2008. Compared with seco= nd quarter 2009 after-tax profits of $2.3 billion, third quarter 2009 after-tax profits were up $2.4 (±0.1) billion.”

Access to QFR Data

“This press release can= be viewed today in portable document format (.pdf) at our QFR internet website= http://www.census.gov/econ/qf= r/index.html. Data in the release are drawn from a more comprehensive data set published in the Quarterly Financial Report for Manufacturing, Mining, and Trade Corporation= s - Third quarter 2009 (QFR/09-Q3). This quarterly publication is available for downloading at our QFR internet website http://www.census.gov/econ/qfr/index.html. The quarterly publication includ= es summary statements of income and retained earnings, balance sheets, and rel= ated financial and operating ratios for manufacturing, mining, and trade corporations. Data presented = are classified by industry and asset size. = ;

Reliability of the Estimates =

Data in this press release are based on quarterly financial reports from approximately 9,000 U.S. corporations. The data are estimated from a sample survey and are subject to sampling and nonsampling errors.

Sampling error occurs because= only a subset of the entire population is measured. Estimates of sampling error can be computed based on the sample and used to construct confidence intervals aro= und the estimates. Statements of = change appearing in this report include 90 percent confidence intervals. Thus, a statement in the report su= ch as “up $2.5 (± 0.2) billion” estimates the interval (+$2.3 billion to +$2.7 billion) within which the actual value is likely to fall i= n 90 percent of samples of the same size and design, drawn from the same populat= ion. If the range of estimated change contains zero (0), then it is uncertain whether there is an increase or decrease; that is, the change is not statistically different from zero (0).= For any comparison cited in the text without a confidence interval, the change = is statistically significant at the 90 percent confidence level. &= nbsp;

Nonsampling error encompasses= all other factors that contribute to the total error of a survey, including response errors, nonresponse, and coverage errors. Although no direct measu= res of nonsampling error are available, precautionary steps were taken in all phases of the collection, processing and tabulation of the data in an effor= t to minimize their influence. The quarterly publication, Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations – Third quarter 2009 (QFR/09-Q3), includes more detailed explanations of nonsampling and sampling error, and additional measures of sampling variability.”

Visual Data Inspection of the Lowest and Hi= ghest Bounds of the Relevant Range

The data set describes the following variab= les: Years, Sales (x), and Expenses (y). For example at the lowest bound of the Relevant Range is year 1998, = with Sales of $444,850 (x), and Expenses (y) of $415,375, in millions of Dollars= . At the highest bound of the Releva= nt Range is Year 2008, with Sales of $923,334, and Expenses of $848,934. The TMC chart graphs the Expenses = or Costs y, the dependant predicted variable, as a function of Activity or Sal= es, x, the independent or predictor variable.&= nbsp; As one can observe from the chart and the data, a company with about $600,000,000,000 would be in the middle of the relevant range, where the validity of the linearity assumption is almost 100%. By observing the Poly and the line= ar trend lines, one can see that they are perfectly aligned. In contrast, as one moves up, furt= her into the upper bound over the Sales label of $800,000 the Poly function dep= arts from the Linear function curve, that is where the goodness of fit drops to 99.7%.

Before getting into the regression analysis= and the statistical and mathematical discussion, it is important for an Expert System (ES) as well as an Expert, especially a forensic accountant providing expert witness testimony to the jury, to explain the data in a jargon free manner and in layman terms that appeal to the common sense. Likewise, an instructor teaching elementary management accounting should describe such issues. This is when a simple 2 dimensional scatter graph and the trend lines can be very visually appealing, to preview the regression analysis and the more mathematical discussion. As one looks at t= he scatter graph, one can see that both trend lines increase from left to righ= t, as the volume of Sales grows, and the Expenses grow proportionately, at a constant slope, UVC, which means that we are inside the relevant range, whe= re the Relevant Range assumptions of functional linearity are valid.

This policy provides some kind on an intern= al control that enables the observer to quickly, by inspection of the chart, t= rap errors and correct them at an early stage before additional work and relian= ce has been erroneously placed on the data.&n= bsp; Such inspection should verify that the cost function shows that the total variable costs (TVC) increase as the activities increase ($Sales), at= a constant rate.

Regression Analysis – Focusing on the Concepts rather than the Translation

In a typical “Regression AnalysisR= 21; authors describe the TMC function again this time decomposing the Total Variable Cost (TMC) into its components, but still failing to name it accurately as TMC and TFC, instead they keep omitting the prefix “Total” from Fixed Cost and Variable Cost, as follows:

Total Cost (omitting Mixed) =3D Fixed Cost = + (Unit Variable Cost x Activity Level).

A clearer, step wise more explicit alternat= ive may be:

Total Mixed Cost (TMC) =3D Total Fixed Cost= (TFC) + Total Variable Cost (TVC) and

Total Variable Cost (TVC) =3D Unit Variable= Cost x Activity Level, therefore:

Total Mixed Cost (TMC) =3D Total Fixed Cost= (TFC) + Unit Variable Cost x Activity Level or

Y=3D a + bx,

Where:

y =3D TMC, “the dependent” pred= icted variable, “that changes in response to a change in x.”

a =3D TFC The 1^ST coefficient of= the independent predictor variable, “The y intercept, or the amount of y = when x is zero.”

b =3D UVC The 2^nd coefficient of= the “independent predictor variable.”

x =3D “The independent” predict= or “variable.”

Including the TMC, TFC, and UVC, facilitate= s the reader’s transitions from the accounting terminology to the statistic= al mathematical terminology. Thi= s way the reader may avoid making the leap of faith in the translation all by themselves, and can focus on understanding the concept rather than translat= ing the accounting terminology to the statistical mathematical terminology. The translation efforts compounded= by the new terms, such as least-squares method and linear regression, forward & backward forecast, trend line, in addition to regression analysis may= be “the straw that breaks the camel’s back.”

The Linearity Assumption of the Relevant Ra= nge and the Regression Analysis Empirically Tested

Regarding the “Relevant Range” forensic experts, providing expert witness testimony and computer litigation support, accounting authors, accountants and textbooks assume implicitly th= at “variable cost is constant per unit within the relevant range.” (Werner, Jones, 2009, page 161) They make the linearity assumption, without providing any empirical evident to that effect or a method to test whether = the TMC function is linear or not. This paper provides such evidence along with an automated method that enables the replacement of a linear function by a more fitting function, such as a polynomial function. Instead = of just assuming that we should always use a linear regression, this paper off= ers a novel empirical automated proofing methodology, along with the traditional statistical hypothesis testing methodology.

Textbooks typically state that “decis= ion makers usually assume activity levels will be within a company’s rele= vant range.” (Werner, Jones, 2009, page 161) Therefore, they also assume that t= he TMC function is linear, without a simple empirical, speedy automated methodolog= y to prove and support such a decision. This paper automatically replaces the linear function, with a more a= ppropriate function, such as a polynomial function, in the event of a significant departure from linearity. Otherwise, if the linearity assumption holds, it provides empirical evidence to such an assumption that will promote accuracy of forecast, as w= ell as reliability of an expert system, or an expert witness, business decision ma= ker that has to forecast or explain cost as a function of activity.<= /span>

R² Goodness of Fit Difference between = the Polynomial of the 3rd Degree and the Linear Model

Table 1	R² Goodness of Fit Differences Between the Polynomial of the 3rd Degree and the Linear Model
	Column L	M	N	O	P	Q
Line 66	Model	Regression	R²	y
Line 67	1	Poly2	9= 9.9%	y =3D 1E-07x² + 0.701x + 80317<= /p>
Line 68	2	Poly3	9= 9.9%	y =3D 1E-08x³ - 0.005x² + 1372.x -1E+0= 9
Line 69	3	Linear	9= 9.8%	y =3D 0.885x + 22809
Line 70		Difference	*0.1%*	=3DN68-N70
Line 71		TMC =3D 0.885*Sales +$ 22,810

Table 1 describes the “R² Goodne= ss of Fit Differences Between the Polynomial of the 3rd Degree and the Linear Mod= el of a univariate regression in an excel spreadsheet in Column L, M… and line 66, starting with the Model, Regression, R², y function of a trend line, and in Line 67, model number 1, Polynomial of the 2^nd degr= ee (Poly2) with its respective pairs of R-Square, 99.9%, and the trend line function of y =3D 1E-07x2 + 0.701x + 80317. The table en= ds with Line 70, the Difference &nb= sp; 0.1%, between the Linear and the Poly3 function R-Squared, goodness of fit. This difference supports the linea= rity assumption, showing that the gain in goodness of fit due to the departure f= rom linearity is lower than .1% for a goodness of fit of 99.9%. Therefore, the expert as well as t= he expert system can proceed with linear model simplicity without significant = loss in forecasting accuracy and goodness of fit.

Rule Automation of the Selection of a Total= Fixed Cost (TMC) Trend Line Regression Model

The experts set up a predetermined rule that states the condition of selecting a certain default trend line regression model. The user can customize= the values of the decision rule according the minimal needed reliability, goodn= ess of fit, and other circumstances. Such rule may specify that if the R-Square between any pair of forecasting trend line methods difference is .1% or low= er the Expert System (ES) or the expert (author, accountant, etc) should defau= lt to the simplest linear model. Following is an example of a couple of rules related to the differen= ce between the R-Squares of the Linear and the Poly model, and their Hypothesis Testing or the value of the R-Square:

If-

Rule 1: If the Linear R-Square minus the Polynomial R-Square < .01 %, and

Rule 2: The Linear R-Square > .9 or 90%,=

Then default to the-

Action: Linear trend model for the Total Mi= xed Cost TMC =3D TFC + UVC*Sales.

Otherwise, select the model that maximizes = the R-Square but is more complicated. The table ends with the excel formula of =3DN68-N70, followed by Lin= e 71, and the cost function of TMC =3D 0.885*Sales + 22,810, of the linear trend = line, restated in the accounting rather than the statistical mathematical terminology.

Raising the level of such simplicity, as in= the linear model compared to the polynomial models is especially important when dealing with students in an elementary course, business executive who may n= ot have studied accounting, math, or statistics, or with a lay court room (Jud= ges, Lawyers, and especially jurors) = Maximizing The Simplicity Among Polynomial, Exponential, Logarithmic, Moving Average, = And Power Model, Without Compromising The Goodness Of Fit Of The Forecasting Tr= end Line

The typical management accounting textbook describes how to “forecast backward and Forecast Forward,” it instructs the reader “to click on the Options tab,” but fails to mention to check mark the box for “R-Square,” the goodness of f= it of the trend line and the forecasting model. This paper illustrates the importa= nce of such a measure, and how easily it can be added to a manual regression calculation. Moreover, for fu= rther simplicity, the user can automate it and program it using VBA (Visual BASIC (Beginners All Purpose Symbolic Interactive Code) for Application) programm= ing language built into excel, and other spreadsheets, as well as more advanced programming languages for ES.

As the ES program automates the calculation= of the goodness of differences among a dozen of models, such as Exponential, Logarithmic, Moving Average, and Power model, the ES will proceed to pick t= he simplest and the best fit, the linear model in the present case, without exposing the author, business executive, reader, courtroom to the mathemati= cal and statistical complexities, yet intuitively enable them to understand and correctly assess the goodness of fit and its importance. Using the earlier narrative, the E= S will proceed to generate the appropriate consistent and clear text, to facilitate the understanding of the concept for a novice student and a business execut= ive, as well as making it easier for an instructor to teach.

Summary Conclusions and Implications

Summary

In summary, this paper reviewed the narrati= ve that describes cost behavior and related “does” and “don’ts.” F= or example, the paper reviews the tendencies of accountants to mix inconsistent terminology, for a single concept, unnecessarily confusing the reader, such= as cost components and elements. Inconsistently adding or dropping the prefix “Total” from Total Mixed, Fixed or Variable Costs, without any apparent reason. For example = using Fixed Cost to describe the Total Fixed, while using Variable Cost for Unit Variable Cost. So, it may not= be perfectly clear whether they are talking about Total Fixed, Mixed, or Varia= ble cost, or maybe about Unit Fixed, Mixed, or Variable Cost. Another issue is making assumption= about the relevant range, linearity, and trend line model selection, with complet= e disregard to the goodness of fit, or any empirical evidence to support such an assert= ion, such as the R-Square. Another issue, maybe the selection of activity measurement that is limited to the number of physical units, excluding the possibility of using Sales in Dolla= rs or other currencies, etc.. Li= kewise, the paper suggested better narratives, and automated evidence producing met= hods to support such assertions and assumptions or depart from such assumption as the circumstances dictate. It provided empirical evidence for the case of the superior simplicity of a li= near model over a polynomial model, and its related rules, as well as their deci= sion parameters.

Conclusions

The conclusion is that for non-durable manufacturing the linear function maybe the best within the relevant range,= of annual report of Total Mixed Cost as a function of Total Variable and Fixed Costs. Likewise, this paper s= uggest that in addition to considering the data of an individual product, company,= or consolidated financial statements, an entire industry trend line may further enhance the model and control for early detection of errors, outliners, or unusual deviants from the national industry standards. It concludes that automation may i= mprove the accuracy without over burdening the user with having to understand fully complicated computational methods, statistical analysis, and mathematical constructs. Yet making speedy assumptions that are supported by the evidence, promoting simplicity without compromising accuracy and validity.

Implications

The implications of this study are that a l= ot more work may be done in testing such conclusions over longer period of tim= es, various levels of aggregation all the way to an individual product. Likewise, applying the same concep= t to varying combinations of dependent and independent variables. For example, testing the same methodology for Sales Forecasting as a function of the Julian date, prior to calculating the costs as a function of sales, due to the fact that Sales Forecast normally precedes cost forecasts.

the implications for trading systems strategies are particularly important. In de= aling with forecasting the profitability of the trade using regression analysis.<= span style=3D'mso-spacerun:yes'> Forensic accounting may help clari= fy frequent mistakes and common confusions, especially in estimating the damag= es using a "but-if" regression analysis. Future studies will demonstrate ho= w to apply these clarifications to automated international portfolio trading sys= tems strategies for currencies, futures, equities and derivatives. Using pips, yens and dollars, in a variety of time frames of daily, hourly, and tick by tick quotes, such mode= ls will help budget the trades and analyze the results, on commercial trading platforms such as Trade Station®, Ninja Trader®, Stock Finder®, eSignal®, Metastock®, Wealth-Lab®, Neuroshell Trader®, Amibroker®, Stratasearch®, Worden Brothers Blocks®, Aspen Graph= ics®, Workstation Tradingsolutions®, Tradecision®, Trade Navigator®, Ensign Software®, VT Trader®, Swingtrader®, TD Ameritrade®, & Strategydesk® partic= ularly in "Confirming Price Trend," using mixed international securities (indexes, equities, currencies, commodities, futures, etc), in a variety of markets.

<= /p>

This study demonstrated the advantages and superiority of some methods over other methods along with their disadvantages.&nb= sp; In the future, some correction and adjustments to linear regression = may improve its performance, while reducing the risk of errors, along with rais= ing the confidence of the traders, by improving the accuracy of trading strateg= ies and trading signals. Such a m= easure of risk, maybe the R-Square drop due to the departure from the basic regres= sion assumptions, or using linear instead of polynomial models.

8-27-10_Mon_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVE AND VALIDITY

//////////////////////////////////////////////////////////= /////////////////////////////////////

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=

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"Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records o= ver the WWW AV IP Network Security Surveillance Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-Up Instructions for Distance or Blended Education and CPE, Emerging and Innovative Research Session" 2= 009 American Accounting Association (AAA) Annual Meeting = NY, NY., Tuesday August 4, 2009, retrieved 10-27-2009 http://aaahq.org/AM2009/Emerg= ing.cfm Rushinek, A. and Rushinek S. "Health Care Forensic Information Technology Networks of Expe= rt (FITN) Witnesses Teams Researching Investigative Tax, Accounting & Audi= ting (FIA) Forecasts Archives of Testimony, Assessment, Publishing, Teaching, Service, & WWW Multi-Media Consulting," = 2009 AAA American Accounting Association, Annual Meeting Proceedings, August 1-5, 2009, New Y= ork, New York. Retrieved 10-27-2009 http://aaahq.org/AM2009/Emerg= ing.cfm Werner, M. and Jones, K. (2009), Introduction to Management Accounting – A User Perspective, Third Edition, Kendall Hunt Publishing, 200= 9. US Census Bureau (2009), the quarterly publication, = = Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations – Third quarter 2009 (QFR/09-Q3), December 14, 2009, US Census Bureau Report http://www2.census.gov/econ/qfr/current/qfr_mg.pdf Retrieved 1-15-2010= \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ Appendix: Chart 1: Screen Shot 1 Sample Data Set 1 Foot Notes to Data Set 1 1-The seasonally adjusted estimates provided in this table were derived using a combination of SIC-based and NAICS-based estimates. = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; 2- Beginning with the 2007Q4 data release, updated Regression ARIMA (Auto Regressive Integrated Moving Average) models for seasonal adjustment were implemented to improve the identification of seasonal patterns in unadjusted net sales and net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing. &= nbsp; &nbs= p; &= nbsp; &nbs= p; 3- Revised &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &nbs= p; &= nbsp; &nbs= p; = ///////////////////////////////////////////////////// Appendix 1: Sample Forensic Accounting Continuing Professional Education (CPE) Workshops for t= he American Accounting Association (AAA) Rushinek, A. and Rushinek, S. “Sarbanes Oxley Microsoft Accelerator Forensic Accounting, Expert Witness Testimony, & Computer Litigation Support Workshop”, American Accounting Association Annual Meeting 2005= , Saturday, August 4,= 2005, San Francisco, California, sponsored by Tea= ching and Curriculum Section. http://aaahq.org/AM2005/cpe/cpe02.htm An International Meeting of The American Accounting Association 2005 Annual Meeting August 7–10, 2005 San Francisco, California Come to the City by the Bay! ---------------------------------------------= ----------------------------------- CPE Session 2: Saturday, August 6, 8:00 AM &#= 8211; 4:00 PM Sarbanes-Oxley Microsoft® Accelerator For= ensic Accounting, Expert Witness Testimony and Computer Litigat= ion Support Description/Objectives: Sarbanes-Oxley Microsoft Accelerator Forensic accounting workshop demonstrates how Accounting academics and practitioners= can use Microsoft Share Point Team Services and Share Point Server to assist an internal audit team in implementing, investigating, and improving compliance audit services. It describes how to become an Accounting Expert Witness and= how to provide Expert Witness Testimony, with an emphasis on HIPAA (Health Insurance Portability and Accountability), Sarbanes-Oxley, and Basel II Compliance Forensic Accounting. We will discuss R&D for experienced expert witnesses, including the issues of dealing with the forensic team of professionals. This workshop focuses on using forensic accounting technique= s in general, and providing Computer Litigation Support in particular. The works= hop objectives include helping experienced expert witnesses to review their ski= lls and upgrade them, while demonstrating to prospective expert witnesses the nature of the work, its intellectual challenges and financial opportunities= and rewards. We plan to meet such objectives, by lectures, role playing, demonstrations, and most importantly active participation of the audience as well as discussions and arguments of pros and cons of various issues.<= /o:p> Format/Structure: DVDs for litigation support, including the pr= os and cons of different standards such as DVD+/-RW/VR (Digital Versatile Disk, Re= ad Write, and Video Recording), DVD-RAM (Random Access Memory), HD (High Definition)-DVD, Blu-Ray DVD, DVD Copying, dubbing, burning, and recording, focusing on the technical and legal facets. Intended Audience: This study targets practicing and academic litigation and litigation support team members, including lawyers, economis= ts, accountants, auditors, doctors, engineers, corporate executives, expert witnesses, plaintiffs and defendants, as well as students and others intere= sted in the topic. This workshop will review the financial opportunities for exp= ert witnesses, fee structures, relationship with the legal profession, and opportunities for students that want to specialize in the area of forensic accounting and litigation support, and/or combine the study of accounting a= nd law, and computer information systems. Presenters: Sara Rushinek, University of Miami= Avi Rushinek, University of Miami<= /span> Sponsor: Teaching and Curriculum Section ---------------------------------------------= ----------------------------------- * Denotes special requirements or prerequisit= e Back to CPE Listings Back to Annual Meeting H= ome Page <= o:p> <= o:p> <= o:p> An International Meeting o= f the American Accounting Association 2005 Annual Meeting August 7–10, 2005 San Francisco, California Come to the C= ity by the Bay!<= /h2> CPE Session 2: Saturday, August 6, 8:00= AM – 4:00 PM Sarbanes-Oxley Microsoft® Accelerat= or Forensic Accounting, Expert Witness Testimony, and Computer Litigation Support Description/Objectives: Sarbanes-Oxley Microsoft Accelerator Forensic accounting workshop demonstrates how Accounting academics and practitioners can use Microso= ft Share Point Team Services and Share Point Server to assist an internal audit team in implementing, investigating, and improving compliance aud= it services. It describes how to become an Accounting Expert Witness and h= ow to provide Expert Witness Testimony, with an emphasis on HIPAA (Health Insurance Portability and Accountability), Sarbanes-Oxley, and Basel II Compliance Forensic Accounting. We will discuss R&D for experienced = expert witnesses, including the issues of dealing with the forensic team of professionals. This workshop focuses on using forensic accounting techniques in general, and providing Computer Litigation Support in particular. The workshop objectives include helping experienced expert witnesses to review their skills and upgrade them, while demonstrating = to prospective expert witnesses the nature of the work, its intellectual challenges and financial opportunities and rewards. We plan to meet such objectives, by lectures, role playing, demonstrations, and most importa= ntly active participation of the audience as well as discussions and argumen= ts of pros and cons of various issues. Format/Structure: DVDs for litigation support, including the pros and cons of different standards such as DVD+/-RW/VR (Digital Versatile Disk, Read Write, and Video Recording), DVD-RAM (Random Access Memory), HD (High Definition)-= DVD, Blu-Ray DVD, DVD Copying, dubbing, burning, and recording, focusing on = the technical and legal facets. Intended Audience: This study targets practicing and academic litigation and litigation support team members, including lawyers, economists, accountants, audit= ors, doctors, engineers, corporate executives, expert witnesses, plaintiffs = and defendants, as well as students and others interested in the topic. This workshop will review the financial opportunities for expert witnesses, = fee structures, relationship with the legal profession, and opportunities f= or students that want to specialize in the area of forensic accounting and litigation support, and/or combine the study of accounting and law, and computer information systems. Presenters: Sara Rushinek, University of Miami Avi Rushinek, University of Miami Sponsor: Teaching and Curriculum Section * Denotes special requirements or prerequisite Back to CPE Listings Back to Annual Meeting Home Page <= /p> <= o:p> <= strong>Note:= CPE fields of study are in parentheses Rushinek, A. and Rushinek, S. “Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & T= each Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support,” American Accounting Association Annual Meeting 2007, Saturday, August 4, 2007, Chicago, Illinoi= s, sponsored by Teaching and Curriculum Section. http://aaahq.org/am2007/cpe/cpe08.htm CPE Session 8: Saturday, August 4, 1:00 PM – 4:00 PM= Assessment CPE Video Podcasts and Blogs "Hands-on:&qu= ot; Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony &a= mp; Microsoft® Office Accounting Computer Litigation Support (Specialized Knowledge & Applications) Description/Objectives: Repurpose DVD-recorded lectures using new Blu-ray® and HD-DVD® in the classroom and CPE, for the CPA, CMA, CIA, CISA/M WWW certificate Preps. Demonstrate download of the Microsoft Office Accounting,= and upload the lecture on iTunesU®, Google Video®, and YouTube®. Re= view test automation compliance with NASBA, AACSB, COSO, Sarbanes-Oxley, HIPAA, = and Basel II. Publish for students, alumnus, professionals, and the general pub= lic. Web cast using multi-media streaming tools to be more effective and efficie= nt, and provide a more enjoyable experience. Use the new Microsoft® Office = 2007 Accounting, and Vista® involving Effective Learning Strategies, Emerging and Innovative Research, and Internet Research Forums. Instructors encourage existing and prospective faculty and students to be involved and contribute= in Assessing Submissions, Publications, Faculty Development, Teaching, Researc= h, Practice, Service, Awards, and Placement streamline automation and SEO (Sea= rch Engine Optimization). Presenters: Avi Rushinek, University of Miami Sara Rushinek, University of Miami Sponsor: Teaching and Curriculum Section. Rushinek, A. and Rushinek, S. “How to Use New Media and Technology to Repurpose Classroom Instruction for the Accounting and Busine= ss Educator” 2008 International Fraud and Forensic Accounting Education Conference, May 9, 2008, Las Vegas, Nevada= http://www.docstoc.com/docs/5525868/2008-Inte= rnational-Fraud-and-Forensic-Accounting-Education-Conference Rushinek, A. and Rushinek, S. "Mock Trial & Forensic Fa= culty WWW Marketplace, Jury, Judge & Attorneys "Courtroom & Classroom Instructional Technology Research & Service," American Accounting Association Annual Meeting 2008, Anaheim, California, CPE Session 2: Saturday, August 2, 8:00 AM-11:00 AM, sponsored by Teaching = and Curriculum Section. http://aaahq.org/AM2008/cpe/cpe02.htm<= /a> = CPE Session 2: Saturday, August 2, 8:00 AM – 11:00 AM = Mock Trial & Forensic Faculty WWW Marketplace, Jury, Judge & Attorneys — Courtroom & Classr= oom Instructional Technology Research & Service = (Accounting - Basic) <= strong>Description/Objective= s: The workshop deals with: E-Discovery Testimony, Accounting Corporate Information Unauthorized Disclosure, Identity Theft, Money Laundering, Fraud investigations, Office Accounting Cops & Robber Simulations, Phony Sales & Fraud detections, Spreadsheet Database Research Tracking, Digital Med= ia Evidence CDs & DVDs, CYBER CRIME, Security ethical hacking anti-Virus controls, Cell Phones & PDAs Evidence, Trade Secrets Alleged Theft, Electronic Evidence Management. Threats Software & Hardware tool Integration Scenarios Deposition Preps, Jury Deliberation Litigation Arbitration & Mediation Industry Specific Academia, Hedge Fund, Governm= ent & Medical Shenanigans, Plagiarism, Theft & Misrepresentation Contro= ls Red-flags White-color Schemes, Scams Cons Crooks Drug & Human Trafficking Certification Qualification Monitor Confessions Boiler-Plate Rooms, Schemes, Counsels, Judges, & Lawyers Gone Wild (900)Sue-U2.com Foreign Offshore Banking, Tax Avoidance Evasion, & Wealth Management PI Family Divorce Real-estate Stock-Market Litigations Hired Guns Cross Examination Structured Settlements. <= strong>Advance Preparation: = Email and or call presenters (arush@miam= i.edu, arushine@aol.com, (305) 666-7890) a= bout cases, articles, issues, or any relevant suggestions and/or materials, and/= or ideas for discussion and analysis during the workshop to presenters. Dr. Avi Rushinek, University of Miami, Accounting Department, Jenkins 421, C. Gable= s, Fl, 33124 USA (Run)Air-Webs/Phone (Cell) U. Miami, 5250 University Drive, C.I.S. Dept. Jenkins 421, C. Gables, Fl, 33124 (786)247-9327/7466 (Cell) Ac= c. Dept. 314 Jenkins, UM, Coral Gables, Fl, 33124, USA or 1205 Mariposa Ave. #= 208, Coral Gables, Fl, 33146. http://www.onafree.com/defa= ult.aspx http://drjd.net/default.aspx http://dvdu.org/default.aspx http://moax.org/default.aspx http://www.webjobnet.com <= strong>Presenters: Avi Rushinek, University of Miami Sara Rushinek, University of Miami <= strong>Note:= CPE fields of study are in parentheses Rushinek, A. and Rushinek, S. "Role Play Forensic Accountin= g, Auditing & Tax Expert Witness: Providing Testimony and Computer Litigat= ion Support Teaching, Services, Research and Development," American Accoun= ting Association Annual Meeting 2008, Anaheim, California, and CPE Session= 11: Saturday, August 2, 1:00 PM-4:00 PM, sponsored by Teaching and Curriculum Section. http://aaahq.org/AM2008/cpe/cpe11.htm CPE Session 11: Saturday, August 2, 1:00 PM &= #8211; 4:00 PM Role Play Forensic Accounting, Auditing &= Tax Expert Witness: Providing Testimony and Computer Litigation S= upport Teaching, Services, Research and Development<= o:p> (Accounting - Basic) Description/Objectives: The workshop deals with: Rogue Wireless Access Points, Internal Threat Management, Vulnerability Exploits Web, Intrusion Detection, Eaves Dropping AV, Surveillance Recording Audit, On Demand Live Consulting, Internet Access Data points, Consumer Electronics Gone Wild, IT (Information Technology) Governance, Log Forensics Stealth Regulatory Compliance Audit Trails, Mobile Device Digital Consumer Electronics Transcription Stenography, voice to text automation, Cell Phone Video Cam Justice & Court TV Shows Camera Imaging Distribution, Lawsuit Incident Response, WWW Network URL TCP/IP HTTP, Stealth Data Intelligence Office Accounting Information Systems Demos "But-for/If" Analytics Metri= cs Trap Door Trojan Horses, Spying & Due Diligence Enterprise Risk & Damage Assessment Market Rates & Charges Malpractice & Business Valuation "Lost Profits" Board of Directors Litigation Service Confessions Acting Telling a Story Sound-byte Tips & Tricks Interrogate Terror Home-land Security Bribery Black-mail Corruption & Where is the Money Laundering. Advance Preparation: Email and or call presenters (arush@miami.edu, arushine@aol.com, (305) 666-7890) about cases, articles, issues, or any relevant suggestions and/or materials, and/or ideas for discussion and anal= ysis during the workshop to presenters. Dr. Avi Rushinek, University of Miami, Accounting Department, Jenkins 421, C. Gables, Fl, 33124 USA (Run)Air-Webs/Phone (Cell) U. Miami, 5250 University Drive, C.I.S. Dept. Jenkins 421, C. Gables, Fl, 33124 (786)247-9327/7466 (Cell) Acc. Dept. 314 Jenkins, UM, C. Gables, Fl, 33124, USA. http://www.onafree.com/default.aspx http://drjd.net/default.aspx http://dvdu.org/default.aspx http://moax.org/default.aspx http://www.webjobnet.com Presenters: Avi Rushinek, University of Miami<= /span> Sara Rushinek, University of Miami= ________________________________________= Note: CPE fields of study are in parentheses<= o:p> http://www.onafre= e.com/PortfolioManager/8-27-10_mhtm_COST%20FUNCTION%20MODEL%20ASSUMPTIONS%2= 0NARRATIVE%20AND%20VALIDITY.mht Appendix A: Microsoft Excel Object RTF Rich Text Format ¹Beginning= with the 2007Q4 data release, updated RegARIMA models for seasonal adjustment = were implemented to improve the identification of seasonal patterns in unadjus= ted net sales and net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing. ²Revised.<= o:p> = Table 2. U.S. Manufacturing Corporations - Seasonally Adjusted Sales and Net Income After-Tax^{1, 2} mmw1-1-18-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NAR= RATIVE AND VALIDITY8-27-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVES AND V= ALIDITY Industry group and = year Sales³ After-tax profits3 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 (million dollars) (million dollars) Year Sales Year Differenced Sales 1999 …………………………= 230;………………………….= 1,002,478 1,999 1,025,712 1,046,924 1,073,856 59,676 <= /span> 62,171 <= /span> 64,002 <= /span> 72,297 <= /span> 2000 …………………………= 230;………………………….= 1,119,592 2,000 117,114 = 1,127,766 1,144,047 1,122,671 77,236 <= /span> 69,508 <= /span> 71,037 <= /span> 52,554 <= /span> 2001 …………………………= 230;………………………….= 1,114,697 2,001 (4,895)<= /span> 1,093,646 1,058,835 1,029,042 8 14,652 <= /span> 8,186 13,049 <= /span> 2002 …………………………= 230;………………………….= 1,024,163 2,002 (90,534)= 1,048,916 1,064,572 1,077,616 25,490 <= /span> 36,498 <= /span> 38,569 <= /span> 39,361 <= /span> 2003 …………………………= 230;………………………….= 1,101,553 2,003 77,390 <= /span> 1,073,776 1,103,919 1,114,247 59,163 <= /span> 48,583 <= /span> 50,607 <= /span> 78,397 <= /span> 2004 …………………………= 230;………………………….= 1,182,454 2,004 80,901 <= /span> 1,222,793 1,242,962 1,283,696 76,412 <= /span> 86,025 <= /span> 87,217 <= /span> 98,310 <= /span> 2005. …………………………= 230;………………………….= 1,300,032 2,005 117,578 = 1,323,838 1,372,740 1,411,489 90,911 <= /span> 98,812 <= /span> 104,848 = 106,385 = 2006 …………………………= 230;………………………….= 1,445,517 2,006 145,485 = 1,456,392 1,451,814 1,426,945 121,210 = 115,020 = 121,927 = 112,043 = 2007 …………………………= 230;………………………….= 1,456,559 2,007 11,042 <= /span> 1,498,135 1,519,787 1,582,189 118,774 = 129,390 = 74,864 <= /span> 119,481 = 2008 …………………………= 230;………………………….= 1,624,752 2,008 168,193 = 1,693,860 1,658,065 1,394,447 119,147 = 102,478 = 118,508 = (74,150)= 2009 …………………………= 230;………………………….= 1,242,798 2,009 (381,954) 1,234,920 1,291,760 1,348,258 35,953 <= /span> 53,049 <= /span> 90,394 <= /span> 103,277 = 2010 …………………………= 230;………………………….= 1,406,121 2,010 163,323 = 107,775 = =3DB37-B36 Polynomial and Linear Univariate Regression O= LS Undifferenced (raw) Data Polynomial and Linear Univariate Regression O= LS Differenced (Quarterly Gains or (Losses)) Data Formatted RTF - Rich Text Format Beginning with the 2007Q4 data release, updated Reg= ARIMA models for seasonal adjustment were implemented to improve the identification of seasonal patterns = in unadjusted net sales and net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing. Linear to Polynomial Comparison= Undifferenced Raw Quarterly Time Series Data Raw Data Raw Data R² Function Raw Data Polynomial 90.12% y =3D 40.516x6 - 1338.7x5 + 15734x4 - 77264x3 + 135= 313x2 + 32234x + 903575 Raw Data Linear 63.96% y =3D 43464x + 969209 Raw Data R**2Drop 26.16% Differenced Quarterly Time Series Data=3DSales Gain= 2002=3DSales2002-Sales2001 +Gain-Loss Differenced R² Function Differenced Polynomial 64.58% y= =3D 169.86x⁶ - 5772.9x⁵ + 76527x⁴ - 503319x<= sup>3 + 2E+06x² - 3E+06x + 2E+06 Differenced Linear 1.15% y= =3D -5173.8x + 67738 Differenced R**2Drop 63.43% Raw to Differenced Comparison R² Function Raw Data Polynomial 90.12% y =3D 40.516x6 - 1338.7x5 + 15734x4 - 77264x3 + 135= 313x2 + 32234x + 903575 Differenced Polynomial 64.58% y= =3D 169.86x⁶ - 5772.9x⁵ + 76527x⁴ - 503319x<= sup>3 + 2E+06x² - 3E+06x + 2E+06 R**2Drop 25.54% R² Slope Function Raw Data Linear 63.96% $ 969,208 <= /span> y =3D 43464x + 969209 Differenced Linear 1.15% $ 67,737 y= =3D -5173.8x + 67738 R**2Drop 62.81% $ 901,471 <= /span> Appendix B: htt= p://codebase.mql4.com/4332 MetaTrader Linear Regression Channel Linear Regression Channel consists of two parallel lines, equidistant up and down from the line of linear regression trend. The dista= nce between frame of the channel and regression line equals to the value of max= imum close price deviation from the regression line. &nb= sp; http://codebase.mql4.com/sou= rce/10880 &nb= sp; //---- #propert= y indicator_chart_window #propert= y indicator_buffers 3#propert= y indicator_color1 LimeGreen #propert= y indicator_color2 Gold #propert= y indicator_color3 Gold extern int degree =3D 3;= extern double kstd =3D 2.0; extern int bars =3D 250; extern int shift =3D 0;= //-----<= /span> double fx[],sqh[],sql[]; double ai[10,10],b[= 10],= x[10],sx[20]; double sum; int ip,p,= n,= f; double qq,mm,tt; int ii,jj,kk,ll,nn; double sq; int i0 =3D 0;= /* void cle= ar() { int total =3D ObjectsTotal(); for (int i=3Dtotal-1; i >=3D 0;= i--) { string name =3D Object= Name(i); if (StringFind(name, p= refix) =3D=3D 0) ObjectDelete(name); } } */ //+-----= -------------------------------------------------------------+ //| Cust= om indicator initialization function = &nb= sp; | //+-----= -------------------------------------------------------------+ int init() {= Se= tIndexBuffer(0,= fx); // Буферы массивов= индикатор= а Se= tIndexBuffer(1,= sqh); Se= tIndexBuffer(2,= sql); Se= tIndexStyle(0,= DRAW_LINE); Se= tIndexStyle(1,= DRAW_LINE); Se= tIndexStyle(2,= DRAW_LINE); Se= tIndexEmptyValue(0,= 0.0); Se= tIndexEmptyValue(1,= 0.0); Se= tIndexEmptyValue(2,= 0.0); Se= tIndexShift(0,= shift); Se= tIndexShift(1,= shift); Se= tIndexShift(2,= shift); return(= 0); }= //+-----= -------------------------------------------------------------+ //| Cust= om indicator deinitialization function = &nb= sp; | //+-----= -------------------------------------------------------------+ int deinit() {= //clear(= ); return(<= /span>0); }= //+-----= -------------------------------------------------------------+ //| Cust= om indicator iteration function = &nb= sp; | //+-----= -------------------------------------------------------------+ int start() {= if ( < bars) return; //---- <= /span> int mi; // п = 77;ременная использую= щаяся только в start ip =3D bars; p=3Dip; // т = 80;па присваива= ние sx[1]= =3Dp+= 1; // п = 88;имечание - [] - означает= массив nn =3D degree+1;= Se= tIndexDrawBegin(0,= Bars-= p-1)= ; Se= tIndexDrawBegin(1,= Bars-= p-1)= ; Se= tIndexDrawBegin(2,= Bars-= p-1)= ; //------= ----------------sx---------------------------------------------------------= ---------- for(mi=3D= 1;mi<=3Dnn*2-= 2;= mi++) // математич= еское выражение= - для в = 89;ех mi от 1 д = 86; nn*2-2 {= sum=3D= 0; for(n=3D= i0;n&= lt;=3Di0+= p;n+= +) { sum+=3DMathPow(n,= mi); } sx[= mi+1]= =3Dsum; } //------= ----------------syx----------- for(mi=3D= 1;mi<=3Dnn;mi++) {= sum=3D= 0.00000; for(n=3D= i0;n&= lt;=3Di0+= p;n+= +) { if(mi=3D=3D1)= sum+=3DClose[n]= ; else= sum+=3DClose[n]= *MathPow(= n,mi-1)= ; } b[= mi]=3Dsum; } //=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3DMatrix=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D for(jj=3D= 1;jj<=3Dnn;jj++) {= for(ii=3D1;= ii<=3Dnn; = ii++) { kk=3D= ii+jj-1;= ai[= ii,jj]=3Dsx[kk]; } }= //=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3DGauss=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D for(kk=3D= 1; kk<=3Dnn-1;= kk++) {= ll=3D= 0; mm=3D= 0; for(ii=3D= kk; ii<=3Dnn; ii++) { if(MathAbs(ai[ii,kk])>mm) { = mm=3DMathAbs(= ai[ii,kk]); = ll=3Dii; } } if(ll=3D=3D0)= return(0); if (= ll!=3Dkk) { for(jj=3D1;= jj<=3Dnn; = jj++) { = tt=3Dai[= kk,jj]; = ai[kk,= jj]=3Dai[ll,jj]; = ai[ll,= jj]=3Dtt; } tt=3D= b[kk]; b[= kk]=3Db[= ll]; b[= ll]=3Dtt; } for(ii=3Dkk+1;= ii<=3Dnn;= ii++) { qq=3D= ai[ii,kk]/ai[kk,kk]; for(= jj=3D= 1;jj<=3Dnn;jj++) { = if(jj=3D=3Dkk) ai[= ii,jj]=3D0;= = else ai[ii,jj]=3Dai[ii,jj]-qq*ai[kk,jj]; } b[= ii]=3Db[= ii]-= qq*b[= kk]; } }= x[nn]=3Db[= nn]/= ai[nn,nn]; for(ii=3Dnn-1;= ii>=3D1;= ii--) {= tt=3D= 0; for(jj=3D= 1;jj<=3Dnn-ii;jj++) { tt=3D= tt+ai[ii,ii+jj]*x[= ii+= jj]; x[= ii]=3D(1/= ai[= ii,ii])*(b[= ii]-= tt); } }= //=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D for(n=3D= i0;n&= lt;=3Di0+= p;n+= +) {= sum=3D= 0; for(kk=3D= 1;kk<=3Ddegree;kk++) { sum+=3D= x[kk+1]= *MathPow(= n,kk); } fx[= n]=3Dx[= 1]+= sum; } //------= -----------------------------Std-------------------------------------------= ---------------------------------------- sq=3D0.0; for(n=3Di0;n&= lt;=3Di0+= p;n+= +) {= sq+=3DMathPow(Close[n]= -fx[= n],2)= ; }= sq=3DMathSqrt(sq/(p+= 1))*kstd; for(n=3Di0;n&= lt;=3Di0+= p;n+= +) {= sqh[= n]=3Dfx[n]= +sq; sql[= n]=3Dfx[n]= -sq; }= return(<= /span>0); }= //+-------------------------------= -----------------------------------+ MetaTrader Expert Advisor e-Regr ba= sed on Regression Channel MetaTrader Indicator If price lower than under line - Buy If price bigger than upper line - Sell http://blog.iticsoftware.com/2008/09/08/linear-and-multiple-regression-c= hannels-free-regression-metatrader-expert-and-metatrader-indicator.aspx= Thi= s way to find the trend via Linear or Multigrade Function is a pretty good idea.<= br> But what do you think about optimation of memory/processor duties? This is a too big work for normal PC. Parameter optimation will need several months!<= br> The way to calculate r-squared for each number of bars to find the highest = r-squared value will bring us to the optimal number of bars. This is an important iss= ue I mean. Is anyone interested to add these subjects to the code? 1. &nbs= p; Metatrader Expert Advisors | Forex Robo= ts | Metatrader Indicators ... Sep 8, 2008 ... a=3D the intercept b=3D the slope u=3D the regression residual. ... Degree - power of Polynomi= al: 1- linear; 2- parabolic; 3- Third-p= ower. Metatrader Expert Advisor based on Regression Channel Metatrad= er Indicator ... The way to calculate r-squared for e= ach number of bars to find the highest r-squared ... blog.iticsoft= ware.com/.../linear-and-multiple-regression-channels-f= ree-regression-metatrader-expert-and-metatrader-indica= tor.aspx - TakeProfit by average line. Stop Loss =3D 0 but you can set Stop Loss Trend Protection:<= /span> If D1 candle (for previously day) bigger than 150 pips – trade prohibition and close all opened position. Third-power Polynomi= al Regression Channel http://www.forex-tsd.com/expert-ad= visors-metatrader-4/22411-regression-metatrader-expert-advisor.htmlhttp://www.forex-tsd.com/expert-advisor= s-metatrader-4/22411-regression-metatrader-expert-advisor.html Metatrader R-Square Indicator Regr= ession http://www.google.com/#hl=3Den&q=3DMetatrader+R-Square+Indicator+Re= gression&aq=3Df&aqi=3Dm1&aql=3D&oq=3DMetatrader+R-Square+In= dicator+Regression&gs_rfai=3D&pbx=3D1&fp=3D9471436a38449abf= Appendix c: Option #1: Regress= ion .809 switched to .618 56, .618 Close NO center line. Lime green on= my chart. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Moving Averages: MA(7,Open) Exponential Color RED MA(4,Close) Exponential Offset-1 Color BLUE =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= Check List: 1. When the Moving Averages cross and they are at the TOP or BOTTOM of the parent channe= l then we could enter in the opposite direction. 2. Never enter when cross is in the no trade zone. (GREEN Regression channel.) 3. NOTE: Blue must cross Red before you enter a trade. 4. Stay in until we get another cross or MACD crosses the 0 li= ne in the opposite direction. 5. Parent regression channel MUST be at least 20 pips or more. (Bigger is better.) 20 is a bare minimum. Jamio method Required setup: File Format: Microsoft Word - View as HT= ML The basic indicators are 2 moving averages a Red 8 period Moving Average and a .... Linear Regression Channels represent a method for computing a mean line ... two parameters: the time period= for the least squares fit and the number of ... www.forexmt4.= com/_MT4.../bob_2006_03_22_Hoover_Method.doc - Similar http://= www.google.com/search?hl=3Den&as_q=3DMetatrader++Indicator+Regression&a= mp;as_epq=3DR+Square+&as_oq=3D&as_eq=3D&num=3D100&lr=3D&= ;as_filetype=3Dppt&ft=3Di&as_sitesearch=3D&as_qdr=3Dall&as_= rights=3D&as_occt=3Dany&cr=3D&as_nlo=3D&as_nhi=3D&safe= =3Dimages#num=3D100&hl=3Den&lr=3D&as_qdr=3Dall&q=3DMetatrad= er++Indicator+Regression+R+Square+filetype%3Adoc&aq=3Df&aqi=3D&= aql=3D&oq=3DMetatrader++Indicator+Regression+R+Square+filetype%3Adoc&am= p;gs_rfai=3D&pbx=3D1&fp=3D9471436a38449abfeS<= /p> Appendix C: NinjaTrader: http://www.bigmiketrading.com/free_downloads/ninjatrader-7/= indicators/page-5.html I believe my version to be slightly more CPU efficient, as it = does not require a square root calculation used in the distribution version. Version 1.0. http://www.bigmiketrading.com/free_downloads/nin= jatrader-7/indicators/525-download.html?view See the 10/26/00 Trad= ers Advantage posting for the Excel formula of the forecast oscillator. Colum= n G displays the value of r-squared. The higher the value of r-squared the greater the strength of the trend. Figure 1: Nasdaq (top chart), VHF for a 10-day period (middle chart), and the smoothed r-squared for periods of 10 and 20 days (bottom chart). Graphic provided= by: MetaStock. http://technical.traders.com/tradersonline/display.asp?art=3D348 Smoothed r-squared is the simple moving average of r-squared and when the two are plotted (Figure 1: bottom chart) you can see that, relative to the Nasdaq (Figure 1: top chart), a 10-day smoothed r-squared is more responsive than = the 20-day r-squared, using smoothing periods (moving averages) of 10 and 20 da= ys respectively. Chande proposes critical values to see if the r-squared is statistically significant or not. For an r-squared of 10 this is a critical value of 0.4 and means that with 95% confidence the value of r-squared is significant. For a 20-period r-squared the critical value is 0.2. Remember neither of these give a direction to the trend, just whether one exists or = not. The slope of the linear regression line indicates the direction of the tren= d. A positive slope suggests an uptrend and negative a downtrend. Appendix E: TradeStation = 1. = [PPT] www.mtadata.org/eweb/docs/ppts/Fulks.ppt File Format: Microsoft Powerpoin= t - View as HT= ML Slope of Line =3D Sharpe Ratio. Depends only upon Investment. T-Bi= lls ... (produce the same optimum return); Optimum return increases as Sharpe Ratio= squared; Process: ..... regression line. Return_Stock =3D Alpha + Be= ta * Return_Index + Noise .... Neutralizer removes overall market fluctuations and most correlation ... Appendix F: StatSoft http://www.statsoft.com/textbook/multiple-regression/ http://www.traders.com/documentation/FEEDbk_do= cs/2007/12/TradersTips/TradersTips.html#worden= ht= tp://www.Blocks.com to download the software and get detailed information on the available data packs. In the article by Barbara Star in this issue, "Confirming Price Trend," she uses linear regression, r-sq= uared, linear regression slope, and stochastics as confirming indicators of the current trend. Each of these indicators (studies) is available = in the Blocks library. No custom coding is required. Open the Personal Chartist workspace in Blocks, then click the Add Study bu= tton () and type "linear regression" to filter the list and select the linear regression study. You can edit the parameters of the linear regressi= on line by clicking on its name in the legend and using the QuickEdit menu: Follow the same proce= dure to add linear regression slope, r-squared and stochastics. Use the QuickEdit menus on each of these studies to customize the settings and parameters (Figure 7). FIGURE 7: BLOCKS, R-SQUARED AND LINEAR REGRESSION SLOPE. Here is a daily chart of AAPL. These indicators can = be used for any time frame. Just click the time frame selector at the top of the ch= art to view daily charts, three-day charts, weekly charts, and so on. With the = Mega Minute pack, you can also view intraday charts (minute, hourly, and so on) = with streaming real-time data. Figure 8 shows the same indicators on an hourly c= hart of AAPL, which can be used to measure shorter-term trends. FIGURE 8: BLOCKS, R-SQUARED AND LINEAR REGRESSION SLOPE. Here is an hou= rly chart of AAPL. To download the Blocks analysis software, go to http://www.Blocks.com. Then,= to choose your Blocks Analysis Packs, call 800 776-4940, or order online at the Blocks website. --Bruce Loebrich a= nd Patrick Argo Worden Brothers, Inc. www.blocks.com, www.worden.com Confirming Linear Trend Line in Various Trading Platforms TRADESTATION: CONFIRMING PRICE TREND eSIGNAL: CONFIRMING PRICE TREND<= /a> METASTOCK: CONFIRMING PRICE TREND WEALTH-LAB: CONFIRMING PRICE TREND NEUROSHELL TRADER: CONFIRMING PRICE TRE= ND AMIBROKER: CONFIRMING PRICE TREND STRATASEARCH: CONFIRMING PRICE TREND WORDEN BROTHERS BLOCKS: CONFIRMING PRICE TREND ASPEN GRAPHICS WORKSTATION 4.2: CONFIRM= ING PRICE TREND TRADINGSOLUTIONS: CONFIRMING PRICE TREN= D TRADECISION: CONFIRMING PRICE TREND TRADE NAVIGATOR: CONFIRMING PRICE TREND= ENSIGN SOFTWARE: CONFIRMING PRICE TREND= NINJATRADER: CONFIRMING PRICE TREND VT TRADER: CONFIRMING PRICE TREND SWINGTRADER: CONFIRMING PRICE TREND TD AMERITRADE STRATEGYDESK: CONFIRMING = PRICE TREND = 1. = 2. &nbs= p; Traders' Tips - December 2007 Here, the TradeStation indicators RSquared and Lin Regression Slope are applied to ..... The Wealth-Lab code sam= ple provided here implements one of the ideas ..... regression slope indicators to measure the correlation between price and ..= . www.traders.c= om/documentation/FEEDbk_docs/.../TradersTips.html - Cached 3. &nbs= p; TRADERS' TIPS - November 2009 Sep 12, 2009... D1DXY=3DDays for the Dollar Index linear regression slope calculat= ion ..... Drawing; using WealthLab; using WealthLab.Indicators<= /em>; using Community. ..... or in any case, to filter out signals when correlation is low. .... then Intercept =3D (sumY - Slope * sumX) / SLe= n 'R squared denom2 =3D (Sqr( (SLen ... www.traders.c= om/Documentation/...docs/.../TradersTips.html - Cached - Similar Appendix i: Detecting Trend Direction And Strength = http://old.artun.ee/~momentum/ebook/STOCKS%20&%20COMMODITIES/Stocks%20&= amp;%20Commodities%20-%20various%20Trend%20Trading%20Articles.pdf<= /o:p> FIGURE 1: ADX AN= D MACD WITH AOL TIME WARNER (AOL). The rising ADX in t= he upper panel does not differentiate between up- or downtrending price movements. Plotting the MACD just below the ADX makes the trend direction much easier to spot. FIGURE 2: A CONF= IRMING PATTERN ON BRISTOL-MYERS SQUIBB (BMY). Both the ADX and the MACD signal a rising trend is in progress when they move up together with price.= FIGURE 3: A DIVE= RGING PATTERN ON CISCO SYSTEMS, INC. (CSCO). The indicators highlight a downtrend by diverging and forming a mirror-like ima= ge. FIGURE 4: A CONSOLIDATION PATTERN. The box shows price consolidation that followed a price uptrend in Bank of America (BAC) stock. The ADX declined but the MACD remained above zero to reflect the consolidation. FIGURE 5: ADX WI= TH TOYS “R” US (TOY). By itse= lf, the ADX can be confusing to interpret because its ups and downs do not necessarily follow price. FIGURE 6: MACD W= ITH AT&T (T). Because it is a momentum indicator, = the MACD does not always track price accurately. Stocks & Commodities V. 20:1 (22-25): Detect= ing Trend Direction And Strength by Barbara Star, Ph.D. Copyright (c) Technical Analysis Inc. At point 1, the MACD solid line rose above its trigger l= ine, which reflected the upmove in price. At point 2 = the MACD crossed below its dotted line, following price t= o the downside. However, the MACD rise above its trigger line at poin= t 3 was not joined by rising prices or an upsloping movi= ng average. The MACD = rose because downward momentum pressure had= diminished as prices slowed their downward desce= nt. Stocks & Commodities V. 18:4 (62-68): Pickin= g Out Your Trading Trend by Martin J. Pring • US GOVERNMENT BOND PRICES= FIGURE 2: SECULAR BOND TREN= DS. In 1982, the downtrend in bond prices broke along with inflation, setting off = the greatest stock bull market in history. FIGURE 3: SWISS FRANC. The downtrend from September= to March was a smooth decline. = FIGURE 4: S&P 500 FIVE-MINUTE BARS. Midway through the trading d= ay, the trend was down. = FIGURE 5: S&P 500 ONE-M= INUTE BARS. There were two profitable sh= ort sell signals, based on the trend = of both the five-minute and one-minute bars. Appendix C: Trading Stories:<= /span> Fortune500 Article here http://money.cnn.com/magazines/fortune/fortune_archive/200= 0/12/18/293117/index.htm Biography : Daniel J. Zanger, Having turned $10,775 into $18 million between June 1998 and December 1999 http://en.wikipedia.org/wiki/Dan_Zanger = = [1] Stoc= ks & Commodities V. 11:9 (382-386): Rating Trend Strength by Tushar S. Cha= nde [2] Rushinek, A., Rushinek, S., Chang, L. and M= ost, K. "The Construction of an International Investors' Perception M= odel for Corporate Published Forecasted Financial Reports for the United States, Great Britain, and New Zealand," MANAGERIAL AND DECISION ECONOMICS (MD= E), THE INTERNATIONAL JOURNAL OF RESEARCH AND PROGRESS IN MANAGEMENT ECONOMICS, Vol. 4, No. 4, 258‑265, December 1983. [3] Avi Rushinek, Sara F. Rushinek, Lucia S. Ch= ang, Kenneth S. Most "The construction of an international investors' perception model for corporate published forecasted financial reports for t= he USA, the UK and New Zealand," Managerial and Decision Economics, Volum= e 4 Issue 4, Pages 258 - 265, Published Online: 9 Nov 2006, Copyright &co= py; 2009 John Wiley & Sons, Ltd. http://www3.interscience.wiley.com/journal/= 113456935/abstract?CRETRY=3D1&SRETRY=3D0 [4] Rushinek, A. and Rushinek, S. "U= sing Financial Ratios to Predict Insolvency," JOURNAL OF BUSINESS RESEARCH, Vol. 15, Issue 1, 93-100 1987. http://ideas.repec.org/s/eee/jbrese10.html http://www.eurojournals.com/jmib_11_01.pdf Citation [5] Rushinek, A. and Rushinek, S. "Evaluating Asset Safeguarding and Data Integrity in the EDP Audit&quo= t;, MANAGERIAL AUDITING JOURNAL, Vol. 4, No. 2, 3‑6, 1989. http://www.emeraldinsight.com/Insight/viewC= ontentItem.do;jsessionid=3D98F6451F5BD9FAD182CCEE6888D98CA7?contentType=3DA= rticle&contentId=3D868055 [6] Rushinek, A. and Rushinek, S. "Using E= xperts for Detecting & Litigating Computer Crime", MANAGERIAL AUDITING JOURNAL, Vol. 8, No. 7, November, 1993, 19-21. http://www.emeraldinsight.com/Insight/viewC= ontentItem.do?contentType=3DArticle&hdAction=3Dlnkpdf&contentId=3D8= 68184 Article URL: http://www.emeraldinsight.com/10.1108/02686909310046862 [7] Rushinek, A. and Rushinek, S. "Forensic Audits of Stock Market Forecas= ts For Texaco & The International Oil and Gas Industry & Financial Mod= els for Online Broker Litigation", OIL, GAS, & ENERGY QUARTERLY, Vol. = 51, No. 4, June, 2003, 659-678. [8] Rushinek, A. and Rushinek, S. "Tax Fra= ud Forecasting For the Petroleum Refining Industry: Financial Ratios & Reversed Accounting Theory Applied to Shell Oil", OIL AND GAS TAX QUARTERLY, March, 1997. [9] Rushinek, A. and Rushinek, S. “Managerial auditors mixed cost forecasting assumption departure error estimates for litigation and professional liability risk reduction: the electronics industry”, MANAGERIAL AUDITING JOURNAL, Vol.12 No. 6, 199= 7. http://www.emeraldinsight.com/Insight/viewC= ontentItem.do?contentType=3DArticle&hdAction=3Dlnkpdf&contentId=3D8= 68360 http://www.emeraldinsight.com/10.1108/02686= 909710180643 [10] Rushinek, A. and Rushinek, S. ”Rating= and Ranking Best Practices of the British Petroleum Oil Company and the Oil &am= p; Gas Industry: Sales to Cash Forecasting Univariate Regression Trend Analysis and Computer Modeling”, OIL AND ENERGY QUARTERLY, December, 1997. [11] Rushinek, A. and Rushinek, S. ”Litigation in Pennzoil and the Petrole= um Refining Industry: A Financial Ratio Automated Peer Review Analysis of Vari= ance Internet Software Development” OIL, GAS & ENERGY QUARTERLY, Vol.47, No.1, September, 1998, 17-32. [12] Rushinek, A. and Rushinek, S. ”Correlating Investors’ Preferenc= es to Stock Characteristics: An Algorithm for Internet Software Decision Suppo= rt System Development in the Crude Oil & Refining Industry” OI= L, GAS & ENERGY QUARTERLY, Vol. 48, No.1, September, 1999, 87-101. [13] Rushinek, A. and Rushinek, S. ”Oil & Gas Market, Sector & Com= pany Relative Strength Congruity Trading Theory (MSCRSSCTT): An Internet Automat= ion Approach Applied to Chevron Corporation’s Stocks” OIL, GAS & ENERGY QUARTERLY, Vol. 48, No.4, June, 2000, 671-682. [14] Rushinek, A. and Rushinek, S. Internet Fraud Auditing: A Simulated Health Care Industry Case Study, JOURNAL OF FORENSIC ACCOUNTING, Vol. 1, No. 1, 125-234, 2000. http://www.rtedwards.com/journals/JFA/content= s.html http://www.rtedw= ards.com/journals/JFA/I-1/125.pdf [15] Rushinek, A. and Rushinek, S. The Role of the Forensic Accountant in Calculating the Damages Using the ‘But-if Anal= ysis in a Case of Internet Day Trader & Online Broker Misconduct Litigation, JOURNAL OF FORENSIC ACCOUNTING, Vol. 1, No. 2, 241-250, 2001. http://www.rtedwards.com/journals/JFA/content= s.html [16] Rushinek, A. and Rushinek, S. “Sarbanes Oxley Microsoft Accelerator Forensic Accounting, Expert Witness Testimony, & Computer Litigation Support Workshop”, American Accounting Association Annual Meeting 2005= , Saturday, August 4,= 2005, San Francisco, California, sponsored by Tea= ching and Curriculum Section. http://aaahq.org/AM2005/cpe/cpe02.htm [17] Rushinek, A. and Rushinek, S. "Assessmen= t CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support," American Accounting Associati= on Annual Meeting 2007, Saturday, August 4, 2007, Chicago, Illinois, sponsored= by Teaching and Curriculum Section. http://aaahq.org/am2007/cpe/cpe08.htm [18] http://itisjournal.com/default= .aspx http://isitjournal.com/default= .aspx [19] Rushinek, A. and Rushinek, S. 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Linear, Polynomial &am= p; Auto Regressive Integrated Moving Average (ARIMA) Regression Models Assu= mptions, Narratives & ValidityAbstractTrading systems strategies freque= ntly deal with forecasting the profitability of the trade using regression = analysis. Yet many of the elementary accounting textbooks authors confuse = very basic issues related to regression analysis. Forensic accounting test= s such confusions in the courts and tries to clarify the as expert witnesse= s try to estimate the damages using a "but-if" regression analysis. This p= aper takes some of these lessons and tries to evaluate them to see whether = they apply to automated international portfolio trading systems strategies.= In an automated portfolio trading system strategies of mixed securities su= ch as currencies, futures, equities and derivatives a common denominator of= predictor variable and predicted variables are important as the systems co= mbine pips, yens and dollars, in a variety of time frames of daily, hourly,= and tick by tick quotes. Trading platforms and software applications freq= uently use different methods to describe time, which is frequently the pred= ictor variable of many profits forecasting method. A case in point is Micr= osoft ExcelÂ®, which is using the Julian date, as a measure of time. Since= some textbooks confuse such issues, the challenge of correctly auditing &a= mp; validating an automated trading strategy on multiple computational and = trading platforms becomes more difficult to handle. Therefore, it may be b= eneficial to identify such confusions as early as possible in the design of= such trading systems to save time and improve performance as soon as possi= ble. The authors will test such systems on commercial trading platforms su= ch as Trade StationÂ®, Ninja TraderÂ®, Stock FinderÂ®, eSignalÂ®, using mix= ed international securities, and brokers, such as GainÂ®, Trade StationÂ®, = in a variety of markets.*--------------------------------------------------= -------------------------------------------------------------------* The au= thors have omitted some details to save space and shorten the discussion, s= uch additional details will be provided by the authors upon written request= s. ------=_NextPart_01CB6177.110BCE60 Content-Location: file:///C:/4367CA65/10-1-10_mht_Predictive-Modeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_Slope_files/props0068.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml ------=_NextPart_01CB6177.110BCE60 Content-Location: file:///C:/4367CA65/10-1-10_mht_Predictive-Modeling_Confirming_Price_Trend-ARIMA_OLS_Poly_Regression_Slope_files/item0069.xml Content-Transfer-Encoding: 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¹Beginning= with the 2007Q4 data release, updated RegARIMA models for seasonal adjustment = were implemented to improve the identification of seasonal patterns in unadjus= ted net sales and net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing.
²Revised.<= o:p>
=

Table 2. U.S. Manufacturing Corporations - Seasonally Adjusted Sales and Net Income After-Tax^{1, 2}
mmw1-1-18-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NAR= RATIVE AND VALIDITY8-27-10_mhtm_COST FUNCTION MODEL ASSUMPTIONS NARRATIVES AND V= ALIDITY
Industry group and = year	Sales³						After-tax profits3
	Q1			Q2	Q3	Q4	Q1	Q2	Q3	Q4
	(million dollars)						(million dollars)
Year	Sales	Year	Differenced Sales
1999 …………………………= 230;………………………….=	1,002,478	1,999		1,025,712	1,046,924	1,073,856	59,676 <= /span>	62,171 <= /span>	64,002 <= /span>	72,297 <= /span>
2000 …………………………= 230;………………………….=	1,119,592	2,000	117,114 =	1,127,766	1,144,047	1,122,671	77,236 <= /span>	69,508 <= /span>	71,037 <= /span>	52,554 <= /span>
2001 …………………………= 230;………………………….=	1,114,697	2,001	(4,895)<= /span>	1,093,646	1,058,835	1,029,042	8	14,652 <= /span>	8,186	13,049 <= /span>
2002 …………………………= 230;………………………….=	1,024,163	2,002	(90,534)=	1,048,916	1,064,572	1,077,616	25,490 <= /span>	36,498 <= /span>	38,569 <= /span>	39,361 <= /span>
2003 …………………………= 230;………………………….=	1,101,553	2,003	77,390 <= /span>	1,073,776	1,103,919	1,114,247	59,163 <= /span>	48,583 <= /span>	50,607 <= /span>	78,397 <= /span>
2004 …………………………= 230;………………………….=	1,182,454	2,004	80,901 <= /span>	1,222,793	1,242,962	1,283,696	76,412 <= /span>	86,025 <= /span>	87,217 <= /span>	98,310 <= /span>
2005. …………………………= 230;………………………….=	1,300,032	2,005	117,578 =	1,323,838	1,372,740	1,411,489	90,911 <= /span>	98,812 <= /span>	104,848 =	106,385 =
2006 …………………………= 230;………………………….=	1,445,517	2,006	145,485 =	1,456,392	1,451,814	1,426,945	121,210 =	115,020 =	121,927 =	112,043 =
2007 …………………………= 230;………………………….=	1,456,559	2,007	11,042 <= /span>	1,498,135	1,519,787	1,582,189	118,774 =	129,390 =	74,864 <= /span>	119,481 =
2008 …………………………= 230;………………………….=	1,624,752	2,008	168,193 =	1,693,860	1,658,065	1,394,447	119,147 =	102,478 =	118,508 =	(74,150)=
2009 …………………………= 230;………………………….=	1,242,798	2,009	(381,954)	1,234,920	1,291,760	1,348,258	35,953 <= /span>	53,049 <= /span>	90,394 <= /span>	103,277 =
2010 …………………………= 230;………………………….=	1,406,121	2,010	163,323 =				107,775 =
			=3DB37-B36


Beginning with the 2007Q4 data release, updated Reg= ARIMA models for seasonal adjustment were implemented
to improve the identification of seasonal patterns = in unadjusted net sales and
net income after taxes for all manufacturing, all nondurable manufacturing, and all durable manufacturing.

*Linear to Polynomial Comparison=*
Undifferenced Raw Quarterly Time Series Data
Raw Data	Raw Data	R²	Function
Raw Data	Polynomial	90.12%	y =3D 40.516x6 - 1338.7x5 + 15734x4 - 77264x3 + 135= 313x2 + 32234x + 903575
Raw Data	Linear	63.96%	y =3D 43464x + 969209
Raw Data	R**2Drop	26.16%

Differenced Quarterly Time Series Data=3DSales Gain= 2002=3DSales2002-Sales2001
+Gain-Loss	Differenced	R²	Function
Differenced	Polynomial	64.58%	y= =3D 169.86x⁶ - 5772.9x⁵ + 76527x⁴ - 503319x<= sup>3 + 2E+06x² - 3E+06x + 2E+06
Differenced	Linear	1.15%	y= =3D -5173.8x + 67738
Differenced	R**2Drop	63.43%

*Raw to Differenced Comparison*
		R²	Function
Raw Data	Polynomial	90.12%	y =3D 40.516x6 - 1338.7x5 + 15734x4 - 77264x3 + 135= 313x2 + 32234x + 903575
Differenced	Polynomial	64.58%	y= =3D 169.86x⁶ - 5772.9x⁵ + 76527x⁴ - 503319x<= sup>3 + 2E+06x² - 3E+06x + 2E+06
	R**2Drop	25.54%
		R²	Slope	Function
Raw Data	Linear	63.96%	$ 969,208 <= /span>	y =3D 43464x + 969209
Differenced	Linear	1.15%	$ 67,737	y= =3D -5173.8x + 67738
	R**2Drop	62.81%	$ 901,471 <= /span>