9-10-= 10_Fri_11.50pm_MHT_Profit SMA Trend-Following OLS Regression ATS Total Trades, Win-Loss Rates, Trade = Size & Intraday Drawdowns.mht

9-10-= 10_Fri_3.15pm_Channel Breakout-Pull Back Pattern (CB-PB) (Recovered).docx C:\_Trading<= /span>

9-10-10_Fri_9.15pm_doc_Channel Breakout-Pull Back Pattern (CB-PB) (Recovered).docx

9-10-10_Fri_9.15pm_doc_Channel Breakout-Pull Back Pattern (CB-PB) (Recovered).doc

9-10-10_Fri_11.30pm_doc_Profit SMA Trend-Following OLS Regression ATS Total Trades, Win-Loss Rates, Trade Size & Intraday = Draw downs

Paper Profit of a Simple Moving Average (SMA) Trend-Following OLS Regression Automated Trading System (ATS) Model of Total Trades, Winners, Win/Loss = Rates, Trade Size & Intraday Drawdown

Avi Rushinek*

Department of Accounting, University of Miami, Coral Gables, Fl 33124, USA Email: arush@miami.edu *Corresponding author

Sara Rushinek

Department of Computer Information Systems, University of Miami, Coral Gables, Fl 33124, USA Email: s.rushinek@miami.edu

Abstract:

This study describes methods of test= ing the hypothesis that certain Automated Trading Systems (ATS) strategies, also called programmed trading, robotic trading, and/or algorithmic trading appl= y to an excessive number of applications. Namely, that "one size fits all," of the securities, marke= ts, and trading periods. Typicall= y, traders run such systems to assist them in conducting their trades, but frequently = they do not bother to formally and quantitatively explain the expected profits a= s a function of other more independent predictor variables, which is what this study is doing. This is a stu= dy of the how Total Trades, Winners, Size = & Intraday Drawdown Explain expected trading Profit. Using a Simple Moving Average (SMA) Trend-Following OLS Regression Automated Trading System (ATS) Model, it develops an equation that can assist in prop= erly budgeting the trades.

Overv= iew

Curre= ncies, Commodities, Equities, Fixed Income Securities Markets Automated Trading Systems (ATS) Holding Periods Wins Audit- Testing Biases Towa= rds Promoting Self-Interest

Economic = Decisions with Related Biases towards Overly Optimistic Self-Interest in Behavioral Economics & Finance

The problems of automated trading s= ystems (ATS) strategies are the Biases Towards overly optimistic Self-Interest as Behavi= oral Economics & Finance explain it. “Behavioral economics[1] and its related a= rea of study, behavioral finance, use social, cognitive and emotional factors in understanding the economic decisions of individuals and institutions performing” This study looks at a differe= nce perspective of Automated Trading Systems developed by individuals and institutions, and used by them and others for “economic functions, including consumers, borrowers and investors, and their effects on m= arket prices, returns and the resource allocation. The fields are primarily concerned with the bounds of ratio= nality (selfishness, self-control) of economic agents. Behavioral models typically integrate insigh= ts from psychology with theory. Behavioral analysts are not only concerned with the effects of= market deci= sions but also with public choice, which describes another source of economic decisions with related biases towards promoting self-interest: Heurist= ics: People often make decisions based on approximate rules of thumb, not strict logic. See also cognitive biases and bounded rationality, Framing; The collection of anecdotes and stereot= ypes that make up the mental emotional filters individuals rely on to understand= and respond to events; Market inefficiencies: These include miss-pricing, non-rational decision making, and return anomalies. Daniel, Hirshleifer, and Subrahmanyam (1998)[2] built models based on extrapolation (seeing patterns in random sequences) and overconfidence to explain security market under- and overreactions, though their source continues to be debated. These models assume that errors or bi= ases are positively correlated across agents so that they do not cancel out in aggregate. This would be the case if a large fraction of agents look at the same signal (such as the advice of an analyst) or have a common bias.” Likewise, agents use advice of sel= lers of automated trading systems and strategies, misleading them to trade in an area that may not be best suited for such systems.

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Back-testing ATS audit Ap= ps, iPhone, Google Droid, free OS HTML= & Java

This study develops an OLS Regression model that will automate the explanation of prof= its that an Automated Trading System (ATS) is expected to generate, leading to = the proper budgeting, and money management.&nb= sp; The problem with many ATS systems is that they lack sufficient money management subsystems, leading to cash-flow problems. Proper budgeting using OLS Regress= ion apps may reduce and control such risks.&nb= sp; Automating such money management subsystems, may make it painless en= ough that traders will be willing to use it, making them more aware of the possi= ble outcomes and their risk.

This study demonstrates “off-the-shelf” quantitative method that can easil= y be programmed into Microsoft Office Excel or similar users that is not a programmer. Using a program c= ode generator, or an embedded Macro Programming Language like Visual BASIC for Applications, included in Microsoft Office Excel 10, the user simply records the applications, while the code is generated automatically by the Excel program, or a similar program, producing a back-testing ATS audit validation application (App). Such Apps = can eventually be recompiled into Java script and HTML (Hyper Text Mark Up Language) running on any web server or mobile device such as iPhone, Google Droid, and other PDAs (Personal Digital Assistants) and computers, without Excel or any other proprietary program or Operating System (OS) such as Microsoft Windows. Instead it= will use free open source OS, such as UNIX or LINUX.

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Introduction & Liter= ature Review

Counterparty Surveillance Promotes Transparency and Clarity of the Facts across Financial Transaction= s

Counterparty surveillance promotes transparency and clarity of the facts across financial transaction= s so users of systems can discover the bias or certain optimization, or over-optimization, built into the systems and deal with it effectively, and efficiently. In the case that Automated Trading Systems (ATS) that are optimized for certain securities s= uch as equities, stocks or stock funds, may be much less successful in trading currencies. Thus they may gen= erate a much higher rate of winning trades in equities, compared to currencies. Likewise, they may be optimized or= over optimized for a maximum trading period of 5 days, and may not be as effecti= ve for longer holding periods, such as periods longer than 5 days. However, sellers of such systems m= ay falsely promote them as equally effective for all securities and all trading time frameworks to maximize their sales volume and revenues. Such sellers may falsely promote t= hese systems, as “one size fits all,” due to the seller’s self-interest bias, which again has to be assessed and discounted properly.=

Uncooking the Books and the Decision Making Process From Biased and Self-Interest of Material Misstatements

Recently the pre= ss has been flooded with cases of cooking and uncooking the books from toxic paper sub-prime mortgages CDS and CSOs material misstatements of the financial services industry: crisis challenges and counterparty surveillance of collateralized debt obligations', as reported by Rushinek and Rushinek (2010 a-l) in the International Journal of Economics and Accounting, Volume 1, No. 1, 2010. Such articles stress the importance of automated audit applications (Apps) that can effectively and efficiently counteract the flo= od of false, misleading and inaccurate information that is flooding the air-wa= ys and the internet. This study = is trying to develop a methodology for such apps that will automatically assess the bias of certain claims, and reject the false claims in a simple numeric= al way that a computer program, can easily issue an alert for the risk of following a suggestion that is bias and may lead to less than optimal decisions. An example of such process can be a series of statistical hypothesis tests that reject the hypothesis that a certain automated trading system (ATS) strategy performs equally well for a variety of securities, such as equities, commodities, currencies, for a variety of trading periods such as 5, 10, 15, and 20n day= s, etc. In the absence of such a= udit apps ATS sellers are more likely to mislead users to apply less then optimal ATS strategies, lose their capital, and go out of business, before they had= a chance to remediate the deficiency. This is especially true in economically slow periods, such as the ye= ars 2009-2010, where it is not clear if the recession has expired or the econom= y is about to get into another recession.

The following literature deals with surveillance, testing and forecasting metho= d to correctly assess and discount variety of biases. Back-testing of securities trades = is such area where validity, forensic accounting and audit testing is very important. This study focuses on employing research methodologies that highlight the importance of forensic accounting, auditing, fraud mitigation, and litigation service issues. These instructio= nal methodologies include multi-media capabilities, data management and data engineering (Rushinek and Rushinek, 2000b; Rushinek and Rushinek, 2001). Software was developed that analyses and improves forensic accounting resea= rch skills, research tools and research techniques for all that use the softwar= e.

Rushinek and Rushinek (1997, 1998, 1999, 200= 0a) demonstrate a self-vetting system for ‘Rating and Ranking Best Practi= ces of the British Petroleum Oil Company and the Oil & Gas Industry’.= As the system runs repeatedly, eventually the ‘Sales to Cash Forecasting Univariate Regression Trend Analysis’ emerges as the best practice am= ong a large variety of ‘Computer Modeling’ (Rushinek and Rushinek, 2005c; Rushinek and Rushinek, 2007). Even though, this was done for the Oil= and Energy industry, the lessons apply to any search for best practices. This s= tudy promotes audio visual (AV) surveillance publications, and their download and viewing rates, ranking and rating publication, as a self-vetting system of = best practices of e-learning and educational materials.

Standards for the eval= uation of skills and e-competences

Standards for the evalua= tion of skills and e-competences are different in the USA and EU. Such differenc= es have to be closed for the purpose of consolidated financial statements. To close such difference in different time zones, different continents, differ= ent language, require minimal IT skills and e-competences. Rushinek et al. (198= 3) reported about empirical models of ‘Development and Testing of a Discriminate Model for Measuring Changes in Instructor Evaluations’. = They demonstrated software programs based on a discriminate linear function for automating the gain of instructors’ ratings for applications of compu= ters in mathematics and science teaching (Rushinek et al., 1983). Rushinek and Rushinek (1983) show that such CAI and testing that are fast and responsive satisfying users in ‘An Evaluation of Mini/Micro Computer SystemsR= 17;, using ‘An Empirical Multivariate Analysis’. The results of the evaluations are stored in a database, for comparative time series analysis (Rushinek and Rushinek, 1983; Friedman et al., 2006).

IFRS compliant AV HD recording audit of standards of effective learning strategies

Rushinek and Rushinek (2009a) have been conducting workshops for the American Accounting Associat= ion (AAA) about International Financial Reporting Standards (IFRS). These works= hops raised the issue of compliance with multiple sets of unrelated standards in= one integrated compliance efforts. Course Programmers for E-competences (CPE) f= or accountants are training programmers and workshops for e-competences, making sure that they can keep their Certified Public Accountant (CPA) or similar certificates current. The following is a description of training course programmers and workshops for e-competences. Automating the process of recording, distributing, making the instructor an instant author of the materials, and rewarding them further with royalties of sales of the videos, the transcripts, the tests and assessments automatically generated from the= se transcripts will improve their overall competencies and performance (Rushin= ek and Rushinek, 2004).

The presenters, Avi Rushinek, University of Miami, and Sara Rushinek, University of Miami, describe it as follows: “Effective Learning Strategies (ELS) that repurposes stand-up traditional class= room instruction AV (Audio Video) surveillance recorded to a DVD, for real-time auditing, distance learning, CPE courseware, as well as tutor CDs and DVDs.= As soon as the class is over, the instructor’s PC uploads the AV session= to Google Video, and to a DVD-burner” (Rushinek and Rushinek, 2009b).

Cell phone real-time forensic audit on the World Wide Web (WWW)

To complement the traditional after-the-fact auditing, this case study deals with real-time auditing. This means that the auditor watches the activities in while they occur; hence the term ‘real-time’. The system is using the World Wide Web instead of limiting the access to the intranet; adding a potential= ly profitable online distances learning service (Rushinek and Rushinek, 2005a; Rushinek and Rushinek, 2009b).

Rushinek and Rushinek (2003c, 2008) demonstrate how surveillan= ce video playback, peer evaluation and discussion of the ‘Role Play Fore= nsic Accounting, Auditing and Tax Expert Witness’, can be very effective in ‘Providing Testimony and Computer Litigation Support Teaching, Servic= es, Research and Development’. It shows that such visual aids, as AV surv= eillance materials can dramatically enhance existing instructional technologies, whi= le improving the security for the participants (Rushinek and Rushinek, 2008). =

1 Using wireless c= ell phone and surveillance technology in the forensic accounting

Let us review how forens= ic accounting instructors can use some of the above technologies in the classr= oom. The instructors can demonstrate ‘Wireless Access Points’ by sha= ring the recorded classroom videos wirelessly with students in the class, letting them download the media in real-time into their devices, and recording the in-coming video signal as it is displayed on their media player. This also demonstrates ‘AV (Audio Video), Surveillance Recording’, the students see how the camera that is situated at the back of the classroom c= an pan, tilt, and zoom (PTZ) an image into their laptop showing the display on their screen.” The instructor can let students, with a cell phone and internet connectivity; control the PTZ IP (Internet Protocol) surveillance = camera by using the phone as a remote control. An instructor/lecturer could have anyone in the world be the cameraperson since class is being broadcasted re= al time over the Internet Protocol.

As the surveillance video recording ends, the videos are automatically stored = and uploaded to social video websites for viewing. Such websites include but are not limited to iTunes U^®, Google Video^® and Y= ou Tube^®. The Forensic Accounting instructor uses the videos to analyze the performance of the expert witnesses together with the session participants. The discussion highlights the pros and cons of the court beha= vior of the experts, letting the experts express their views. This activity also instructs the participants about Search Engine Optimization (SEO) of videos on the web (Rushinek and Rushinek, 2003a). They learn how to garnish the top 100 positions, on Google Video^®, using meta-tags, lecture transcriptions, video transcriptions, court-reporter techniques to index and internet searches of court document databases. They learn how an expert wit= ness can promote his brand on Google Video^®, by monopolizing the = top 100 videos on certain search keyword meta-tags, such as Nets-Expert (http://video.google.com/videosearch?hl=3Den&q=3Dnets-expert&num=3D= 100#). The instructor could invite practicing attorneys as guest speakers. The students can role-play as judges, prosecutors, defense attorneys and experience the ‘examination of the experts’ all while being recorded for later discussion and review (Rushinek and Rushinek, 2005b).

Counterparty Surveillance audit AV improves Records what is going on Inside and Outside = the Computer

Counterparty surveillance applies to the surveillance of a sel= ler of ATS strategies. The buyer = can evaluate the veracity of the seller by test the claims that it trades equal= ly well for all securities, markets, and time periods. Product/services in the manner of course/lecture blending, distance instruction and self-study education all = add to additional funds generated for all parties involved. If security surveil= lance equipment is already in place and working, the cost of equipment is already been realized by the institution and only minimum capitalization may be nee= ded to begin revenue generation (Rushinek and Rushinek, 2002).

Currencies, Commodities, Equities, Fixed Income Securi= ties Markets Automated Trading Systems (ATS) Holding Periods Wins Audit

This study develops an OLS regression to explain the p= rofit of an ATS example, and illustrate how traders may produce a budget for the expected profits to reduce the chances of running out of capital or cash, a= nd avoid bankruptcy, as a part of proper money management. This will also be part of an audit= that should be conducted periodically to ensure proper operational and financial controls.

Hypothesis Testing

This study tests the hypothesis that there is no signific= ant difference among Automated Trading Systems (ATS) regression coefficients. <= span style=3D'mso-spacerun:yes'> This study test such a null hypothe= sis claiming that there is no difference in the values of the predictor indepen= dent variables of the OLS regression equation.&= nbsp; These trades are dealing with a variety of securities including Curr= encies, Commodities, Equities, Fixed Income Securities, & Holding Periods in the number of Days of Exits from the trades.&n= bsp; These systems have been back-tested in the following markets: S&P-500, 10-year T-note, Wheat, Eurodollar, Corn, Cotton, Crude oil, Su= gar, Japanese yen, Live hogs, Soybeans, U.S. bond, British pound, Deutsche mark, Gold, Canadian dollar, Orange juice, Silver, Coffee, Heating oil, Copper, S= wiss franc, to generate these paper profits.

Data Collection

The data collected by the Automated Trading System (ATS) includes Securities Traded Currencies % Wins, & Holding Periods in the number of Days of Exits for Commodities, Equities, Fixed Income, with the duration of 5, 10, 15, Exit Days. These systems have been back tested in the following markets: S&= P-500, 10-year T-note, Wheat, Eurodollar, Corn, Cotton, Crude oil, Sugar, Japanese= yen, Live hogs, Soybeans, U.S. bond, British pound, Deutsche mark, Gold, Canadian dollar, Orange juice, Silver, Coffee, Heating oil, Copper, Swiss franc.

Total Trades, Winners, Si= ze & Intraday Drawdown Explain Paper Profit - Simple Moving Average (SMA) Trend-Following OLS Regression Automated Trading System (ATS) Model

Appendix 1 shows the Test result= s for 65sma-3cc trend-following system in columns 1-7, it includes the Market Dat= e, Start, End of Trade, Paper Profit ($), which is the predicted or dependant variabl= e of this multiple linear regression model, using the Ordinary Least Square (OLS= ) method. Following it on the right hand sid= e are the remainder of the variables, which are the independent or the Predictor Variables. They include the T= otal Trades, Winning Trades (%), Average Win/Loss, Average Trade (S), Maximum Intraday Drawdown (S), Days, & n the number of trades or observations. So for example = of the British pound, the range of dates was 7/75-7/95, starting at the 7/1/1975, = and ending at the 7/1/1995, with paper profits of 125,344, due to a total of 105 trades, 34% of them are Winning Trades, 3.72 Average Win/Loss, 1,193 average trade, and Maximum Intraday Draw-down of -25,431, for a total holding perio= d of 7305 days, which is observation number 1.&= nbsp; In a regression equation form it will look like this:

Paper Profit ($) =3D f (Total Tr= ades+ Winning Trades (%)+ Average Win/Loss+ Average Trade (S)+ Maximum Intraday Draw= down (S)+ Days)

This Automated Trading System (ATS) is the Three Consecutive Closes (3cc) above or below the 65-Day SMA (65sma) (Chande, 199= 9)The 65sma-3cc Trend-Followi= ng System

= This section discusses how to formulate and test a simple, nonoptimized, trend-following system that makes as few assumptions as possible about price action. It arbitrarily uses a 65-day simple moving average of the daily clo= se to measure the trend. Sixty-five days is simply the daily equivalent of a 13-week SMA (13x5=3D 65), representing one-quarter of the year. This is an intermediate length moving average that will consistently follow a market's major trend.”

As the daily data shows, “= ;when the market is trending up, prices are above the 65-day SMA, and vice versa.= In sideways markets, this SMA flattens out and prices fluctuate on either side. Clearly, the trading system picks up and sticks with the prevailing tr= end….

There are many ways to make the decision that the trend has turned up. The usual way is to use a shorter mo= ving average of, say, 10 days, and decide that the trend has changed when the shorter average crosses over or under the longer moving average. If you dec= ide to use a short moving average, its "length" will be crucial to yo= ur results. Another weakness is that often prices will move faster than t= he shorter moving average, so that the entries can seem rather slow.

… September 1995 Japanese yen contract showing the 65-day SMA and the signals generated by the system. … The 65sma-3cc system stayed long throughout this major uptrend in the S&P-500 index in 1995.<= /span>

Hence= , the 65sma-3cc system will require three consecutive closes (3cc) above or below= the 65-day SMA (65sma) to determine that the trend has changed. For example, the trend will be said to have turned up after three consecutive closes above t= he 65-day SMA. Similarly, the trend will have turned down after three consecut= ive closes below the 65sma. Once again, the requirement of three consecutive cl= oses is arbitrary. It could be ten consecutive closes or any other number. Clearly, the results will vary with the number of confirming closes.”=

Data Analysis

---- = Insert Table 1 ---- Micros= oft Office Excel Binary Worksheet Object

Table 1 shows the SUMMARY OUTPUT= of the Regression Statistics, displaying the Multiple R of 0.987426251, R Square of 0.975010602, and the Ad= justed R Square of 0.965639578, indicating that the independent variables explain almost 100% of the variability of the dependant variable. It shows the observations of 23 represent the total number of trades that the system generates.<= /span>

---- = Insert Table 2 ---- Micros= oft Office Excel Binary Worksheet Object

Table 2 describes the ANOVA with= its parameters of df, SS, MS, F, Significance F, Difference, and

Regression. It rejects the null hypothesis cla= iming the regression coefficients are zero by the positive difference between the= F and the F Significant of =3D104.045254- 6.5426E-12=3D 104.045254, >0, at= the .o5 of statistical significance level.

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---- = Insert Table 3 ---- Micros= oft Office Excel Binary Worksheet Object

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

Table 3 shows the independen= t variables Coefficients, Standard Error, t Stat, P-value, Lower 95%, Upper 95%, Lower 95.0%, and Upper 95.0%. Accordingly, the Intercept has a coefficient value of -63259.10465, = while the Total Trades has a coefficient of 186.4034778, so that we can multiply = it by a future number of trade to get the expected contribution to the paper profit of such future trades.

---- = Insert Table 3 ---- Micros= oft Office Excel Binary Worksheet Object

[[[[[[[[[[[[[[[[[[[[[[[[

Table 4 shows the RESIDUAL OUTPU= T, and the PROBABILITY OUTPUT, for each Observation, Predicted Paper Profit ($), R= esiduals, Standard Residuals, and Percentile Paper Profit ($) .= Accordingly, for observation 1 the Predicted Paper Profit ($) value equals $122373.6699, etc. The= table proceeds to cover all 23 trade observations.

For the development of Automated Trading System (ATS) strategies, this study proposes an OLS regression model that will estimate the profits that the trades may generat= e, as part of more effective money management subsystem. Such subsystem may help in correctly assessing the risk of running out of resources and cash in particular.=

The null hypothesis has been rejected in favor of the alternate hypothesis stat= ing the coefficient are not zero, and the equation can explain more than 90% of= the variability of the expected profits. The weaknesses in this system are that all the sample observations h= ave been used for testing. Theref= ore, out of sample observations have not been included, which means that when a trader applies the model to additional “out of sample” transactions, the accuracy of the explanations will deteriorate. Therefore, more testing and adjust= ments will be required, to refine this approach.

Such tests have to be = done on a regular basis and apply to “out of sample” observations, o= r a “hold-out” sub-sample. Such back-testing should be built into trading platforms that use ATS strategies, and should = not require programming, as to make them more users friendly. As of now, most of these methods a= re not built-in and do require some programming, or at least understanding of some programming. Therefore, many = users do not explain the profits and do not estimate formally and quantitatively = expected future profits, and cash-flows. This is leading to a shortage of resources, liquidity problems, cash= -flow shortage, and eventually a premature bankruptcy. To reduce such problems, additional profit projection techniques such as OLS regression models can be deployed = to quantify the risk, and be better prepared to handle the worst case scenario= s.

References

Chande, Tushar S. “Beyond Technical Analysis: How to Develop and Implement a Winning Trading System,” John Wiley & Sons, Inc. = New York, 1995<= /p>

Retrieved: Beyond Technical Analysis.doc - Beyond Technical AnalysisEffect of Exits and Portfolio Strategies on Equity Curves 186 .... The systems co= uld use different types of data, such as 5-minute bars or weekly data. <= b>...... sell at a point five ticks below twice the previous day's tra= ding range subtracted ... The August crude oil contract with curve-fitted system www.fxf1.com/books/english/= Beyond%20Technical%20Analysis.doc

http://www.google.com/search?hl=3D= en&as_q=3Ddays+hours+minute+tick+crude+oil+prices+tracking+equities&= ;as_epq=3D&as_oq=3D&as_eq=3D&num=3D100&lr=3D&as_filetyp= e=3Ddoc&ft=3Di&as_sitesearch=3D&as_qdr=3Dall&as_rights=3D&a= mp;as_occt=3Dany&cr=3D&as_nlo=3D&as_nhi=3D&safe=3Dimages days hours minute tick crude oil prices tracking equities filetype:doc=

=

http://en.wikipe= dia.org/wiki/Behavioral_economics

=

Daniel, K.; Hirshleifer, D.; Subrahmanyam, A. (1998). "Investor Psychology and Security Mar= ket Under- and Overreactions". Journal of Finance 53 (6): 1839–1885. doi:10.1111/0022-1082.0007= 7.

=

Friedman, M., Rushinek, = A. and Rushinek, S. (2006) ‘The effect on achievement of using emerging =

Technology in the managerial accounting course’, The Accounting EducatorsR= 17; Journal,

Vol. 16, pp.25–40. =

Rushinek, A. and Rushinek, S. (1983) ‘An evaluation of mini/micro computer syst= ems: an empirical multivariate analysis’, Data Base, Vol. 14, No. 4, pp.37–47.

Rushinek, A., Rushinek, = S. and Stutz, J. (1983) ‘Development and testing of a discriminant model=

for measuring changes in instructor evaluations due to using computer assisted instruction’,

The Journal of Computers in Mathematics and Science Teaching, Vol. 2, No. 4, pp.17–25.

Rushinek, A. and Rushine= k, S. (1997, December) ‘Rating and ranking best practices of the British=

petroleum oil company and the oil & gas industry: sales to cash forecasting univariate

regression trend analysis and computer modeling’, Oil and Energy Quarterly.

Rushinek, A. and Rushine= k, S. (1998, September) ‘Litigation in Pennzoil and the petroleum <= /o:p>

refining industry: a financial ratio automated peer review analysis of variance inte= rnet

software development’, Oil, Gas & Energy Quarterly, Vol. 47, No. 1, pp.17–32.

Rushinek, A. and Rushinek, S. (1999, September) ‘Correla= ting investors’ preferences to stock characteristics: an algorithm for internet software decision support system development in the crude oil & refining industry’, Oil, Gas & Energy Quarterly, Vol. 48, = No. 1, pp.87–101.

Rushinek, A. and Rushinek, S. (2000a) ‘Oil & gas market, sector & compa= ny 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, pp.671–682.

Rushinek, A. and Rushinek, S. (2000b) ‘Internet fraud auditing: a simulated hea= lth care industry case study’, Journal of Forensic Accounting, Vol= . 1, No. 1, pp.125–234.

Rushinek, A. and Rushinek, S. (2001) ‘The role of the forensic accountant in calculating the damages using the “but-if” analysis in a case of internet day trader & online broker misconduct litigation’, Jo= urnal of Forensic Accounting, Vol. 1, No. 2, pp.241–250.

Rushinek, A. and Rushinek, S. (2002) ‘E-commerce security measures: are they wo= rth it?’, ACM: Ubiquity, Vol. 3, No. 39, pp.1–12.

Rushinek, A. and Rushinek, S. (2003a) ‘What makes a publisher important? Maximization of internet citations methodology’, ACM: Ubiquity, Vol. 4, No. 12.

Rushinek, A. and Rushinek, S. (2003b) ‘The University of DVD’, ACM: Ubiquity, Vol. 4, No. 41.

Rushinek, A. and Rushinek, S. (2003c) ‘Forensic audits of stock market forecasts for Texaco & The International Oil and Gas Industry & Financial Mod= els for Online Broker Litigation’, Oil, Gas, & Energy Quarterly, Vol. 51, No. 4, pp.659–678.

Rushinek, A. and Rushinek, S. (2004) ‘Patents & Provisional Business Method Patent Application (PPA) Disclosure Document Program (DDP) For Building &am= p; Testing Quality Research Productivity Of Expert Witness Testimony & Computer Litigation Support Of Trade Secrets & IP’, Productivi= ty and Quality Management Frontiers.

Rushinek, A. and Rushinek, S. (2005a) ‘What makes users unhappy: share-point te= am services web server security’, ACM: Ubiquity, Vol. 6, No. 2. <= o:p>

Rushinek, A. and Rushinek, S. (2005b) ‘Sarbanes-Oxley Microsoft^® Accelerator Forensic Accounting, Expert Witness Testimony, and Computer Litigation Support’, 2005 American Accounting Association (AAA) An= nual Meeting, San Francisco, California.

Rushinek, A. and Rushinek, S. (2005c) ‘Internet web server comparative forecast= ing sales models: reversed accounting of Texaco Corp and The Oil & Gas Indu= stry linear, exponential, power, polynomial & logarithmic univariate regressions’, Oil, Gas, & Energy Quarterly, Vol. 54, No. 2, pp.87–95.

Rushinek, A. and Rushinek, S. (2007, November) ‘An automated business intellige= nt stock market matching engine for the oil and gas industry: materiality of violating the normality assumption of linear regression control analysis’, Oil, Gas, & Energy Quarterly, Vol. 56, No. 2, pp.435–448.

Rushinek, A. and Rushinek, S. (2008, August) ‘Role Play Forensic Accounting, Auditing & Tax Expert Witness: Providing Testimony and Computer Litigat= ion Support Teaching, Services, Research and Development’, 2008 Americ= an Accounting Association (AAA) Annual Meeting Anaheim, CA. Available onli= ne at: http://aaahq.org/AM2008/cpe/cpe11.htm (accessed on 27 October 2009).

Rushinek, A. and Rushinek, S. (2009a) ‘IFRS compliant real-time 2-way AV (audio/video) HD (high definition) recording audit of effective learning strategies for forensic IT networks of expert witnesses research that focus= es on investigative accounting testimony, & computer litigation support, effective learning strategies’, 2009 American Accounting Associati= on (AAA) Annual Meeting, 3 August 2009, New York. Available online at: http://aaahq.org/ AM2009/ELS01.cfm (accessed on 27 October 2009).

Rushinek, A. and Rushinek, S. (2009b) ‘Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records over the WWW AV IP Network Security Surveillan= ce Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-= Up Instructions for Distance or Blended Education and CPE, Emerging and Innova= tive Research Session’, 2009 American Accounting Association (AAA) Annu= al Meeting, Tuesday 4 August 2009, NY. Available online at: http://aaahq.org/AM2009/Emerging.cfm (accessed on 27 October 2009).

Rushinek, A. and Rushinek S. (2010a) 'Uncooking the books from toxic paper sub-prime mortgages CDS and CSOs material misstatements of the financial services industry: crisis challenges and counterparty surveillance of collateralized debt obligations', The International Journal of Economics and Accounting, Volume 1, No. 1, 2010. (Accepted for publication)

        &= nbsp;       Retrieved on: 9/1/2010: http://www.inderscience.com/search/index.php?action=3Drecord&= amp;rec_id=3D33906"Uncooking the books from toxic paper sub-prime mortgag= es CDS and CSOs material misstatements of the financial services industry: cri= sis challenges and counterparty surveillance of collateralized debt obligations"

http://inderscience.metapress.com/app/home/contribution.asp?refer= rer=3Dparent&backto=3Dissue,9,10;journal,1,1;linkingpublicationresults,= 1:121805,1

Oil ETN Trading Rule Linear & Polynomial Daily Open & Close Regression Forecasts- An Automated Software Development- Exit Strategy And Automated Stop Rules', Oil, Gas, Energy Quarterly, June 2010, Volume 58, No. 4. (Accepted for publication)

Rushinek, A. and Rushinek S. (2010c) 'Assessment automation software for accounting of domain name auctions, forensic audit appraisal, ANOVA of price and co= st, ROI univariate and multivar= iate linear regression forecasting models', International Journal of Managerial and Financial Accounting=

Rushinek, A. and Rushinek S. (2010d) 'For= ensic-free AV surveillance audit podcasts for e-competence of academics and staff R= 11; repurposed traditional instructions for blended, distance and self-study e-learning to maximize lecture- capture ROI', = International Journal of Cont. Engineering Ed. And Life-Long Learning Volume 20 2010 (Accepted for publication)

Rushinek, A., Rushinek, S., Stutz, J.= (2010e) 'Auditing Employer Discrimination Compliance Analysis/ Alert System and= the Convergence of Forensic Experts in Accounting and Computing', 2010 Annual American Accounting Association (AAA) Forensic and Investigative Accounting (FIA) Section Research Conference, Baton Rouge, LA. May 7 - 8, 2010.

Retrieved 9/10/2010: http://aaahq.org/meetings/2010FIA_program.htm

Rushinek, A. and Rushinek, S. (2010f) 'Assessing punitive damage awards = upheld by the courts when academics retaliate defame-Anti sex-discrimination EEOC = Gender compliance Forensic WWW Audit & Salary Forecast Class Action Lawsuit RSS Controls- Multivariate Regression of eMBA Rankings,' American Accoun= ting Association AAA Annual Meeting 2010, San Francisco, CA., August 4, 2010.

           &= nbsp;    Accessed 9/10/2010: http://aaahq.org/AM2010/abstract.cfm?submissionID=3D969<= /span>

=

=

Rushinek, A., Rushinek, S., Stutz, J. (2010g) 'Automated Diversity Compliance real-time AV audi= t, network security, disaster planning, backup & recovery costs reduction sponsor ad funding, Ecommerce podcast revenues from digital media streaming,' American Accounting Associat= ion Annual Meeting 2010, San Francisco, CA., August 3, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/abstract.cfm?submissionID=3D1368

Rushinek, A. and Rushinek, S. (2010h) 'Asse= ssment Automation Software for Accounting, Teaching, and Recruiting Programs: Ecommerce of Lecture Capture DVDs & VOD Casting WWW Methodology<= /em>', American Accounting Association Annual Meeting 2010, San Francisco, CA. Aug= ust 4, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/E= LS01.cfm   http://aaahq.org/AM2010/E= merging.cfm

Rushinek, A. and Rushinek, S. (2010i) 'e-Collaboration Publishing Lecture Capture Contents on the Internet Using Surveillance Technologies am= ong University Administrators, Professors, Students, Auditors, A/V Security, Au= dio to Text Transcribers', American Accounting Association Annual Meeting 2010, San Francisco, CA., August 4, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/Emerging.cfm

Rushinek, A. and Rushinek, S. (2010j) 'Lecture Capture Cell Phone Forensic Audit of Business Continuity Contingency Planning, Emergency Preparedness Response, Disaster & Backup Recovery Using AV Recordings Online via the World Wide Web', American Accounting Association = AAA Annual Meeting 2010, San Francisco, CA., August 4, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/Emerging.cfm

Rushinek, A. and Rushinek, S. (2010k) 'A= ssessment Automation Software for Accounting, Teaching, and Recruiting Programs: e-Commerce of Lecture Capture DVDs & VOD Casting WWW Methodology', American Accounting Association AAA Annual Meeting 2010, San Francisco, CA. August 4, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/E= LS01.cfm

Rushinek, A. and Rushinek, S. (2010l) 'Forensic Accounting Repurposed HD Surveillance Video Podcast Lecture Capture for Distance Ed &a= mp; Blended Learning—Brand Name Audit SEO Social Networking for Expert Witness Testimony & CD Computer Litigation Support', American Accounting Association AAA Annual Meeting 2010, San Francisco, CA., August = 4, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/E= LS01.cfm

9-10-10_Fri_8.15pm_Channel Breakout-Pull Back Pattern (CB-PB) (Recovered).docx

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Appendix 1 shows the Test results for 65sma-3cc trend-following system in columns 1-7, it includes the Market Date, Start, = End of Trade, Paper Profit ($), Total Trades, Winning Trades (%), Average Win/L= oss, Average Trade (S), Maximum Intraday Drawdown (S), Days, & n the nu= mber of trades or observations. So= for example of the British pound, the range of dates was 7/75-7/95, starting at= the 7/1/1975, and ending at the 7/1/1995, with paper profits of 125,344, due to= a total of      = ;     105 trades, 34% of them are Winning Trades, 3.72 Average Win/Loss, 1,193 average trade, and Maximum Intraday Draw-down of -25,431, for a total holding perio= d of 7305 days, which is observation number 1.

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