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Write for me what is a capstone class mean

Write for me what is a capstone class mean capstone night light plate cover for money ubmta simple letter writing ´╗┐accounting revenue errors is not about fraud we're not talking about fraud here fraud is the malicious intent to steal to swindle people by top management board of directors or individuals that could be directed toward assets manipulation of financial statements to stockholders and so forth what I'm talking about here is accounting revenue errors that could be due to a number of reasons misunderstandings misapplication and sometimes just stuff happens stuff happens because we don't understand or sometimes there's tremendous pressure on management to do things because things happen and one of the studies that I've been looking at for some time is how we can do some things now in auditing there's been tremendous pressure on us as auditors to address the issue of fraud of course but also on the issue of keeping on top of that and as you know in the audit model we have to start with the top down we start with a risk assessment the risk assessment we analyze the top board of directors for corporate governors we look at our risk to find what could be a high priority as far as the business nature if the business is involved with high-risk nature be it financial derivatives we look at that versus may be involved with high inventory counts and we define or are a risk or auditive system to address those as we will down line to do in substantive testing and tests of details and so forth like this so in the issue there though we still need to look at being able to predict the possibility of errors so SAS 56 is SAS 99 are standards that address the issue of revenue recognition as auditors officially in the model of continuous auditing we have to be cognizant going in during and after the audit that we have to have models that help us assist that now in that situation is the more we can have in our toolbox or toolbox or many that can help us do that the better we can do the research problem that I discussed here is the ability to identify and predict accounting revenue errors to provide lower monitor and control agency costs for public accounting firms with public stock firms which are public you know sock firms in New York Stock Exchange the Nasdaq the public accounting firms need a better method to predict stock firms that commit accounting revenue errors in the future this leads to higher control costs and lower corporate governance value to the Audit Committee as we know all our public accounting firms must report to the Audit Committee of how we're doing if we cannot predict accounting revenue years it leads to higher audit costs which leads to higher transaction costs which leads to higher costs to the stockholders so we're trying now to provide a tool to reduce those costs by being able to predict ahead what firms would do that that's what the game is being played right now as auditors and I am a big believer in continuous auditing we're leaving in tools like well it's good to you know I'm working now some of the current literature out there there's been a lot of course on fraud bankruptcy and so forth but in the issue pacifically of accounting revenuers started very much back with my good friends at universe Utah Lobeck Martha innings and Willingham where they did a great survey of about several hundred public accounting partners in the United States in 1989 where they identified three conditions for errors condition motivation and opportunities and they found within there's that people commit fraud and commit errors if the conditions are right which could be an economic condition where the industry sector is declining or the motivation is right where I am under tremendous pressure to do something maybe because I have a board who is creating incentive plans that say if I don't meet my numbers I must then there something or else an opportunity I must be in a position to do something or else I can't do something if I'm the janitor the firm is not that I don't want to improve I don't have the opportunity to push the numbers but if I'm the CEO and the staff upstairs I do have the opportunity to make the changes so the lubbock inning in william study was a groundbreaking initial work that actually identified with using public accounting firms partners and that was the beginning of some fundamental study about what is actually happening in the United States cochlea Brown did the first work in neural networks which said that material errors and they look at 36 month data but that was the first study of using neural networks in the research of accounting revenue errors whoa looked at tax cases using 90 cases that were fraudulent in 90 cases that were not but that work was done pretty much using tax cases busting wine back in 1998 was the first wonder actually was able to create an accuracy for this using 800 observations and the neural network in that case was able to create sixty percent accuracy using simulation data which meant it was not actual data at all they simulated that situation pretty much Lynn rhinebeck and becca used a fuzzy neural network using six financial ratios from 1982 1985 used 200 firms 160 were in the air firms and 40 were errors and the only thing that came out of that was that they were able to prove that the fuzzy neural network was better at predicting non error firms were better than logistic but were not significant significant in proving error firms so it was kind of in billa in that situation NASDAQ and Costa in 2003 basically was a metadata file for the last ten years and did not show any significance as far as a predicting accounting revenue eres cosa cava in 2004 published the paper was very much summarize again what has happened in the last five years now in Kosova and back in 2007 looked at it from a continuous auto point of view they said that neural networks in their paper were just competitive with logistic regression roketa and levena in 2008 they did something very unique they use a neural network to look at restatements of accounting revenue statements for 180 sample firms and 51 Test firms and only thing they found out was that there were able to predict non-revenue firms first as compared to the logistic regression so the data has not been able yet to say that anything has been able to predict accounting revenue errors and the only artificial machine language tool has been used in neural networks so we're still pretty much in the gray area out there an existing current literature doesn't mean there hasn't been other things as far as bankruptcy protection going concern management fraud that's other areas in accounting literature but as far as this specific area here this only thing has been out there pretty much know what I decided to do when I got asked for this I decided to follow up raas s and I said to myself how can we structure this methodology what I would want to do as an accountant and don't what I didn't bore her what I want to do is say to myself a water established two different paths I want to see if cart could do something and as an accountant said test edition math person want to keep it simple I want to see can cart do something without the bells and whistles if I took continuous data if I took data that had independent variables net sales net sales changed from previous year net sales changed from three previous years gross profit and I'm not going to read all this but pretty much you can see this these are my independent variables and this information is information that can be attained pretty easily that's an accountant you know me that's straightforward information that comes from comp you're sad 4x years and I use that as my input ok that's pretty sounds straightforward nothing complex about that I don't have to do partial differential equations I don't feel like an handle that that's my input going in what's my output capstone courses meaning Farmingdale State College.

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