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Evaluation of GAAP Accounting Paper
Not disclosing the new oil pool to the public and then quietly buying most of its stock as treasury stock is the ethical issue in this text. Before the announcement was made the stock price was listed at just $5, after the discovery and quiet buying of the stock the price was listed at a whopping $28.
The Joyful Gas took advantage of this discovery to make a purchase of the stock at a reduced price before going public with the news. This is not only an ethical issue but it is unlawful and fraud. Had the organization made the discovery of 3x the oil reserve public and then proceeded to buy the stock then there would be no issue.
The organization should have utilized the GAAP to ensure that they were in line with the best accounting practices, this would have also kept them out of looming litigation. Once the layers start being peeled back it will be discovered that they bought stock in bad faith and those that benefited from the actions will be punished and finally the organization will sink like the titanic.
The definition of stakeholders is resource providers, financial analysts, brokers, attorneys, government regulators, and news reporters (Edmonds, 2015). The stakeholders in this text are the ones who have benefited from the discovery of the oil pool, that being the head sheds of the organization.
Board of directors, employees, and anyone who has interest or benefit from the purchase of stock. When the information is made public then this adds a whole other pool of stakeholders, this would include anyone who is trying to get information such as the organizations financial statements. This would be the normal bunch of people such as creditors, investors, government regulating agencies such as the SEC, and media outlets.
Edmonds, T., Edmonds, C., Edmonds, M., Edmonds, J., Olds, P. (2021). Survey of Accounting (6th ed.). N.p: McGraw-Hill Education
I will answer the ethical issue and the stakeholders involved in this situation.
First, the ethical issue. Joyful Gas Company is a publicly traded company that discovered a pool of oil that will 3X their reserves. Nothing wrong with that. What they did after that was that they bought their own shares back. Again, there is nothing wrong with that. What was wrong was that they did not make the discovery public until they bought most of their own stock back.
The stakeholders involved in this situation is the company itself, as they are the ones who are buying their own stock and made the discovery to begin with. The shareholders, this being the executives or insiders, and the institutions and the retail traders.
The executives and the insiders stand to make a lot of money, but the institutions and the retail traders who are unaware of the situation and are selling back the stock to the company are going to miss out. SEC would be another stakeholder, as they would be looking to investigate what happened here and enforce the rules, whether this be against the company, the executives, and/or the insiders.
I should note that insider trading itself is not illegal. Rather it is trading based on material that is not available to the public that would make it illegal. Thus, if you are an executive at a company and the company made a discovery like Joyful Gas Company did and did not make that information public, then it would be in your best interest not to trade. Company insiders also have to disclose their trades made in the company (or companies) that they are insiders of.
Edmonds, T., Edmunds, C. & Olds, P. (2021). Survey of Accounting (6th ed.). New York, NY: McGraw-Hill Education.
SEC. (2015). Insider Trading Policy. Retrieved from https://www.sec.gov/Archives/edgar/data/1164964/000101968715004168/globalfuture_8k-ex9904.htm
Different than search engines, which provide results based on the data entered by a user, recommendation engines aim to provide suggestions and recommendations to users based off their behavior or the behavior of similar users. The goal of the recommendation engine is to provide options to users in order to stimulate ideas, or product options when used for marketing (Maruti Techlabs, 2021). LinkedIn and Facebook are good examples of recommendation engines as they are able to use a contact list to provide examples of additional connections a user may have.
Many streaming and music apps use recommendation engines to do content-based filtering. This provides recommendations for new movies, TV shows, songs or artists based off of searches, selections, likes, and reviews (Turban, Pollard, & Wood, 2018, p. 189).
In order to implement a content-based recommendation engine, website operators need to have enough data from their users in order to provide the recommendations using content-based filtering. For example, in order for Netflix to provide recommendations for movies, a consumer will need to watch a certain number of movies in order for similar movies to be recommended (Turban, Pollard, & Wood, 2018, p. 189).
Recommendation engines do have their challenges. New users need to have a minimum amount of searching or content in order for recommendations to be made, and if users don’t continue to provide information, the difficulties can continue. The engine itself must have enough features to analyze and be set up in a way for the engine to use appropriately. Also, if the recommendation engine is not designed correctly, it may give very similar recommendations that does not broaden the options for the user (Turban, Pollard, & Wood, 2018, p. 191).
Maruti Techlabs. (2021). How do recommendation engines work? What are the benefits? Retrieved from MarutiTech: https://marutitech.com/recommendation-engine-benefits/
Turban, E., Pollard, C., & Wood, G. (2018). Information Technology for Management, 11th edition. Hoboken: Wiley.
The difference is the assumption of a search engine; which assumes the consumer knows what they are looking for. With a recommendation engine, it is a way to proactively identify products that have a high probability of being something the consumer might want to buy (Efraim Turban, 2018). Recommendation engines are like building a consumer profile for advertisements. When the consumer logs in they are given recommendations (like on Amazon) of items they may be interested in based on previous purchases.
Social media outlets and streaming services take advantage of recommendation engines to get users to view more or new content. The algorithm that Facebook uses for the recommendation engine needs to be tweaked. By clicking on a video on the newsfeed, the engine now thinks this is an interest and will thus present similar videos.
The next few logons will have similar videos of that one. When considering either style of service, what is transpiring is content-based filtering which is recommending products based on the product features of an item the customer has interacted with in the past (Efraim Turban, 2018). Then there is collaborative filtering where recommendations are based on a user’s similarity to others.
Before implementing content-based recommendations operators must learn what the consumer’s likes and dislikes are about their product. Content filtering bases the products on the features of other products the consumers have interfaced with in the past what the similar attributes are to other products. A good example of this is the Music genome where over 450 attributes are used to describe songs (Efraim Turban, 2018).
(Efraim Turban, 2018) pg. 191. Cold Start/New user limits recommendation engines due to this type of consumer has not provided any information to the engine. Without some information on likes, purchases, or features the recommendation engine is unable to provide recommendations. It has been suggested by Torishi and colleagues (2011) using existing social media profiles to develop a baseline in situations of new users with insufficient histories.
Sparsity depends on having information about a critical mass of users to compare to the target user to create reliable or stable recommendations (Efraim Turban, 2018). If the product does not have a lot of reviews, then it is not possible to identify a group of people with similarities of preferences to be exploited.
Limited feature content requires sufficient information about product features and information in a structured format so it can be interpreted by computers. The downfall here is having to manually place features information.
Overspecialization can only recommend items that are highly similar to a user profile then the recommendations may not be useful. With narrow configurations, users would only see the items they may have liked prior.
Efraim Turban, C. P. (2018). Information Technology For Management. Hoboken: John Wiley & Sons.