LAWS3101: Tax Advice Statement (Semester 2, 2024) Tax Advice Statement Problem Sets Instructions 1. Read the Task description and Criteria, marking and performance standards on the course Blackboard site. 2. You are required to prepare typed-up responses to all three problem sets described herein in one Word document. 3. This is an individual assessment activity. Students must work alone. The use of generative artificial intelligence (AI) tools is not permitted. Any attempted use of AI may constitute student misconduct under the Student Code of Conduct. Students must read and comply with the Integrity Notice for this assessment item available on the course Blackboard site. 4. You are required to conduct research in preparing your responses to the problem sets using four specific ATO Guides. These guides and specific instructions about the relevant parts of each guide you must use, are set out on the course Blackboard site: >> Assessment-folder >> Tax Advice Statement-folder. You must not include references to these ATO Guides in your responses. Therefore, there is no word limit allocation for references to these ATO Guides. Simply use the ATO Guides to formulate your responses. 5. There is no requirement to use a specific font type or font size when you type up your responses. Using Arial or Times New Roman in 10 or 12 font size works well. Do not include the problem sets in your responses, just your answers. 6. Type your name, surname and student ID at the top of the first page. 7. You are not permitted to use footnotes, end notes , spreadsheets or text boxes. You must not include a separate bibliography. As a matter of principle, you must reference relevant case law and sections and divisions of the Income Tax Assessment Acts in your responses that are specifically listed in the Topic Learning Plans. Do not include other references in your responses, as there is no word limit allocation for other references. Therefore, you must treat this assessment item problem sets as you would any other question. 8. The only permitted submission method is to upload one Word document via Turnitin, using the Tax Advice Statement submission link on the course Blackboard site: >> Assessment-folder >> Tax Advice Statement-folder. Students cannot resubmit from 5 pm on Thursday 10 October 2024. It is your responsibility to retain a copy of the Turnitin receipt. Email or paper copy submissions will not be accepted. Contact AskUs through the UQ Library and retain evidence of your actions if you encounter technical difficulties. 9. Due date and time: 5 pm on Thursday 10 October 2024. 10. The total word limit for your responses that includes all headings, all responses, all references, all pages and all words, all calculations and all amounts is 2500 (two and a half thousand) words. If you exceed the word limit, the course staff will only grade the first 2500 words in your submission. Therefore, no penalty applies to submissions that exceed the word limit. Only the first 2500 words are graded. Total marks for the Tax Advice Statement = 70 marks Weighting in final mark = 30% Problem Set 1 Abigail Barnes and her three sisters, Caitlin, Donna and Elizabeth, started a private company, Sisterhood Investments Pty Ltd, in 2010. They each own 25% of the shares in the company. Sisterhood Investments Pty Ltd sells expensive clothing items to members of the public, earns rent from office buildings that the company rents out to tenants in Brisbane and Townsville, and receives dividends from a share investment in the Commonwealth Bank Ltd, Australia’s largest listed bank. This is the taxable income calculation of Sisterhood Investments Pty Ltd (hereafter referred to as ‘SI’) for the 2023–2024 income year ending 30 June 2024: $ Turnover from the sale of trading stock, income from business, s 6-5 ITAA97 40,000,000 Fully franked dividends received, Commonwealth Bank Ltd, s 44 ITAA36 140,000 Gross-up, being franking credits associated with dividends received from the Commonwealth Bank Ltd, s 207-20 ITAA97 60,000 Rent received from office building tenants, assessable income, s 6-5 ITAA97 8,000,000 Total assessable income 48,200,000 Less: Tax deductible expenses in relation to retail trading activities, s 8-1 ITAA97 32,000,000 Tax deductible expenses for the office building rental properties, s 8-1 ITAA97 2,450,000 Tax deductible capital allowances, div 40 ITAA97 5,450,000 Taxable income $8,300,000 In addition, this is the other relevant information about SI for the period 1 July 2023 to 30 June 2024: • The opening credit balance of SI’s franking account on 1 July 2023 totalled $345,000. • During this 12-month period, SI made PAYG income tax instalment payments totalling $1,350,000. • On 28 August 2023, SI paid its shareholders a fully franked dividend totalling $1,800,000. • On 15 October 2023, SI received a tax refund of $495,000 in relation to its 2019–2020 income year after it successfully challenged an ATO decision to deny SI tax deductions for legal fees it incurred. • On 14 February 2024, SI paid its shareholders dividends totalling $900,000 with franking credits of $240,000 attached. Abigail comes to you for advice: She wants to know what the maximum amount of a fully franked dividend is that SI can pay shareholders on 30 June 2024 without incurring franking deficit tax. In preparing your detailed tax advice statement for Abigail, show all your calculations and provide reasons for your answers. In this problem set, there are no case law or sections and divisions from the Income Tax Assessment Act to reference. In relation to the franking account that you must prepare as part of your advice statement, use the following five-column layout: Date Transaction Debit Credit Balance Total marks for this problem set: 10 marks How many words can you expect to type up for your response? No more than 250 words. It is possible to prepare an excellent-quality response in about 230 words. Problem Set 2 Ross Kirkwood is an Australian resident individual, aged 56. He has always been an Australian resident individual for income tax purposes. Ross is an award-winning freelance press photographer who won the Pulitzer Prize for news photography in 2020. Ross comes to you for advice: He wants to know what his taxable income is at the end of the 2023-2024 income year ending 30 June 2024. In preparing your detailed tax advice statement for Ross: • Ignore Goods and Services Tax (GST). • Show all your calculations and provide reasons for your answer, referencing relevant sections and divisions of the Income Tax Assessment Acts listed in the relevant Topic Learning Plans. Do not include other references. • Apply the criteria for preparing capital gains tax (CGT) responses as explained on pages 1-3 of the Topic 8 Learning Plan. Therefore, in preparing your responses, you must apply the CGT method statement described in the law. • Ross wants to return the lowest possible capital gain in each case. • On 30 June 2023, Ross had unapplied capital losses of $90,000 from the sale of a rental property in 2017, and unapplied losses of $12,000 from the sale of a piano in 2021. The piano was originally manufactured in 1890. Ross played the piano in his free time. His taxable income from his business for the 2023-2024 income year, before taking into the information below, is $485,000. In addition: • He sells a camera for $12,000 on 1 July 2023. He purchased the camera on 1 November 2021 for $18,000. Ross used the camera in his business from that day. The effective useful life of the camera is four years. Ross uses prime cost and applies div 40 ITAA97. • He sells all 10,000 of his National Australia Bank Ltd shares on 30 June 2024 when the share price is $40 per share. Over his ownership period, Ross received dividends from the National Australian Bank Ltd every year, including fully franked dividends of 98 cents per share on 29 September 2023. His financial adviser, Fred Astaire, provides Ross with the following investment summary statement: • He sells his four-bedroomed home in Greenslopes on 30 April 2024 for a selling price of $1.6 million. He pays a real estate agent commission of $38,400 for finding a buyer for his home. Ross originally purchased the home for $350,000 on 1 February 2010 after accepting a job with a Brisbane-based newspaper. A Sydney-based newspaper previously employed him. He paid cash for this Greenslopes home because he sold his home in Sydney for a handsome profit. Ross moved into his Greenslopes home on 1 February 2010. Ross regularly went on work photo assignments overseas. Based on his work diary, from 1 February 2010 to 31 December 2018, he spent 748 days outside Australia on these short work assignments. Ross then agreed with his employer that he would spend one year, from 1 January 2019, working in war-torn countries in Central Africa to photograph the impact of war on children. He rented out his home for the duration of this work assignment. Unfortunately, Ross was taken captive by a militant group before the end of this work assignment. The tenants extended their lease until Ross was finally released in March 2020. The tenants vacated the premises on 31 March 2020. Ross moved back into his home on the next day. One of the photographs he took while being held captive won him the 2020 Pulitzer Prize for news photography. His new-found fame as a press photographer and his harrowing experience of being held captive by the militant group spurred Ross to resign from his job and commence working as a freelance photographer on 1 June 2020. He set up two rooms in his home as a studio and office for his new business. These rooms took up 35% of the floor space. He used the rooms for that purpose until the day he sold the home. These are the other details of the home: • The home is situated on a 2-acre block. • He has never claimed any tax deductions in relation to the home. • He paid a real estate buyer's advocate agent $3,500 for finding him the home in 2010. He used this agent because he did not have the time to fly to Brisbane to look for a home. • He paid transfer duties of $5,000 when he purchased the home. • He paid a solicitor $1,500 in legal fees when he purchased the home and $2,700 in legal fees when he sold the home. • In 2015, he paid $23,000 to have an in-ground swimming pool built on the property. • He paid a company $2,750 to have the home professionally staged with beautiful furniture to increase the marketability of the home when he put it on the market for sale. Total marks for this problem set: 40 marks How many words can you expect to type up for your response? No more than 1,650 words. It is possible to prepare an excellent-quality response in about 1,500 words. Present your responses for the detailed calculation of his taxable income in a table format like this: $ Problem Set 3 Louisa James comes to you for advice: On 18 August 2023, she purchased a unit in a newly constructed unit complex from the developer as an investment property. She immediately advertised the unit for rent on that day. Louisa wants to know the income tax implications of her decision to invest in the property at the end of the 2023–2024 income year ending 30 June 2024. Her clear instruction to you is: • That she wants to claim the largest possible tax deductions as quickly and as early as possible. • She wants you to use the ATO’s effective useful life of assets. • She wants you to provide her with a detailed taxable income calculation, showing all your calculations and reasons for your answer, referencing relevant case law and sections and divisions of the Income Tax Assessment Acts listed in the relevant Topic Learning Plans. Do not include other references. These are the facts that you must use in preparing a detailed calculation of the tax implications in relation to the property: Before she made the decision to invest in this, her first investment property, she consulted a financial adviser who charged her $750 on 10 July 2023. The financial adviser explained the advantages and disadvantages of owning an investment property to Louisa. After seeking this financial advice, she decided to find a new development to invest in. The developer sold the property to Louisa for $1.2 million. The breakdown of this purchase cost, as certified by the supervising architect, is as follows: • Constructions costs = $1,150,000. • Automatic garage doors = $14,600 • Carpets = $12,000. • Ceiling fans = $1,750. • Dishwasher = $1,250. • Stove = $2,400. • Window shutters = $18,000. Louisa funded the purchase cost using a 20-year mortgage loan. She: • Paid stamp duty on the mortgage totalling $450 on 18 August 2023. • Incurred interest totalling $65,000 during the income year. Louisa rented the property to a tenant from 1 September 2023: • The tenant paid a rental bond totalling $1,600 to the Queensland Residential Tenancies Authority on 1 September 2023. • Louisa received rent totalling $34,000 during the income year. These are Louisa’s other expenses incurred during this income year: • The tenants damage a wall when they move in. Louisa obtained two quotes from a builder, Derek’s Wall Repairs, to repair the damage. One for $2,500 with a tax invoice that includes an ABN. Another for $1,800 cash without a tax invoice. Louisa accepts and pays the lower quote in cash totalling $1,800. A month later, the tenant reimbursed her the full $1,800, opting not to use their rental bond for that purpose. • She visited the rental property five times using her own car. She travelled a total of 200 kilometres on these trips. Louisa is aware of the cents per kilometre method under div 28 ITAA97. • She pays the Brisbane City Council $2,000 in rates for the property. She makes one late rates payment and as a result, the Brisbane City Council charged her interest on the late payment totalling $38 that she also paid during this year. • She pays the body corporate the following levies: o Admin fund levies totalling $2,800. o Sinking fund levies totalling $1,200. o A special levy charged over two years (this year and the next year) to fund the construction of a swimming pool at the unit complex as part of the common property. This year’s levy that Louisa paid totals $3,000. Total marks for this problem set: 20 marks How many words can you expect to type up for your response? No more than 600 words. It is possible to prepare an excellent-quality response in about 540 words. Present your responses in a table format like this: $
Java Lab 19: File I/O This lab tests the hypothesis that buffered readers and writers are faster than unbuffered, and whether wrapping them inside Scanner/PrintWriter make any difference. 1. Create project Lab19 with Lab19Main and six static long methods – three that write a data file and three that read it. The first line in each of these methods should be long startTime = System.nanoTime(); At the end of each method, declare another long set to nanoTime( ), subtract them, and return the resulting value. The nanoTime( ) method gives the current time in nanoseconds and returns a long integer. Each method will have two parameters, a file name and an int, n. It will contain a for loop that counts from 1 to n and either writes or reads a single char “A” to a file that many times. For example, if the parameters are “myfile.txt” and 1000, you’ll write “A” (or read that character) 1000 times. Use the code on Slide 13 from today's lecture, "Scanner, cont." that shows some code with a Scanner wrapping some other stuff. Instead of reading or writing int's, the code will read or write char's. 2. printWriterTest() will use the three-part wrapping: FileWriter inside a BufferedWriter inside a PrintWriter. Use the print(char) method to write out “A” inside the for loop. 3. bufferWriterTest() will use two-part wrapping: FileWriter inside a BufferedWriter. Use the BufferedWriter write(char) method to write out 'A' inside the loop. Make sure you new up a BufferedWriter object and use it inside the for loop. 4.fileWriterTest() will just use a FileWriter. Use the FileWriter write(char) method to write out 'A' inside the loop. Make sure you new-up a FileWriter object and use it inside the for loop. 5. scannerTest() will use the three-part wrapping: FileReader inside a BufferedReader inside a Scanner. Before the for loop, put the line: scanner.useDelimiter (""); to ensure single characters are read – Scanner really wasn't meant for this, so it's a bit of a kludge. Use scanner.next().charAt(0) to read single characters. 6. bufferedReaderTest() will use two-part wrapping: FileReader inside a BufferedReader. Use read( ) inside the for loop. 7.fileReaderTest will just use a FileReader. Use read( ) inside the for loop. 8. In main, call each of the six methods in the order given using "test.txt" and 10000 as the parameters. Display the resulting values in a formatted printf( ) statement, with a label for each run (method name) and using %15d for the return value of the method (so the numbers get lined up). 9. Repeat #8 using 1000000.
MGT204 Corporate Finance &Portfolio Valuation Individual assignment Weighting-100%of the marks for this module This is an individual assignment of 2,500 words for guidance purposes. The hand in date is: Tuesday 20th May 2025 by 4.00pm UK time. Requirements: Students are required to select ONE of the two possible questions contained within the assessment.Each question carries a maximum mark of 100 marks. The assignment has been designed to cover the following learning outcomes associated with successful completion of the module: LO1: Compare and contrast a range of theoretical models that underpin corporate finance and portfolio valuation decision-making. LO2: Apply,analyse,and interpret how financial data influences financial decision-making. LO3: Critically reflect upon how financial decision-making tools and techniques impact upon the value of a portfolio. Part A You have recently obtained employment within the investment analysis team at Fleetwood PLC,a financial advisory firm that makes regular investments in several FTSE350 companies'ordinary shares that are listed on the London Stock Exchange (LSE).Initially, you have been provided with f1 million worth of company funds to invest over a 12-week timeframe. As part of the initial investment portfolio,you are required to select five FTSE350London Stock Exchange listed companies and invest the funds equally within their ordinary shares.Furthermore,on a minimum of three separate occasions within the 12-week timeframe you have been asked to sell one of the investments and to reinvest the funds in a differing company that you have chosen. Required: Provide a report to the Board of Directors of Fleetwood PLC that offers critical reflection and analysis of the decision-making process regarding the investments undertaken across the 12-week timeframe. In this section students should critically analyse and reflect upon the various decisions taken regarding the investments made across the twelve-week timeframe.The decision-making process must be underpinned by relevant themes and concepts that are prevalent within the field of corporate finance and are covered within the modular content.The introductory section of the report should provide a clear,robust rationale that clearly justifies and evaluates why the selected companies were chosen as part of the initial portfolio.Upon removing and adding to the portfolio the student should offer clear justification for the choices undertaken and support this with relevant financial information,analysis and evaluation that draws upon a range ofcorporate finance topics.Upon adding a share to the portfolio,it must be retained within the portfolio for a minimum one-week duration.It would be expected that the report would incorporate a range of charts,tables,and screenshots of portfolio performance to visually enhance the quality of the work submitted. Within the recommendation/conclusion section students should clearly identify and reflect upon the decisions undertaken and elaborate upon what they have learnt across the duration of the portfolio-based exercise.Students will not gain further marks based upon the portfolio performance, rather they should reflect upon the decisions undertaken during the simulated exercise. Total for Part A-100 marks Part B You have recently joined the investment team as a financial analyst at Patterson PLC,a financial advisory firm based within the City of London.As part of the initial duties you have been asked by the Board of Directors to investigate the five-year performance of ONE of the following FTSE100 listed companies provided below with the view to potentially investing future company funds into the chosen company: i. BP PLC. ii. Tesco PLC. iii. Marks &Spencer Group PLC. iv. Astrazeneca PLC. v. Rio Tinto PLC. vi. Sainsburys PLC. vii. BAE Systems PLC. vii. Next PLC. ix. National Grid PLC. x. BT Group PLC. xi. Sage Group PLC. xii. JD Sports Fashion PLC. Required: Select any ONE of the above companies and prepare a report for the Board of Directors of Patterson PLC analysing and evaluating the five-year performance of the chosen company. In this section students should demonstrate both knowledge and understanding ofa range of topics,theories,and concepts covered within the MGT204 Corporate Finance &Portolio Valuation module.A report format should be utilized that offers clear,concise analysis, resulting in the production of robust recommendations.You should choose to analyse approximately 3 topics from the syllabus including the key performance indicator ofshare price analysis/fluctuations/key events.Further topics and concepts that can be considered for inclusion within the report include evolution of corporate governance policies,financing strategies,investment strategies,capital structure alterations,dividend poltcies,financial ratio analysis and merger and acqutsition activities.This is not an exhaustive list and students should be prepared to investigate other key aspects from the module if they feel necessary. It is imperative you choose one of the above companies as listed within the above. Total for Part B-100 marks
Cost-Benefit Analysis ( Spring 2025) Assignment #1: Efficiency of the EPA’s Waste Emission Charge This assignment is worth 100 points and is due by 11:59 pm on March 2, 2025. You may discuss the assignment and how it can be solved with your classmates, but you must do your own work. Ultimately it is an individual assignment. All calculations and written narratives should be your own. A Pigovian tax (also spelled “Pigouvian tax”) is a tax assessed on goods and economic activities that create negative externalities for society. The tax is meant to discourage the production or consumption of these goods and activities by creating a financial incentive against them. Ideally, the tax should be equivalent to the external damage caused by the production and use of the good or activity. In November 2024, EPA implemented a Waste Emissions Charge (WEC) as required under the Inflation Reduction Act (IRA) of 2022. In this law, Congress directed the EPA to collect a Waste Emissions Charge (WEC) on waste emissions of methane from certain oil and gas facilities. The WEC applies to petroleum and natural gas facilities that emit more than 25,000 metric tons of CO2 equivalent per year, that exceed statutorily specified waste emissions thresholds set by Congress, and that are not otherwise exempt from the charge. This methane fee is, essentially, a Pigovian tax on methane emissions. The WEC starts at $900 per metric ton for 2024 reported methane emissions, increasing to $1,200 per metric ton for 2025 emissions, and $1,500 per metric ton for emissions years 2026 and later. More information on this rule can be found at https://www.epa.gov/inflation-reduction-act/waste-emissions-charge. Also, in December 2023, the EPA released a report that estimated climate benefits using a new set of Social Cost of Greenhouse Gas (SC-GHG) estimates. These estimates incorporate recent research addressing recommendations of the National Academies of Science, Engineering, and Medicine (2017). One set of values in this report was the Social Cost of CH4 (i.e., the social cost of methane) using three near-term Ramsey discount rates. That report can be found at https://www.epa.gov/system/files/documents/2023-12/epa_scghg_2023_report_final.pdf. The Regulatory Impact Analysis for EPA’s 2024 final rule implementing the methane waste emissions charge (WEC) is posted on the class site. For this assignment, you will assess the economic efficiency associated with the WEC as specified by Congress and compare it to alternative methane fee values based on EPA’s report on the social cost of greenhouse gases. Background information on a Pigovian tax with a negative externality that can be abated In week 3, we framed a Pigovian tax in the context of a bag tax, but then we switched to describing a more general “negative externality” from a generic polluting good. We described this in a way in which the externality's damage was added to the supply curve. The graph we used looked as follows. In this graph, the marginal social cost is higher than the marginal private cost because the consumers do not consider the damage from the externality. As a consequence, the market supplies too much of this polluting good. In this scenario, placing a Pigovian tax increases the price of these goods, which drives some buyers out of the market and reduces some deadweight loss. If the Pigovian tax is set equal to the social damage caused by the externality, then the benefits of the tax (B + C + F) exceed the cost or the lost market transactions (B +F), and the optimal amount of external damage is reduced, resulting in benefits equal to the deadweight loss, (C). This is the standard approach to illustrate the benefits of a Pigovian tax, but it has several assumptions embedded in the example. In particular, this example assume that the externality is entirely inseparable from the output of the market good. Notice that we didn’t discuss “abating” the pollution, or doing something to continue consuming the same amount of the good but reducing pollution involved. In this example, the only way to reduce the external damage was to consume less of the market good. An alternative is if it is possible to reduce the pollution from the production of the good, but at some cost to the supplier. This is what we mean by “abatement.” Firms producing the market good can reduce the damages from the pollution by expending some amount of money, referred to as “abatement costs.” For example, imagine a factory that produces some market good, but that production also cause air pollution. The firm can continue to make the good but can reduce the air pollution by, say, releasing the pollution through a tall smoke stack to increase dispersion, using less pollution-intensive inputs, or putting a device on the smokestack that removes pollutants from the exhaust stream. Each of these is possible but may come at a different cost. A rational firm looking to reduce pollution will choose the least costly approaches first and then move on to the next most expensive as more pollution is reduced. To illustrate this, we can focus exclusively on the damage function from the externality and not include the supply curve. That is, we are only looking at the difference between S* and S# in the graph above. We can put emissions on the x-axis and the monetary cost of that pollution on the y-axis. This would look like the following. We assume that the total damages rise as emissions increase, so the total damage function increases. A common assumption is that the damages rise at an increasing rate, so the total damage function curves upward. The marginal damages from pollution emissions is the slope (or the first derivative) of the total damage function, which we represent as an increasing, linear marginal damage function. This doesn’t have to be the case. If the total damage function was an upward-sloping linear function, then the marginal damage function would be a horizontal line equal to the slope of the total damage function. In either case, the marginal damage at any level of emissions is the cost incurred from the last unit of pollution. The total damages are the area under the marginal damage curve (or the integral under the marginal damage function), labeled “D” in the graph above. We can illustrate the abatement costs similarly, as follows. If the firm is not abating any pollution, then the total abatement cost is zero. With no abatement, emissions are at the maximum amount the firm will produce. If the firm chooses to reduce emissions, the total cost of that abatement will rise, and the emissions from the firm will decline. This is represented as a total abatement curve that slopes downward as emissions increase. If total abatement increases at an increasing rate as emissions are reduced, the total abatement curve looks like we have drawn it. Similar to the discussion above, the marginal abatement cost function is the slope (or first derivative) of the total abatement curve, and is represented by a linear, downward-sloping line. The marginal abatement cost is the cost of reducing one more unit of emission at any level, and the total abatement cost is the area under the marginal abatement cost curve, labeled “C” in the graph above. Combining these two graphs, the marginal abatement cost (MAC) function slopes downward and the marginal damage function (MD) function slopes upward. The optimal level of emissions is (hopefully, unsurprisingly) where the marginal damages of emissions equals the marginal cost of abating those emissions. As you learned from the lectures, this optimal level of emissions can be achieved by setting a tax equal to the point of this intersection of MD and MAC. The welfare impacts of this tax are as follows. · The tax would encourage the firm to reduce emissions to e*. This reduction would require the firm to pay a total of C in abatement costs (remember, total abatement costs are the area under the MAC function). In addition, the firm would have to pay the tax rate for all emissions it still produces, so it would pay A + B in taxes. So, the total cost to the firm would be A + B + C. · Those affected by the emissions can be considered third parties, and they gain the benefit of the area under the portion of the damage function that has been reduced. So, third parties benefit by C + D. Note that this area differs from how we displayed the total damages above. Before the tax, the total damages the third parties endured was B + C + D. After the tax, the third parties only suffer B. So, the benefits to these third parties are C + D. · The government receives the tax revenue of A + B, and presumably will transfer that tax money to various people in society. · So, the social benefit of this action is area D. This is the scenario we are addressing with the methane fee. Oil and gas facilities produce fossil fuels, but some of that production releases methane emissions. Methane emissions cause climate damage, and the government is imposing a tax to reduce the emissions and the associated damage. If the tax is set equal to the marginal damages at the optimal level, e*, then the tax is an optimally efficient Pigovian tax. As it turns out, marginal damages from climate damages can be represented as a flat line. This is because the total damage function is almost linear for small amounts of emission reductions, so the marginal damage function can be represented as a horizontal line. In this case, the firm suffers total costs of A + C, comprised of C in total abatement costs and A in tax expenditures. Third parties gain benefits of C + D from reduced methane emissions. Finally, the government gains tax revenue of A. So, the total benefit is D. 1. Familiarize yourself with the 2024 Regulatory Impact Analysis of the Waste Emissions Charge. a. Read the Executive Summary (from page 1-1 to 1-10) to get a general sense of the rule. b. Notice that the Executive Summary says the following. The social cost of energy market impacts is the loss in consumer and producer surplus value from changes in natural gas market production and prices. The economic impacts analysis uses a partial equilibrium model and estimates that the impact of the gas market is minimal, with the largest impact occurring in the first few years with a price increase of less than 0.1% and a quantity reduction of less than 0.1%. As a consequence, you will not analyze the social cost of energy market impacts. You will only analyze the total cost of the the engineering costs for methane mitigation actions implemented by the oil and natural gas industry to reduce WEC obligations and compare those costs to the social benefits. c. Read the introduction to Chapter 5 and Section 5.1 (from page 5-1 to the top of page 5-6) to understand the EPA’s approach to estimating the social cost of methane mitigation. Also read the section on the oil and gas sector’s methane mitigation potential and the WEC transfer payments (from the last paragraph on page 5-15 to page 5-17) d. Read the introduction to Chapter 6 and Section 6.1 from page 6-1 to the top of page 6-14 to understand the EPA’s methodology to estimate the climate benefits from methane mitigation. Note that there is more information in Section 6-14 (from page 6.15 to page 6-12) but you will not need this information for this assignment. There is a lot of information in the pages you will read, but only Tables 6-1 and 6-2 contain the information you will need for this assignment. Avoid getting bogged down in the details and read the chapter for a general understanding. e. Reach Chapter 7 (from page 7-1 to page 7-9) to understand how to compare the costs and benefits that you will calculate. 2. (20 points) Develop a conceptual understanding of estimating the costs of methane abatement using a methane fee. Using the data for 2026 from the RIA, estimate the quantity of methane emission abatement that would be induced using the congressionally mandated WEC and the methane emission abatement that would occur if the WEC were set at the EPA’s Social Cost of CH4 using the three near-term Ramsey discount rates. Calculate the social cost of the methane mitigation, the tax revenue (which is an economic transfer rather than a social cost), and the social benefits associated with these four possible WEC values. a. (2 points) Draw a graph with the marginal abatement curve as we have done in class. Put dollars (in 2019$) on the Y-axis and methane emissions (in thousands of metric tons) on the X-axis. Draw a downward sloping, linear marginal abatement curve, from some y-intercept near the top of the y-axis to an x-intercept near the right side of the x-axis, and label this curve “MAC.” b. (2 points) Draw the congressionally mandated methane tax rate. Draw a horizontal, straight line starting near the bottom quarter on the y-axis, and extend this line the same length as the x-axis. Label this line as “WEC.” This line will represent the congressionally-mandated WEC. c. (2 points) Draw EPA’s estimates of the social cost of methane emissions at the three near-term Ramsey discount rates of 1.5%, 2.0%, and 2.5%. Draw three more horizontal lines above the WEC, each one progressively higher than the previous one, but all of them below the y-intercept for the MAC line. Label these three progressively higher curves, “SC-GHG 2.5%,” “SC-GHG 2.0%,” and “SC-GHG 1.5%,” respectively. These lines represent the EPA’s Social Cost estimate of the social damages from methane emissions, depending on the near-term Ramsey discount rates. These would also be EPA’s estimates of the benefits of reducing methane emissions. d. (2 points) Label the methane emissions that would occur if the WEC were implemented at the congressionally mandated value. On the x-axis, indicate the amount of methane emissions that would be expected to occur with the WEC. e. (5 points) Using what you have learned in class, label the areas of the graph representing the abatement cost associated with a methane fee equal to the MAC, the tax revenue, and the benefits associated with this reduction if the benefits were equal to the WEC and the EPA’s the social cost of methane emissions at the three near-term Ramsey discount rates. Using letters (e.g., A, B, C, etc.), label the areas in your figure. Then report the area (e.g. A) or sum of areas (e.g., A+B) that represent the following values. i. The total cost of abatement for the methane emission reductions that would occur if the WEC were implemented. ii. The tax revenue that would be expected from the WEC. iii. The benefits of this emission reduction assuming the benefits could be measured using three different values. 1. Benefits per ton are equal to the SC-GHG 2.5% value. 2. Benefits per ton are equal to the SC-GHG 2.0% value. 3. Benefits per ton are equal to the SC-GHG 1.5% value. f. (2 points) Label the methane emission that would occur if the methane fee were set equal to the EPA’s Social Cost of CH4 using each of the three near-term Ramsey discount rates. On the x-axis, indicate the amount of methane emissions that would be expected to occur with if the methane fee has had been set at the Social Cost of CH4 using each of the three near-term Ramsey discount rates of 1.5%, 2.0%, and 2.5%. g. (5 points) Label the areas of the graph representing the abatement cost, the tax revenue, and the benefits if the methane fee were set equal to the EPA’s Social Cost of CH4 using each of the three near-term Ramsey discount rates. Use letters that you used to label your graph in part 2.e. (Add more letters if you need them.) For each of the SC-GHG values (at the near-term Ramsey discount rates of 1.5%, 2.0%, and 2.5%), report the area (e.g. A) or sum of areas (e.g., A+B) that represents the following values. i. The total cost of abatement for the methane emission reductions. ii. The tax revenue that would be expected. iii. The benefits of this emission reduction, assuming that the benefits are only measured at the SC-GHG value for the discount rate being evaluated. Note that unlike part 2.e, where you had three benefits for one methane tax (equal to the congressionally mandated WEC), here you will have one benefits measure for each SC-GHG value. In other words, when evaluating the SC-GHG 2.5%, you will report the total cost of abatement, the tax revenue, and the benefits assuming that the SC-GHG 2.5% value is used consistently for the methane fee and the benefits measure. You will not use the benefits for the SC-GHG 2.0% and SC-GHG 1.5% when analyzing the SC-GHG 2.5% value. Similarly, you will evaluate the SC-GHG 2.0% and SC-GHG 1.5% values assuming that they are used consistently for the methane fee and the benefits measure, each with only one benefits measure. (If you get confused, send us a message or ask in the discussion section for this assignment.) 3. (35 points) Re-draw your marginal abatement curve graph from part 2 with values for 2026 and calculate the areas that you specified in parts 2.e and 2.g. a. (4 points) Obtain the necessary data from the 2024 Regulatory Impact Analysis of the Waste Emissions Charge and report the values for 2026 and report that data here. i. From Table 1-5, report the following. 1. WEC Payments in Policy Scenario (million nominal $). 2. WEC Payments in Policy Scenario (million 2019$). ii. From Table 5-10, report the value in 2026 for the following. 1. Net Methane Emissions Subject to WEC in Baseline (thousand metric tons). 2. Net Methane Emissions Subject to WEC in Policy Scenario (thousand metric tons). 3. Charge Specified by Congress (nominal $ per metric ton) iii. From Table 5-9 report the value in 2026 for Total Technical Abatement Potential (kt). iv. From Table 6-1, report the value in 2026 for the Estimates of the Social Cost of CH4, (in 2019$ per metric ton CH4) for the three Near-Term Ramsey Discount Rates. b. (2 points) Convert the Charge Specified by Congress from nominal $ per metric ton to 2019$. Using the data for the WEC Payments in Policy Scenario that you extracted from Table 1-5, create a deflator for 2026 that can be used to convert nominal dollars to 2019 dollars. Use this deflator to convert the 2026 Charge Specified by Congress that you extracted from Table 5-10 from nominal $ per metric ton to 2019$ per metric ton. Report the Charge Specified by Congress (2019$ per metric ton) for 2026 here. c. (5 points) Calculate the Net Methane Emissions Subject to WEC in a Policy Scenario in which the methane fee was set equal to the EPA’s Social Cost of CH4 using each of the three near-term Ramsey discount rates (1.5%, 2.0%, and 2.5%). If the methane fee has been set using one of the EPA’s Social Cost of CH4 values, then abatement would occur to point where the marginal cost of abatement is equal to the appropriate SC-GHG value. You can find this abatement level by solving for the marginal abatement curve and setting it equal to the appropriate SC-GHG value. Note that your graph has price on the y-axis and quantity on the x-axis, so, technically, it is an inverse marginal abatement cost curve. The formula for the marginal abatement cost curve can be found as follows i. The intercept of the of the marginal abatement curve is where your MAC curve intersects the x-axis, which is the Net Methane Emissions Subject to WEC in Baseline from Table 5-10. ii. The slope of the marginal abatement curve can be calculated as “rise over run” using the values you extracted from Table 5-10, but the “rise” is the change in net emissions between the policy scenario and the baseline, and the “run” is the WEC charge specified by Congress (in 2019$). Report the formula for your marginal abatement curve here (and show your work) and report the net methane emissions subject to WEC assuming that the fee was set equal to the EPA’s Social Cost of CH4 using each of the three near-term Ramsey discount rates (1.5%, 2.0%, and 2.5%). Note, the net methane emissions subject to the WEC cannot be less than zero! d. (5 points) Calculate the reductions in methane emissions due to mitigation in 2026 for your four cases (the congressionally mandated WEC value, and a methane fee equal to the EPA’s SC-GHG at the three discount rates) and confirm that these reductions do not exceed the total technical abatement potential for that year. Report the adjusted reductions in methane emissions in 2026 for these scenarios. The reductions in methane emission due to mitigation are calculated at as the Net Methane Emissions Subject to WEC in Baseline less the Net Methane Emissions Subject to WEC in Policy Scenario that you parts 3.a and 3.c. Compare this value to the Total Technical Abatement Potential from Table 5-9. Confirm that the calculated emission reduction does not exceed the technical potential and report the adjusted reduction for 2026 for each scenario here. e. (4 points) Re-draw your marginal abatement curve graph from part 2 with the 2026 values for the following variables added to the appropriate axis. i. Net Methane Emissions Subject to WEC in Baseline. ii. Net Methane Emissions Subject to WEC in Policy Scenario for your four cases (the congressionally mandated WEC value, and a methane fee equal to the EPA’s SC-GHG at the three discount rate). iii. The WEC Charge Specified by Congress (2019$ per metric ton). iv. The Estimates of the Social Cost of CH4, (in 2019$ per metric ton) for the three Near-Term Ramsey Discount Rates (1.5%, 2.0%, and 2.5%). f. (5 points) Using this graph and values, calculate the areas that you identified in part 2.e. i. The total cost of abatement for the methane emission reductions that would occur if the WEC were implemented. ii. The tax revenue that would be expected from the WEC. iii. The benefits of this emission reduction assuming the benefits are measured using three different values. 1. Benefits per ton are equal to the SC-GHG 2.5% value. 2. Benefits per ton are equal to the SC-GHG 2.0% value. 3. Benefits per ton are equal to the SC-GHG 1.5% value. g. (5 points) Using this graph and values and assuming that the methane fee was set equal to the EPA’s Social Cost of CH4 using each of the three near-term Ramsey discount rates, calculate the areas that you identified in part 2.g. i. The total cost of abatement for the methane emission reductions. ii. The tax revenue that would be expected. iii. The benefits of this emission reduction. h. (5 points) Report the net benefits (benefits minus costs) from mitigation for 2026 under the following scenarios. i. The congressionally mandated WEC and benefits valued using the SC-GHG 2.5% value. ii. The congressionally mandated WEC and benefits valued using the SC-GHG 2.0% value. iii. The congressionally mandated WEC and benefits valued using the SC-GHG 1.5% value. iv. A methane fee based on the SC-GHG 2.5% value and benefits values using the SC-GHG 2.5% value. v. A methane fee based on the SC-GHG 2.0% value and benefits values using the SC-GHG 2.0% value. vi. A methane fee based on the SC-GHG 1.5% value and benefits values using the SC-GHG 1.5% value.
Ecn 496 economic honor thesis Title: The Impact of Prize Pool Distribution on Player Effort and Team Performance in CS Esports Tournaments Introduction The growth of esports has significantly reshaped the landscape of competitive gaming, turning it into a billion-dollar industry. One of the core components that motivate participants in esports tournaments is the structure of the prize pool. Understanding how different prize pool distributions affect player effort and team performance is crucial for stakeholders like tournament organizers and sponsors. The design of the prize pool could maximize not only player engagement but also viewer satisfaction, thereby enhancing the economic sustainability of esports. My research focuses on the question: How does prize pool distribution (X) affect player effort and team performance (Y) in CS esports tournaments? This question aims to uncover the influence of financial incentives on performance within an industry where prize pool structures can vary substantially, impacting individual and team motivations. This study could provide valuable insights into designing optimal incentive mechanisms that encourage higher levels of effort and engagement in a dynamic, high-frequency competition like esports. Literature Review This research builds upon several foundational papers in tournament theory and incentive structures. Lazear and Rosen (1981) provided one of the earliest and most influential examinations of tournament theory, demonstrating that larger prize differences can drive increased participant effort. This finding is highly relevant to esports, where prize pool distributions vary significantly between tournaments. Applying their theory to the esports context allows us to examine whether larger financial incentives translate into greater player effort and improved team performance. Ehrenberg and Bognanno (1990) examined the role of financial incentives in golf tournaments, finding that larger prizes led to better performance. This paper provides a useful comparison foresports, as both golf and esports involve individual and team dynamics influenced by financial rewards. By extending Ehrenberg and Bognanno's analysis to the context of esports, this study will explore whether similar incentive effects are present in high-frequency, short-duration esports tournaments. The study by "AI-enabled prediction of video game player performance using the data from sensors" (2022), presents an artificial intelligence solution for predicting esports player performance using sensor data. By collecting physiological, environmental, and smart chair data from both professional and amateur players, the study assesses in-game performance through a recurrent neural network, offering insights into factors influencing player outcomes. This research provides a technological angle on how various factors impact player performance, complementing the analysis of prize pool incentives. Another recent study, "Fighting fair: community perspectives on the fairness of performance enhancers in esports" (2024) explores the competitive gaming community's opinions on various performance enhancers and their potential impact on esports. Through qualitative and quantitative surveys, the research identifies key themes in how players rationalize their views on fairness and performance enhancement. This perspective helps to understand how fairness considerations can influence player behavior. and engagement in esports tournaments. Finally, The research titled "Performance analysis in esports: part 1" (2024) focuses on the validity and reliability of match statistics and notational analysis in "League of Legends." It aims to objectively capture aspects of athlete performances to inform. coaching, highlighting the emerging expertise domain within esports and the limited performance analysis currently available. This study underscores the importance of accurate performance metrics in understanding player effort and outcomes, which directly ties into how prize pool incentives can be effectively analyzed. Data The analysis will utilize data from multiple esports databases, including HLTV.org, Liquipedia, and Esports Earnings. These sources provide detailed information on CS tournaments, such as prize pool breakdowns, player and team statistics, and match outcomes. The sample population comprises professional CS players and teams who have participated in tournaments from approximately 2015 to the present. Key variables for this analysis include the structure of the prize pool (total pool and payout format,such as winner-takes-all vs. tiered distributions),team performance metrics (e.g., win-loss records, rankings, and standings), and individual player statistics (e.g., kills, deaths, damage per round). The data can be accessed through public APIs or web scraping, ensuring a comprehensive and timely dataset for the analysis. Empirical Strategy To evaluate the causal impact of prize pool distribution on player effort and team performance, I will employ a difference-in-differences (DiD) approach. This method will compare changes in effort and performance metrics across tournaments before and after changes in prize pool structure, while controlling for confounding variables. The specific equation to be estimated is: Individual Yirt = α + β × Treatment + Xᵢ + δrt + εᵢᵣ ● Yirt: Outcome variable representing player or team performance for team i at time t. ● Treatment: Indicator variable for tournaments with altered prize pool distribution (e.g., tiered vs. winner-takes-all). ● Xᵢᵣ: Control variables, such as historical performance, player rank, and team composition. ● δᵣ: Time fixed effects to control for time-specific variations. ● β: The key coefficient of interest, representing the effect of prize distribution structure on performance outcomes. Team Level: Yᵣgt = αᵣgt + β × Treatment + Ɣt + ωg + εᵣgt ● Yrgt: Outcome variable representing team performance for team g in tournament r attime t. ● β: The key coefficient of interest, representing the effect of prize distribution structure on performance outcomes. ● Treatment: Indicator variable for tournaments with altered prize pool distribution (e.g., tiered vs. winner-takes-all). ● Ɣt : Time fixed effects to control for time-specific variations. ● ωg : Team fixed effects to control for team-specific characteristics. ● εᵣgt: Error term capturing any unobserved factors affecting performance. The DiD approach is particularly suited to this research as it helps isolate the effect of prize pool changes by comparing performance between treated and untreated groups over time. To address potential endogeneity issues, robustness checks will be conducted, including instrumental variable (IV) methods, such as using tournament size as an instrument. This empirical strategy will provide robust evidence on how financial incentives impact effort and performance in esports, which can offer valuable guidance to tournament organizers in optimizing prize pool structures. Conclusion This research proposal seeks to explore the intricate relationship between prize pool distribution and player effort in the esports industry. By drawing on established economic theories and utilizing high-quality tournament data, this study aims to contribute to both the esports and economics literature, offering actionable insights for tournament organizers and stakeholders in this rapidly growing industry.
SUMMATIVE ASSIGNMENT Overall word limit: 2000 words SUBMISSION INSTRUCTIONS Your completed assignment must be uploaded to Ultra no later than 02 May 2025 12 midday A penalty will be applied for work uploaded after 12:00 midday as detailed in the Student Information Hub. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated. Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font. Assignments and dissertations/business projects must not exceed the word count indicated in the module handbook/assessment brief. The word count should: · Include all the text, including title, preface, introduction, in-text citations, quotations, footnotes and any other items not specifically excluded below. · Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count. Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be clearly over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) (x) of the General Regulations. Very occasionally it may be appropriate to present, in an appendix, material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness. Any appendices will not have a role in the assessment - examiners are under no obligation to read appendices and they do not form. part of the word count. Material that students wish to be assessed should always be included in the main body of the text. Guidance on referencing can be found on Durham University website and in the Student Information Hub. MARKING GUIDELINES Performance in the summative assessment for this module is judged against the following criteria: · Relevance to question(s) · Organisation, structure and presentation · Depth of understanding · Analysis and discussion · Use of sources and referencing · Overall conclusions ECON1181 Mastering Data and Computation Individual Project Question 1 1. (a) Explain the code below on two levels: – Explain each code block e.g., the first block recalls the relevant libraries. (40 marks) – Explain the overall purpose of the code (20 marks) (b) Furthermore, please make corrections if you run an error when executing the code. (10 marks) (c) Finally identify and discuss the improvements to the code. (30 marks) library(tidyverse) library(lubridate) library(scales) library(zoo) create_sample_data % ungroup() } impact_factors % mutate( spending_ma = rollmean(consumer_spending, k = window_size, fill = NA), employment_ma = rollmean(employment_rate, k = window_size, fill = NA), revenue_ma = rollmean(business_revenue, k = window_size, fill = NA) ) %>% ungroup() } calculate_yoy_changes % group_by(income_group) %>% arrange(date) %>% mutate( spending_yoy = (consumer_spending / lag(consumer_spending, 365) - 1) * 100, employment_yoy = (employment_rate / lag(employment_rate, 365) - 1) * 100, revenue_yoy = (business_revenue / lag(business_revenue, 365) - 1) * 100 ) %>% ungroup() } plot_indicator
Department of Electrical and Electronic Engineering EIE2105 Digital and Computer Systems Tutorial 4: Sequential Logic I Q1. What is the difference between synchronous counter and ripple counter? Q2. What is the problem of S-R latch? How can D latch solve this problem? Q3. What is the latch timing problem? Explain using an example and a wave/timing diagram. Q4. Complete the following timing diagram if the positive-edge triggered T flip-flop is simulated. You can assume gate delays are zero. Q5. a. Which of the following circuits is/are master-slave/edge-triggered flip-flop? b. Complete the following timing diagram if circuit A is simulated. You can assume gate delays are zero. c. Complete the following wave/timing diagram if circuit B is simulated. You can assume gate delays are zero. d. As compared with master-slave S-R flip-flops, what is the advantage of edge triggered D flip-flops on how its output reacts to the input?
Bloomberg Session 1 Digital Banking and Fintech (N1632) Practical Exercises: 1.Firstly, you get familiar with the instruction of the use of remote Bloomberg Terminal. Please use the above links. Functions and reactivations Instructions and rules on using the lab The access rules and the booking instructions are available via this link 2. Secondly, you will be expected to complete the following exercises during the workshop, please download the necessary data for Barclays Bank (BARC:LN). Type the FA, RV, and SPLC functions. Finally, ensure you can export the processed data into an Excel file for discussion during the workshop 3. Thirdly, conduct an analysis of the FTSE 100 market index and the behavioral changes of UK firms in response to Brexit. Obtain FTSE 100 data: Type "ftse100" in the search bar. Type "GP" and click on the chart content. Add "BAR (Equity, London)" to the chart. Select "MAX" on the top of the graph. 4. Examine the Barclays 2019 Annual Report to identify the trends in gross profit and comprehensive income over the preceding two years. Assess whether the report provides any indications of how the COVID-19 pandemic might have affected the company's financial position. Consider utilizing data science (DS) functions for analysis.
International Year One in Business INU1111 Quantitative Methods Summative Assignment Assignment Part B: Data Analysis Report (75% of final mark) Distribution and Submission Details Due Date: Assignment Part B: - Due Monday 31st March 2025 (9.00am UK time) Submission through Turnitin. Please Note: Ø Late or plagiarised assignments will be penalised, marks will be deducted for late submission. Assignment guidelines: · Using the Assignment Part B guidance on Canvas continue with the data analysis of your sample of data. Your written work in Assignment Part A forms part of your final Assignment Part B report. · Maximum report size 16 sides of A4, maximum word count 3000 words. This includes your graphs, but excludes the front page, contents page, reference list and appendix. Assignment Part B: 75% of the module mark. In the second larger part of the assignment, you will analyse your data in depth using a variety of methods. This analysis must include the following sections. 1.0 Introduction Use the introduction you wrote for Assignment Part A as the basis for this section. Make any amendments that were mentioned in the feedback for Part A. 2.0 Sample Method Provided a detailed explanation of your sample method. Use the sample method you wrote for Assignment Part A as the basis for this section. Include any adjustments you have made (for example, if you have since added to or changed your data based on feedback from Part A). As a reminder, your sample method should discuss. · The sampling methodology you have used. · Your sample sizes per group. · How you have controlled your sample (e.g., only used certain locations or video types) · Any limitations in the data that may influence the results. 3.0 Initial analysis of your data · This is the first part of your analysis. The purpose of this section is to look at your two numerical variables individually. · Use the descriptive statistics and graph that you created for ‘The Number of Views’ in Assignment Part A as a starting point. Then repeat this process by creating a second set of descriptive statistics and graph for your other numerical variable. · Include a comparison between two groups (for example, comparing locations or video types). 4.0 Regression and Correlation Analysis · Use simple linear regression and correlation analysis with accompanying graphs to analyse the relationship between your key variables. Discuss your regression equation. · Test your regression equation and interpret the results. · Explain whether your independent variable is a good predictor for your dependent variable. Consider the correlation (r) and the coefficient of determination (the R-square value). If the independent variable does not explain 100% of the variation in the dependent variable suggest reasons why this could be the case. Important Guidance: use a simple regression model in this section, do not use a Multiple Regression Model. The aim here is to investigate a relationship between two variables whilst identifying and discussing the problems, it is not to find the best model. 5.0 Further Analysis Carry out the hypothesis test assigned to you in Semester 2 Week 7. 6.0 Conclusion · Summarise your findings from each of the three analysis sections (Initial Analysis, Regression and Further Analysis) 7.0 References Include a range of references. These should be in the Harvard Reference format. Each reference should feature in the report as a citation and each citation must have an accompanying reference. 8.0 Appendix Include a copy of your raw data as a table in an appendix at the end of the report. Marking Breakdown for the Assignment Sections · Introduction to the topic and explaining the sample selection process in detail (10 marks) · Initial analysis of your data using summary statistics and graphs (30 marks) · Calculating the regression equation and correlation coefficient. Discussing the regression equation. Making appropriate comments about the regression and correlation results. Testing the equation and discussing the validity of the regression equation (30 marks). · Hypothesis test (25 marks). · Conclusion discussion (5 marks) Please refer to the Quantitative Methods module handbook for a breakdown of the grade descriptors (what is required to reach each marking band).
Java Lab 20: More I/O This lab practices writing and reading objects, plus a few miscellaneous nio methods. 1. Create project Lab20. Download Lab20.zip; unzip it and copy the class into this project: the Cargo class and its children, the CargoSizeable interface, the CargoEnum, CargoContainer, and Lab20Main, which contains the main program. The Cargo class already implements CargoSizeable; make it also implement Serializable. 2. Add the method public ArrayList CargoContainer.getCargoList( ), that returns its ArrayList (breaks encapsulation). 3. Add a new class named CargoFileOperations with String data member filename and the following methods: - overloaded constructor that sets filename - public void writeList(ArrayList). This should declare an ObjectOutputStream that wraps a FileOutputStream; use a try-catch block here. It should write out, as objects, all of the ArrayList, using filename as the parameter to FileOutputStream's constructor and close the file at the end. Each ObjectOutputStream method used should have its own try-catch block with their own error messages (like “Output file failed to open”), so that you know which thing failed (that is, don't use one try-catch block for the whole thing), so print a message in the catch that tells what went wrong and then exit. - public ArrayList readList( ). This should declare an ArrayList, initially empty. Declare an ObjectInputStream and a FileInputStream as separate variables. Wrap the second inside the first. Write a while loop that reads from filename, checking the FileInputStream variable's available( ) > 0 to quit the loop; close the file at the end. Use try-catch blocks – one around the new’ing of the two streams, and one around the while loop. Don't forget to cast the objects read back to type Cargo. Return the ArrayList. 4. In the main program, after the call to cargoContainer.display(), create a CargoFileOperations object; write out the Cargo items using its writeList( ) method. Then read them back using its readList( ) method, and display the returned objects. You should verify that CargoContainer's display method showed the same data. 5. Add a public void display( ) method to CargoFileOperations. In it, declare a path object using Paths.get( ) on its filename and display) its toString(), absolute path, and getRoot. Declare a File object and display isDirectory( ) and getAbsolutePath( ). Finally, display the return values of these Files methods on the Path variable: isExecutable( ), isReadable(), isWriteable(). Make sure each display has a simple label, as in "isExecutable() returns true". Call display() at the end of main.
AcF633 - Python Programming for Data Analysis Group Project 20th February 2025 noon/12pm to 6th March 2025 noon/12pm (UK time) This assignment contains one question worth 100 marks and constitutes 35% of the total marks for this course. You are required to submit to Moodle a SINGLE .zip folder containing a SINGLE Jupyter Notebook .ipynb file (preferred) and/or Python script. .py files and supporting .csv files (e.g. input data files, if any), together with a signed group coversheet. The name of this folder MUST be your group number (e.g. Group1.zip, where Group 1 is your group). In your main script, either Jupyter Notebook .ipynb file or Python .py file, you do not have to retype the question for each task. However, you must clearly label which task (e.g. 1.1, 1.2, etc) your subsequent code is related to, either by using a markdown cell (for .ipynb files) or by using the comments (e.g. #1.1 or ‘‘‘1.1’’’ for .py files). Provide only ONE answer to each task. If you have more than one method to answer a task, choose one that you think is best and most efficient. If multiple answers are provided for a task, only the first answer will be marked. Only ONE of the group members is required to submit the work for your group. Your submission .zip folder MUST be submitted electronically via Moodle by the 6th March 2025 noon/12pm (UK time). Email submissions will NOT be considered. If you have any issues with uploading and submitting your group work to Moodle, please email Carole Holroyd at [email protected] BEFORE the deadline for assistance with your submission. Please ensure that ALL members of the group who have contributed to the group work have signed the group coursework coversheet before your coursework is submit-ted. Group members who have not signed the coversheet are deemed to have not contributed to the group submission and will be awarded a zero mark. This assignment is AI Assessment AMBER (i.e. Generative AI tools can be used in an assistive role). Please refer to the University position page: University position on Artificial Intelligence for more details about AI Assessment RAG categories. If you use AI to assist your work, you are required to submit an AI appendix. The following penalties will be applied to all coursework that is submitted after the specified submission date: Up to 3 days late - deduction of 10 marks Beyond 3 days late - no marks awarded Every student is required to submit a peer evaluation form. electronically via Moodle to evaluate their group members’ participation and contribution to this empirical assign-ment. Deadline for peer evaluation submission is 7th March 2025 noon/12pm (UK time), i.e. one day after the Group Project’s deadline. Good Luck! Question 1: The S&P100 index is a market capitalization-weighted index of 100 leading stocks listed in the US stock exchanges. The csv data file ‘SP100-Feb2023.csv’ lists the constituents of the S&P100 index as of February 2023 with the following information: ❼ Ticker: Company’s stock symbol or ticker ❼ Company: Company’s name ❼ Sector: Sector in which the company belongs ❼ Market Value: Company’s market capitalization Import the data file to an object called “Index” in Python and perform. the following tasks. Task 1: Descriptive Analysis of S&P100 index (Σ = 25 marks) 1.1: How many unique sectors are there in the S&P100 index? Print the following statement: ‘There are ... unique sectors in the S&P100 index, namely ...’, where the first ‘...’ is the number of unique sectors, and the second ‘...’ contains the names of the sectors alphabetically ordered and separated by commas. (2 marks) 1.2: Write code to create a dictionary with keys being the unique sectors in the S&P100 index sorted in alphabetical order, and values being lists of tickers, also alphabetically ordered, in each sector. Hint: An example of a key-value pair of the required dictionary is ‘Materials’: [‘DOW’, ‘LIN’]. (3 marks) 1.3: Modify code in Task 1.2 to create a dictionary with keys being the unique, alphabetically sorted sectors in the SP100 index, and values being tuples of two elements: the first being the number of tickers in each sector, and the second being the list of alphabetically ordered tickers in each sector. Hint: An example of a key-value pair of the required dictionary is ‘Materials’: (2,[‘DOW’, ‘LIN’]). (3 marks) 1.4: Add a column called “Weighting” to the “Index” object that computes the weight (in percentages, rounded to 3 decimal places) of each company in the S&P100 index. (3 marks) 1.5: Write code to find the company having the largest index weight and the one with the smallest weight. Print the following statements: Company ... (ticker ..., sector ...) has the largest index weight of ...%. Company ... (ticker ..., sector ...) has the smallest index weight of ...%. The range of the weights is ...%. (3 marks) 1.6: Write code to produce the following pie chart that shows the S&P100 index weighting by sectors. Only show the percentage weighting labels for sectors whose weights are larger than 1%. Print the following statement: Sector ... has the largest index weight of ...%, and Sector has the smallest index weight of ...%. (6 marks) 1.7: Write code to classify the S&P100 companies into 3 groups: “Large cap” when market value ≥ 100m, “Mid cap” when 100m > market value ≥ 30m, and “Small cap” when market value < 30m. Add a column called “Group” to the “Index” object to capture this grouping information. (2 marks) 1.8: Write code to produce the following pie chart that shows the S&P100 index weighting by company grouping. (3 marks) Task 2: Portfolio Allocation (Σ = 30 marks) 2.1: Using your group number (e.g. 1, 2, 3, etc.) as a random seed, draw a random sample of 5 stocks (i.e. tickers) from the S&P100 index excluding stocks ABBV, AVGO, CHTR, DOW, GM, KHC, META, PYPL and TSLA.1 Sort the stocks in alphabetical order, and then import daily Adjusted Close (Adj Close) prices for the 5 stocks between 01/01/2009 and 31/12/2024 from Yahoo Finance. Compute the simple daily returns for the stocks and drop days with NaN returns. (3 marks) 2.2: Create a data frame. to summarize key statistics (including sample size, mean, standard deviation, minimum, quartiles, maximum, skewness, kurtosis, Jarque-Bera statistic, Jarque-Bera pvalue and Normality) for the daily returns of the five stocks over the above sample period. Jarque-Bera statistic is the statistic for the Jarque-Bera normality test that has the formula where T is the sample size, Sb and Kb are sample skewness and kurtosis of data, respectively. Under the null hypothesis that data is normally distributed, the JB statistic follows a χ 2 distribution with 2 degrees of freedom. Jarque-Bera pvalue is the pvalue of the JB statistic under this χ 2 distribution. Normality captures the conclusion of the Jarque-Bera test - whether daily returns of a stock are normally distributed (Yes) or not (No), based on a 5% significance level. Your data frame. should look similar to the one below, but for the five stocks in your sample. (4 marks) 2.3: Using and/or modifying function get_efficient frontier() from the file Eff_Frontier_functions.py on Moodle, construct and plot the Efficient Frontier for the five stocks based on optimization based on data over the above period. In your code, define an equally spaced range of expected portfolio return targets with 2000 data points. Mark and label the locations of the five stocks in the Efficient Frontier plot. Also mark and label the locations of the Global Minimum Variance portfolio and the portfolio with the largest Sharpe ratio, assuming the annualized risk-free rate is 0.01 (or 1%). (5 marks) 2.4: What are the return, volatility, Sharpe ratio and stock weights of the portfolio with the largest Sharpe ratio? Write code to answer the question and store the result in a Pandas Series object called LSR port capturing the above statistics in percentages. Use the words ‘return’, ‘volatility’, ‘Sharpe ratio’, and stock tickers (in alphabetical order) to set the index of LSR port. (3 marks) 2.5: Paul, a mean-variance optimizer, is interested in the five stocks in your sample. He has an expected utility function of the form. U(Rp) = E(Rp) − 0.5Aσp 2 , where Rp and σp 2 are respectively the return and variance of the portfolio p, and A is Paul’s risk-aversion coefficient. Assume A = 4. Also assume that Paul does not have access to a risk-free asset (i.e. he cannot lend or borrow money at the riskfree rate) and he would like to invest all of his wealth in the five stocks in your sample. How much, in percentages of his wealth, should Paul invest in each of the stocks in your sample to maximize his expected utility? Write code to answer the question and store the result in a Pandas Series object called Paul port respectively capturing the return, volatility, Sharpe ratio and the stock weights of Paul’s portfolio. Set the index of Paul port correspondingly as in Task 2.4. (4 marks) 2.6: Paul is interested in knowing how the expected return of his optimal (i.e. maximum-utility) portfolio changes with his risk-aversion level A, when he does not have access to a risk-free asset. Write code to produce a similar plot to the following for your stock sample, setting A between 0 and 10. What can you conclude from the plot? (5 marks) 2.7: Now suppose that Paul has access to a risk-free asset and can borrow and lend money at the risk-free rate. In this case, he will choose the efficient portfolio with the largest Sharpe ratio in Task 2.4 as his optimal risky portfolio and will divide his wealth between this optimal portfolio and the risk-free asset to maximize his expected utility. He could also borrow money (i.e. have a negative weight on the risk-free asset, which is assumed to be capped at -100%; that is, the maximum amount that he can borrow is equal to his wealth) to invest more in the risky assets. What will be his portfolio compositions in this case, assuming that his risk-aversion coefficient A = 4? Write code to answer the question and store the result in a Pandas Series object called Paul port rf respectively capturing the return, volatility, Sharpe ratio, the stock weights and risk-free asset weight of Paul’s portfolio. Set the index of Paul port rf correspondingly. (6 marks) Task 3: Factor models (Σ = 25 marks) 3.1: Denote P be the portfolio formed by combining the five stocks in your sample using equal weights. Compute the daily returns of the portfolio P over the considered time period from 01/01/2009 to 31/12/2024. (3 marks) 3.2: Using data from Kenneth R. French website, estimate a Carhart four-factor model for portfolio P over the above period. Test if portfolio P possesses any abnormal returns that cannot be explained by the factor model. (4 marks) 3.3: Conduct the White test for the absence of heteroskedasticity in the residuals of the above factor model and draw your conclusion using a 5% significance level. (3 marks) 3.4: Conduct the Breusch-Godfrey test for the absence of serial correlation up to order 10 in the residuals of the above factor model and draw your conclusion using a 5% significance level. (3 marks) 3.5: Based on results in the above two tasks, update the Carhart four-factor regression model and re-assess your conclusion on the pricing of portfolio P according to the factor model in Task 3.2. (3 marks) 3.6: Compute the 4-year rolling window β estimates of the Carhart four factors for portfolio P over the sample period. That is, for each day, we compute β loadings for the three factors using the past 4-year data (including data on that day). Plot a 2×2 subplot figure similar to the following for your stock sample, showing the rolling window β estimates of the factors, together with 95% confidence bands. Provide brief comments. (9 marks) Task 4: (Σ = 20 marks) These marks will go to programs that are well structured, intuitive to use (i.e. provide sufficient comments for me to follow and are straightforward for me to run your code), generalisable (i.e. they can be applied to different sets of stocks, different utility functions for Paul with minimal adjustments/changes to the code) and elegant (i.e. code is neat and shows some degree of efficiency).
Single Use Plastics and Home Recycling (15 Points) Part I Single Use Plastics 1. (0.5 pt) What is the definition of a single-use plastic? 2. (0.5 pt) What are some common single-use plastic items? 3. (1 pt) What happens to plastics over time? 4. (1 pt) Which country produced the most plastic waste per person in 2023 (i.e. per capita)? 5. (1 pt) What is recycling? 6. (1 pt) What are the benefits of recycling? 7. (1 pt) When is recycling not a good strategy? 8. (1 pt) What are 5 single-use plastic items that you use? 9. (2 pts) Use the internet to search out alternatives to these single-use plastics. A. Bottles of Shampoo B. Plastic grocery store bags C. Plastic Straws D. Bottled water E. Bread bags F. Dish Soap G. Cleaning Products H. Soda in Plastic Bottles I. Yogurt containers Part II: Home Recycling Many people do not know exactly what can and cannot go in their curbside recycling. In this section, you will figure out what is acceptable in your recycling bin. 1. What City or Township do you live in? _________________________________ 2. (1 pt) Find the website for your community’s curbside recycling or the drop-off station you identified in Question 1. For a City or Township, I recommend searching for “City of _____________ Recycling”. Copy and paste your city’s or drop-off station’s recycling website here. 3. (4 pts) Below is a list of commonly recycled items. Use the website you found in Question 2 to determine you can recycle these in your curbside recycling or at your drop-off station. To determine what can be recycled at McMaster, visit the https://facilities.mcmaster.ca/sustainability/waste/. Answer yes/no. Item Type Recyclable at your Home? Recyclable at McMaster? Plastics #1 Plastics #2 Plastics #3 Plastics #4 Plastics #5 Plastics #6 Plastics #7 Plastic Bags Glass Metal (Tin, Aluminum, Steel) Paper and Newspaper Pizza Boxes Concrete 4. (1 pt) Search Google. What is wishful recycling? Why does it do more harm than good?
EMET8002 Case Studies in Applied Economic Analysis and Econometrics Semester 1 2025 Computer Lab in Week 3 Question 1: Simple Linear Regression Download the “states” data from Wattle and open it in Stata. As part of this question we explore the relationship between SAT (Scholastic Assessment Test) scores and the per pupil expenditure in primary and secondary school, in the U.S. on a state level. (a) Describe the variables of interest (the SAT score, coded as “csat” and education expense, coded as “expense”) individually as well as their correlations and a scatterplot. Are there any outliers? (b) Run a simple linear regression model where “csat” is the dependent (outcome) variable and “expense” is the independent (explanatory) variable. Do this with and without accounting for outliers. What changes? Which model do you prefer? (c) Test whether the distribution of the residuals from your regressions in part (b) follows a normal distribution. Does the normality assumption hold? Question 2: Multiple Linear Regression and Quantile Regression We continue working with the “states” dataset. As part of this question we explore the relationship between SAT (Scholastic Assessment Test) scores and the following four variables: (1) Per pupil expenditure in primary and secondary school ("expense"), (2) % High school graduates taking SAT ("percent"), (3) Median household income in $1,000 ("income") and (4) % adults college degree ("college"). The data is provided on a state level for the U.S. (a) Describe the five variables of interest individually as well as their correlations. (b) Run a multiple linear regression model where “csat” is the dependent (outcome) variable and the other four variables are the independent (explanatory) variables. (c) Test whether the distribution of the residuals from your regressions in part (b) follows a normal distribution. Does the normality assumption hold? (d) Instead of running a multiple linear regression which estimates the mean test scores, as in part (b), run quantile regressions to estimate the median, the 10th quantile and the 90th quantile of mean test scores. Use the same dependent and independent variables as in your model from part (b). Question 3: Preparation for the Research Report [not required for problem set] Last week we discussed some aspects of the research report (worth 45% of your final mark) and we now continue the preparation for the report as well as the research proposal. We strongly recommend starting your work on the project as soon as possible. (a) Have a look at the section with the research report on Wattle and discuss the structure of the final research report. (b) What data is required for replicating the papers? What are the data sources? If you need to apply for the data through the Australian Data Archive werecommend to start the process now. (c) As part of the project you are required to replicate and extend one of the papers. First of all, explain in your own words what is meant by replicating the main findings of a paper. (d) Now explain in your own words what is meant by extending the results of a paper. (e) As an example, consider the possible extension to update the data (e.g., using new waves of data). Discuss some ideashow this could be turned into a research question and backed up with economic theory and/or academic literature.
Elec4621 Lab Task 2 This lab aims to cover the concepts of filtering and linear time-invariant systems. It will give you some exposure to the ways these systems are manipulated and allows you to develop some insight into their working. 1 Filters and Impulse Response The first part of the lab looks at filters. In particular you will consider an FIR in order to focus on the concepts of impulse response and transfer function. You will also apply the filter to real data and experiment with the location of its zeroes to achieve a particular response. A fifth order all-zero filter has the following transfer function, where 1. Find the impulse response of the filter. Hint: There are a number of ways to do this: One way is to build a vector, v, containing all 5 zeroes, and then use the Matlab function, 'poly', to find the coefficients of the polynomial with these roots-the coefficients of the polynomial are the filter taps. Another way is to proceed from the second order sections of the transfer function and use polynomial multiplication to build 5-th order numerator. Then you will have the filter taps. 2. Find and plot the magnitude and phase responses of the filter. Hint: You should remember that the frequency response is obtained by substituting z=ejw. Therefore you can think of this as the evaluation of the transfer function on a fine grid of w. Thus. you can construct a vector w of angular frequencies and evaluate the transfer function for this vector, then plot the amplitude and phase of the result. 3. Apply the filter to the sunspot data from the previous lab. What do you observe? 4. Suppose that we want to suppress the high frequency noise of the sunspot data to better see the 11 year and longer term cycles. Experiment with the zeros of the filter above to achieve this. 2 Sampling and Reconstruction This section of the lab deals with the concepts of sampling and reconstruction. In this lab you will focus on these concepts in order to verify and cement your understanding of how these ideas apply. You will employ the view of reconstruction as "joining the dots" in some way to understand how aliasing can be recruited to manipulate signals. Consider the signal x(t) = Acos(wt) where A = 1 and ω = 2πf with f = 110 Hz. In the rest of this part, and unless otherwise specified, you are to plot the signals in the time for 0 ≤t< 30 ms and in the frequency domain for-1000
MATH2110 - STATISTICS 3 SPRING SEMESTER SEMESTER 2025 Coursework 1 Deadline: 3pm, Friday 14/3/2025 Your neat, clearly-legible solutions should be submitted electronically as aJupyter or PDF file via the MATH2110 Moodle page by the deadline indicated there. As this work is assessed, your submission must be entirely your own work (see the University’s policy on Academic Misconduct). Submissions up to five working days late will be subject to a penalty of 5% of the maximum mark per working day. Deadline extensions due to Support Plans and Extenuating Circumstances can be requested according to School and University policies, as applicable to this module. Because of these policies, solutions (where appropriate) and feedback cannot normally be released earlier than 10 working days after the main cohort submission deadline. Please post any academic queries in the corresponding Moodle forum, so that everyone receives the same assistance. As it’s assessed work, I will only be able to answer points of clarification. The work is intended to be approximately equal to a week’s worth of study time on the module for a student who has worked through the module content as intended - including the R aspects. If you have any issues relating to your own personal circumstances, then please email me. THE DATA The objective is to build a predictive model for the median house price in Boston neighbourhoods using various neighbourhood characteristics. Median house price is a crucial indicator for urban planning and economic studies. It is important to understand how different social indicators affect it. To this end, the dataset we will analyse here contains detailed records of 506 neighbourhoods, capturing factors such as crime rates, age of the properties, etc. The training and test data are provided in the files BostonTrain.csv and BostonTest.csv available at the Moodle page. The train file contains observations for 506 neighbourhoods. The target variable is medv, median value of houses in thousands of dollars. The predictors include: • crim, which contains the per capita crime rate by town. • zn, which contains the proportion of residential land. • rm, which contains the average number of rooms per house. • age, which contains the proportion of houses built before 1940. • dis, which contains distances to large employment centres. • ptratio, which contains the student-teacher ratio by town. • lstat, which contains the percentage of lower-status population. The test data is provided in the file BostonTest.csv, containing observations for 102 neighbourhoods. The test data should only be used to evaluate the predictive performance of your models. THE TASKS (a) (80 marks) Using only the training data (BostonTrain.csv), develop one or more models to predict the median house price (medv) based on the predictor variables. You may use any methods covered in this module. For this part, the test data must not be used. Your analysis should include: – Model selection and justification. – Diagnostics to assess the quality of your model(s). – Interpretation of the model parameters. Which parameters seem to have a greater importance for prediction? (b) (20 marks) Use your “best” model(s) from (a) to predict the median house price (medv) for the neighbourhoods in the test dataset (BostonTest.csv). Provide appropriate numerical summaries and plots to evaluate the quality of your predictions. Compare your predictions to those of a simple linear model of the form.: medv ∼ crim. NOTES • An approximate breakdown of marks for part (a) is: exploratory analysis (20 marks), model selection (40 marks), model checking and discussion (20 marks). About half the marks for each are for doing technically correct and relevant things, and half for discussion and interpretation of the output. However, this is only a guide, and the work does not have to be rigidly set out in this manner. There is some natural overlap between these parts, and overall level of presentation and focus of the analysis are also important in the assessment. The above marks are also not indicative of the relative amount of output/discussion needed for each part, it is the quality of what is produced/discussed which matters. • As always, the first step should be to do some exploratory analysis. However, you do not need to go overboard on this. Explore the data yourself, but you only need to report the general picture, plus any findings you think are particularly important. • For the model fitting/selection, you can use any of the frequentist techniques we have covered to investigate potential models - automated methods can be used to narrow down the search, but you can still use hypothesis tests, e.g. if two different automated methods/criteria suggest slightly different models. • Please make use of the help files for R commands. Some functions may require you to change their arguments a little from examples in the notes, or behaviour/output can be controlled by setting optional arguments. • You should check the model assumptions and whether conclusions are materially affected by any influential data points. • The task is deliberately open-ended: as this is a realistic situation with real data, there is not one single correct answer, and different selection methods may suggest different “best” models - this is normal. Your job is to investigate potential models using the information and techniques we have covered. The important point is that you correctly use some of the relevant techniques in a logical and principled manner, and provide a concise but insightful summary of your findings and reasoning. Note however that you do not have to produce a report in a formal “report” format. • You do not need to include all your R output, as you will likely generate lots of output when experimenting. For example, you may look at quite a large number of different plots and you might do lots of experimentation in the model development stage. You only need to report the important plots/output which justify your decisions and conclusions, and whilst there is no word or page limit, an overly-verbose analysis with unnecessary output will detract from the impact.
21-259: Calculus in Three Dimensions Lecture #8 Spring 2025 Partial Derivatives Consider the function z = f (x, y) = x2 + 2y2. By fixing y = 2, we focus our attention to all points on the surface where the y-value is 2, shown in both figures. These points form. a curve in space: z = f (x,2) = x 2 + 8 which is a function of just one variable. We can take the derivative of z with respect to x along this curve and find equations of tangent lines, etc. The key notion to extract from this example is: by treating y as constant (it does not vary) we can consider how z changes with respect to x. In a similar fashion, we can hold x constant and consider how z changes with respect to y. This is the underlying principle of partial derivatives. We state the formal, limit–based definition first, then show how to compute these partial derivatives without directly taking limits. Definition: Let z = f (x, y) be a continuous function on an open set D in R2. 1. The partial derivative of f with respect to x is fx (x, y) = 2. The partial derivative of f with respect to y is f y (x, y) = Alternate notations for fx (x, y) include: with similar notations for f y (x, y). Since partial derivatives are defined using the limit definition, all of the standard rules techniques for computing derivatives of single-variable functions apply. Thus all one has to do when finding a partial derivative is treat all other variables as constants. Example 1. If f (x, y) = x3 + x2 y3 −2y2 , find fx (2,1) and f y (2,1). Example 2. If f (x, y) = find fx and f y . Example 3. If g (x, y, z) = exy lnz, find gx , gy , and gz . Example 4. If z is implicitly defined as a function of x and y via the equation x − z = arctan(yz), find Example 5. Find fx and f y if f (x, y) = x y. Just as higher-order derivatives are found for single-variable functions, they can be found for multivariable functions. However, we now have the idea of mixed partials: Example 6. Compute all second partials of the function f (x, y) = cos(2x2 +3y). Clairaut’s Theorem: Suppose f is defined on a set D that contains the point (a,b). If the function fx y and f y x are both continuous on D, then fx y (a,b) = f y x (a,b). Example 7. If f (r,s,t) = r ln(r s2 t 3 ), find fr ss and fr st. Example 8. How many third-order partial derivatives does a function of two variables have? If all of the partials are continuous everywhere, how many of them are distinct? Definition: A partial differential equation is an equation relating an unknown function of several variables and some of its partial derivatives. Example 9. The equation uxx +uy y = 0 is known as Laplace’s equation. Solutions of this equation are known as harmonic functions. Determine if u(x, y) = e x cos y is a solution of Laplace’s equation. Tangent Planes and Linear Approximations Suppose z = f (x, y) has continuous partial derivatives. An equation of the tangent plane to the surface z = f (x, y) at the point P(x0, y0, z0) is z − z0 = fx (x0, y0)(x − x0)+ f y (x0, y0)(y − y0). Example 10. Find the equation of the plane tangent to z = y lnx at the point (1,4,0). Example 11. Find the equation of the plane tangent to z = arctan(x y2) at the point (1,1,π/4). Definition: The linearization or linear approximation to the function z = f (x, y) at the point (a,b) is given by f (x, y) ≈ L(x, y) = f (a,b)+ fx (a,b)(x − a)+ f y (a,b)(y −b). Example 12. Find the linearization of the function at the point (3,0) and use it to approximate f (3.1,−0.1). Definition: The differential d z, also known as the total differential, is defined by It is an estimate of ∆z, the actual change in z, as x changes from x to x +∆x and y changes from y to y + ∆y by taking d x = ∆x and d y = ∆y. Example 13. Let z = x4 e3y . Find d z. Example 14. A cylindrical steel storage tank is to be built that is 10ft tall and 4ft across in diameter. It is known that the steel will expand/contract with temperature changes; is the overall volume of the tank more sensitive to changes in the diameter or in the height of the tank? What about a tank with a height of 1ft and radius of 5ft?
Department of Accounting and Finance Digital Banking and Fintech (N1632) Seminar 1 1. What is a bank balance sheet? What are the main items in retail and investment bank’s balance sheet? Prepare trend analysis of Barclay’s excel sheet. Bloomberg: FA Hints: Topic 1 -Canvas Module Website Reading related: Chapters 3-5: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on understanding bank structure, functions, and regulations.) Chapters 9-11: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on bank capital, liquidity, and profitability.) 2. What are the main banking earnings and incomes? Prepare trend analysis of Barclay’s excel sheet. Bloomberg: FA Hints: Topic 1 -Canvas Module Website Reading related: Chapters 3-5: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on understanding bank structure, functions, and regulations.) Chapters 9-11: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on bank capital, liquidity, and profitability.) 3. What are the main difference between different types of banking? Hints: Topic 1 -Canvas Module Website Reading related: Chapters 3-5: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on understanding bank structure, functions, and regulations.) Chapters 9-11: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on bank capital, liquidity, and profitability.) 4. What are the main differences between the trading book and the banking book? What are the key challenges and opportunities facing retail banks in the post-pandemic era? Hints: Topic 1 -Canvas Module Website Reading related: Chapters 3-5: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on understanding bank structure, functions, and regulations.) Chapters 9-11: Casu B., Girardone C. & Molyneux P. (2015). Introduction to Banking, 2nd edition. Pearson. (Focus on bank capital, liquidity, and profitability.)
International Financial Management (N1548) Spring term Mid-term Assignment: 1000 w essay Due: week 8 (please refer to Sussex Direct for submission deadline) You are the CEO of Sygma Ltd, a multinational company incorporated in the UK but do business in many emerging countries including Bolivia. In 2011 the central bank of Bolivia decided to adopt a crawling peg system for its currency, the Boliviano, against the USD. As a CEO you are asked to address the following points about the country’s economy and your company’s position: a) Discuss the implications on the country’s economy after the change of the system (50%). b) What strategy should you employ in order to manage the exchange rate exposure resulting from the change in the currency regime? (50%) Please keep a copy of the essay for your own records. The essay should be submitted electronically via Turnitin. Specific Guidelines 1- Your essay should not rely solely on the lecture notes and core textbooks. 2- You are expected to use external resources and professional databases (See below the list of exclusive databases) to learn more about the topic and provide an adequate analysis. All external sources must be referenced accordingly. 3- You are expected to mention all possible techniques to manage the exposure of the company, but speak in details about one technique. The adopted technique must be different than the ones used in the module. 4- Your essay must rely on professional databases and trusted sources to cite (see full list below). You are not allowed to cite news feed, blogs, general newspapers that are publicly available. 5- You are entitled to use a numerical example of your own when it comes to the hedging technique. 6- You are given an exclusive list of data sources, you must stick to the list, you are not allowed to use any other list. When presenting your figures or graphs, you must take a screenshot of the main source and attach it to your document, without changing the format, colours, labels or anything else from the original graph. On the use of GenAI (such as ChatGPT): The use of GenAI is acceptable and encouraged, however, only to help with structuring, speeding up your research and to improve your writing style. However, please remember that GenAi processes its information from other sources, some of which may be unreliable. Your content is your own responsibility, you are the one who should verify if it’s accurate or not. You could do so by providing real market examples, data, figures and tables to support your arguments. These data must be gathered from the main source (the list of exclusive data sources is provided below). You are not allowed to use any other sources for data. Please use GenAi to improve your knowledge base but not to wholly rely on it to complete your assignment. Exclusive Data Sources You are given an exclusive list of data sources, you must stick to the list, you are not allowed to use any other list: 1. Peer reviewed Economics and Finance Journal Articles 2. Worldbank database 3. Bloomberg Terminals 4. IMF database 5. ORBIS 6. FAME 7. FT 8. The economist 9. Statista 10. Other databases only if they are available through Sussex Library. General guidelines: I. Your essay should clearly demonstrate analytical, critical and evaluative skills in relation to international financial management. II. It is expected that your essay should demonstrate a wide background of reading and research; and only then, you would get an above average grade. III. All references should be acknowledged (see Library handout on reference styles –Harvard approach is strongly recommended). IV. Good standards of written English and presentation are expected and marks will be deducted if such standards are not met. V. The length of the coursework should be no more than 1,000 (exceeding the word limit will cost you 10% of the mark).
