Financial Engineering and Machine Learning FINA1147
You will use one market index and four companies’ daily data and these companies should be
from two different sectors. (The required data can be downloaded from Yahoo finance:
http://uk.finance.yahoo.com/). The sample period should be latest and at least 2 years in
length (for example, from Jan 2018 to Jan 2020). For the volatility forecast, the required FX
data can be downloaded from the course Moodle page. For the empirical analysis, you can
use statistical software such as EViews, STATA, or SPSS etc., which has to specify in the
report.
A. Mean-Variance Optimization
1. Briefly explain mean-variance portfolio optimization.
(5 Marks)
2. Estimate the covariance matrix for the selected four companies’ stocks.
(5 Marks)
3. Plot by creating portfolios using the selected four companies and the obtained covariance
matrix. Discussing the results of the portfolio.
(10 Marks)
B. Panel Data Analysis
1. Construct a panel data set using the latest 100 days of the four stock prices. Transfer the
stock prices, market index, and risk-free rate into log returns.
(8 Marks)
2. Verify the CAPM theory using OLS, FE, and RE estimators. Select the appropriate model
(OLS, FE or RE). Discuss the obtained regression results.
(12 Marks)
C. Time-Series Data Analysis
1. Choose one of your stock price series, compute ACF and PACF for the log returns. Discuss
the results.
(4 Marks)
2. Forecast the log returns with AR(5) model and verify the forecasting accuracy by
considering the last 6 months of the data as out-of-sample.
(8 Marks)
3. Estimate the ARMA(3,2) model and comment on the estimations.
(8 Marks)
D. Volatility Analysis
1. Choose one of your stock price series, verify the ARCH effect and estimate GARCH(1, 1)
model.
(10 Marks)
2. Using the data provided, carry out a GARCH(1, 1) volatility forecasting by considering the
last 6 months of the data as out-of-sample. Discuss the results.
(10 Marks)
E. Discussions on Machine Learning Application
1. Discuss the following concepts.
a) Machine Learning
b) Supervised Learning
c) Differentiate between test set and training set
(12 Marks)
2. Explain your understanding on neural network in machine learning. Provide one possible
application of neural network in the financial practice and explain the processes.
(8 Marks)
You need to do all the above tasks and submit your results with detailed discussion on the
tests in a report form (using academic style and minimum 2000 words).
Please ensure you are following academic writing requirements, otherwise a
deduction of marks ranging from 10 to 30 will be applied. You will use one market index and four companies’ daily data and these companies should be
from two different sectors. (The required data can be downloaded from Yahoo finance:
http://uk.finance.yahoo.com/). The sample period should be latest and at least 2 years in
length (for example, from Jan 2018 to Jan 2020). For the volatility forecast, the required FX
data can be downloaded from the course Moodle page. For the empirical analysis, you can
use statistical software such as EViews, STATA, or SPSS etc., which has to specify in the
report.
A. Mean-Variance Optimization
1. Briefly explain mean-variance portfolio optimization.
(5 Marks)
2. Estimate the covariance matrix for the selected four companies’ stocks.
(5 Marks)
3. Plot by creating portfolios using the selected four companies and the obtained covariance
matrix. Discussing the results of the portfolio.
(10 Marks)
B. Panel Data Analysis
1. Construct a panel data set using the latest 100 days of the four stock prices. Transfer the
stock prices, market index, and risk-free rate into log returns.
(8 Marks)
2. Verify the CAPM theory using OLS, FE, and RE estimators. Select the appropriate model
(OLS, FE or RE). Discuss the obtained regression results.
(12 Marks)
C. Time-Series Data Analysis
1. Choose one of your stock price series, compute ACF and PACF for the log returns. Discuss
the results.
(4 Marks)
2. Forecast the log returns with AR(5) model and verify the forecasting accuracy by
considering the last 6 months of the data as out-of-sample.
(8 Marks)
3. Estimate the ARMA(3,2) model and comment on the estimations.
(8 Marks)
D. Volatility Analysis
1. Choose one of your stock price series, verify the ARCH effect and estimate GARCH(1, 1)
model.
(10 Marks)
2. Using the data provided, carry out a GARCH(1, 1) volatility forecasting by considering the
last 6 months of the data as out-of-sample. Discuss the results.
(10 Marks)
E. Discussions on Machine Learning Application
1. Discuss the following concepts.
a) Machine Learning
b) Supervised Learning
c) Differentiate between test set and training set
(12 Marks)
2. Explain your understanding on neural network in machine learning. Provide one possible
application of neural network in the financial practice and explain the processes.
(8 Marks)
You need to do all the above tasks and submit your results with detailed discussion on the
tests in a report form (using academic style and minimum 2000 words).
Please ensure you are following academic writing requirements, otherwise a
deduction of marks ranging from 10 to 30 will be applied.
Project 4: Reflecting on the Semester and Forecasting the Future – Metacognitive Reading
As your final assignment and instead of an in-class final exam, you will write extensively on your experiences as a first year college student. This final project of the semester must assess your work as well as identify the skills and habits of mind you have developed this semester as documented in your projects, in class writings, and class discussions before predicting how those skills could be of value to you in the future.
Write an essay that addresses the key concepts and ideas that have emerged in the course so far; such as: responding to texts, techniques on reading scholar sources, useful ways of incorporating sources and using quote sandwiches, research skills and finding/evaluating sources, the revision process, using citation forms, paraphrasing/summarizing/quoting, finding ways of conveying arguments as well as incorporating all sides of a discussion, plagiarism, or anything else you can find in your notes.
Reflect on what we have done in this class all semester long and find at least three things (these can be themes from the in-class readings, essays you read for this class, or anything addressed in a class video or resource) you remember that you will be able to take with you moving forward.
You must reflect on your own journey as a student through this college writing class experience in order to help you become conscious of your path in developing these skills. You should show that you are thinking carefully about your learning – both the content of the learning and the way that you are learning it – by giving lots of examples and details.
Make sure to choose an audience. If it helps, write this as a letter, either to future you as a way to show them all you have learned now or to a new college student, perhaps. Pick a clear and easily identified audience for this project.
The purpose of this project is to teach your audience what you’ve decided is most significant about all you’ve learned about effective academic writing so far. Secondary purpose: to demonstrate to me, your instructor, that you’re comprehending, applying, and reflecting on the ideas from the course.
You will need to cite at least one of the essays you read over the course of this class. Everything is expected to be done MLA format.
This should be a comprehensive story about the past year, feel free to include the hardships of learning in a pandemic and any future goals, but focus on how far you have come. Take a look at your very first piece of writing for me and see what you would do differently now.
finance
Chisholm University’s new athletics director must reallocate the athletics budget in its entirety, balancing legal obligations with broader educational and financial goals. A committee appointed to work on this issue had failed to reach consensus due to disagreements about how to comply with Title IX, the law mandating gender parity in all educational offerings, including athletics. The athletics director has the facts organized into an optimization problem so she can systematically balance the tradeoffs and manage system demands or constraints. Using the case study ‘Fair Play at Chisholm University
Download Fair Play at Chisholm University‘ act as a consultant and create a paper addressing the following:
- Summarize the major issues of the case (one page),
- Analysis and evaluate Burke’s three options and concerns/strategies to address the issues from a financial, legal, athletic department/conference continuity lens (at least two to three pages),
- Pick the best strategy and justify your strategy selection by documenting how you will optimize the budget while balancing legal obligations with broader educational and financial goals (at least two pages),
- Provide chart documentation of how your choice will help Chisholm University with its financial goals moving forward (one page).
English essay
1. Based on the narrator’s description of Dee in “Everyday Use,” has this character changed from
the way she was before she went to college to the way she is during the events described in the
story? Why or why not? Be sure to use details from the story to support your argument. (15
points)
2. In “The Nose,” Nikolai Gogol comments on the role of a person’s title in Russian society. What
commentary is Gogol making by repeatedly using the terms “committee-man” and “major” in his
narrative? Be sure to use specific details from the story to support your argument. (15 points)
3. In “The Black Cat,” the narrator begins the story by speaking directly to the reader. The
narrator continues this practice periodically throughout the story. How does the author’s use of
this structure create mystery in the story? Be sure to use specific details from the story to
support your response. (15 points)
Lab 6 – Thevenin Equivalent Circuits
Part 1. Black Box Testing to Determine the Thevenin
Equivalent Circuit
1. Use Multisim Live to build the circuit above. Connect a voltmeter across the output
terminals, a and b. Directly measure Voc to determine Vth.
1. Voc = Vth =
2. Connect a 1M? load resistor across the output terminals. Save a copy of your
schematic and include with your lab write-up. Measure and record VL.
1. VL =
3. How does this value compare to your measurement of Voc from above? What do you
think is the reason for that relationship?
4. Now adjust the values of the load resistance according to the table below. Measure
and record VL and IL as you go. Compute Power = VL * IL
RL VL IL Power= VL * IL
1M?
100k?
10k? 8.1633 V 816 uA 6.661 mW
2.5k?
2k?
1.5k?
1k? 3.3058 3. 3058 mA 10.92 mW
5. Maximum power transfer occurs when the load resistance is equal to Rth. Which load
resistance gave you the maximum power?
RL, max power =
6. Next, measure Isc = Short circuit current. Disconnect the load resistor and attach an
ammeter directly to the output terminals of the black box to measure Isc.
Isc =
7. Compute Rth based on Voc and Isc.
Rth = Voc/Isc =
How does your Rth compare to the RL value that gives max power?
8. A. Connect a load resistor equal to Rth and to the output terminals of the circuit.
Measure the load voltage and current and compute the power delivered to the load.
VL=
IL =
P =
B. Repeat the measurements above for a resistor half as large as RTH.
VL=
IL =
P =
C. Repeat the measurements above for a resistor twice as large as Rth.
VL =
IL =
P =
9. How do the values for (8a), (8b), (8c) compare?
Part 2. Lab Report
Write or type all of your calculations and answers to the questions above and save as
lab6yourname.pdf. Upload via Canvas.
CSIS 1190 – Excel in Business
INTRODUCTION
The purpose of this MS Excel Major Assignment is to give you some experience with several of the advanced “decision support” features of the Microsoft Excel spreadsheet. These features include:
- Data analysis using pivot tables
- Comparison of different scenarios using scenario manager
- Working with multiple worksheets
- Spreadsheet organisation using grouping & outlines
- Advanced MS Excel functions
DECISION MAKING SCENARIO
The purchase of a house usually entails some exchanges of prices between buyers and sellers. The realtor is there to facilitate the transactions. The seller has an asking price but typically settles for less. The commission paid to Happy Realty (for property priced above $400,000) is 9% commission on the actual selling price for the first $200,000, and a further 2.5% for the remaining selling price. A flat commission of 7.5% is charged (for property priced at $400,000 & below). The realtor in turns gets 40% of the total commission paid to Happy Realty.
Happy Realty wishes to encourage its realtors to try to sell the house as close to the asking price of the sellers as possible. To do that Happy Realty will pay additional bonus for house sold at a certain percentage of the asking price. The distribution of extra bonus is calculated as follows:
- A extra 1.65% bonus of the actual selling price is paid to the realtor if he/she is able to sell the house at 95% or more of the asking price.
- A extra 1.25% bonus of the actual selling price is paid to the realtor if he/she is able to sell the house sold at 90% or more of the asking price.
- A extra 1.05% bonus of the actual selling price is paid to the realtor if he/she is able to sell the house sold at less than 90% of the asking price
- No extra bonus will be paid for houses sold at less than 80% of the asking price.
Happy Realty revenue is calculated from the commissions paid by the seller minus the commission and extra bonus paid to the realtor. Calculate the percentage of $ earned for each home based on the actual final transacted price. Develop a worksheet to be used by the CEO of Happy Holdings Group of Companies to allow him/her to identify the key profit earned at Happy Realty.
Also use pivot tables to organise Salesperson’s commission by month with total commission for the first half of the year for each salesperson. Also in another separate pivot table, organise your data to reflect the net profit by house and by month.
REQUIREMENTS
You are required to create on the 1st spreadsheet (Commissions) a spreadsheet-based decision support model that allows the CEO of Happy Holdings Group of Companies to understand how the economic climate will affect the revenue earned at Happy Realty to compare different scenarios (e.g. Varying percentages collected from owners: 7%, 9%, 11% for the 1st $200,000; the 2.5% is fixed for all scenarios). Rather than having a separate model for each scenario, you are expected to design a single model and employ “scenarios” to change only those aspects of the model that varies with the scenarios being considered. At the same time, he is able to use the same workbook to forecast income for his group of companies.
Use advanced MS Excel built-in functions (IF, HLOOKUP, VLOOKUP, etc.) in your spreadsheet wherever you see fits. Also use pivot tables to organise Realtor’s commission by month with total commission for the first half of the year for each realtor. Also in another separate pivot table, organise your data to reflect the net profit by house and by month.
On a 2nd worksheet (Forecast), also plan a 5-year forecast of the Group’s corporate taxable income based on the following assumptions:
| Forecast of Increase/Decrease Percentages: | |||
| Restaurant | -2.25% | ||
| Motor | 11.5% | ||
| Realty | 20.0% | ||
| Entertainment | -17.0% | ||
| Computers | -9.0% | ||
| Directors’ Fees | 9.5% | ||
Organise your spreadsheet (if possible) into grouping & outlines.
|
2022 | |||||
| Group Income | ||||||
| Net Profit from Happy Restaurant | $595,000 | |||||
| Net Profit from Happy Motors | $485,800 | |||||
| Net Profit from Happy Realty | X value | |||||
| Net Profit from Happy Entertainment | $678,050 | |||||
Net Profit from Happy Computers |
$695,250 | |||||
| Other Operating Expenses | ||||||
| Annual Directors’ Fees | $790,900 | |||||
| Misc. Dividends to Shareholders | Y value | |||||
Total Corporate Taxable Income = Group Income – Other Operating Expenses
On the 3rd spreadsheet, (Loan) prepare a worksheet for all salespersons to use with the assumptions that if customer needs to take a house loan, it will normally be 75% of final transacted price with 25% being down payment. The loan interest rate is prime rate (3%) plus 0.75%, (assume prime rate to be 3%, and can be changed anytime). Period of loan is usually 15 years. All price used are net of GST and PST.
Calculate the following: (You may pick one of the houses listed in the 1st worksheet)
- Purchase Price of a house (may use linking worksheets feature)
- Monthly mortgage payment & Total house payments (including down payment) over the 15 years
- Total Interest for the house loan with Interest & Principal paid per month
- Starting & Ending dates of payment
- Beginning Principal & Ending Balance at the end of each month till the end of the loan
Example:
…
| Purchase Price | $850,500.00 | House Loan | $637,875.00 | |
| Monthly Payment | $4,638.77 | Down Payment | $212,625.00 | |
| Total Payment | $1,047,603.63 | |||
| Prime Rate | 3.00% | |||
| 3Plus rate | 0.75% | Period (years) | 15 | |
| Month | Beginning Principal | Interest Paid | Principal Paid | Ending Balance |
| Apr-22 | $637,875.00 | $1,993.36 | $2,645.41 | $635,229.59 |
| May-22 | $635,228.59 | $1,985.09 | $2,653.68 | $632,575.91 |
…
DESIGN ISSUES
The following is a short list of generic design issues that you should consider when building your application:
- Never use a number in a formula. The purpose of a separate table of assumptions is to allow you to identify your assumptions and change them easily
- Named ranges and cells should be used where practical to make your formulas more readable.
- Use scenarios (instead of 2 separate workbooks) to compare situations that share the same basic model, but which have different values for critical decision inputs.
- Create 1st 2 spreadsheets in ONE workbook named Happy.xls and the 3rd spreadsheet in another workbook named Dream.xls and link them. Use linking formula across workbooks/worksheets wherever possible.
SUBMISSION
You are required to submit all relevant softcopies of your designed spreadsheets via email by the deadline. You are required to e-mail to me with the e-mail subject (example FirstName_LastName_Campus) (example: your subject line should read JohnSmith_NW or JohnSmith_DL).
You should also make a backup of the system. Late submissions will not be graded.
4
DATA (You MUST use the following data for your assignments. )
Sales for the 1st 6 months as follows:
Address of House |
Date Sold | Asking Price | Final Transacted Price | Realtor |
| 2567 Mica Place | Jan-22 | $2,229,000 | $2,528,000 | Eric |
| 1288 Pinetree Way | Jan-22 | $1,923,000 | $2,200,000 | Eric |
| 204 #81 Elm Street | Feb-22 | $1,028,000 | $850,500 | Eric |
| #101 800 Schoolhouse Ave | Mar-22 | $715,000 | $599,900 | Sam |
| #501 1290 Greenway Pl | Feb-22 | $995,900 | $788,000 | Sam |
| #409 999 Como Lake | Apr-22 | $822,000 | $750,000 | Carol |
| #111 122 Gilmore Ave | Apr-22 | $499,800 | $399,800 | Sam |
| 2388 Sugarpine Ave | Jun-22 | $3,922,000 | $3,850,000 | Carol |
| 1634 Diamond Cres. | Jun-22 | $3,029,000 | $2,650,000 | Eric |
| 123 Holdom Ave | May-22 | $1,999,990 | $1,880,000 | Sam |
| 582 #14 West 13th St | Feb-22 | $650,000 | $499,900 | Carol |
Editing and Proofreading Task (Mechanics and Punctuation
Using your textbook (especially Appendix, Part C), identify the error in each sentence. Correct the error using Track Changes in Word.
- Many students fail to understand the importance of proofreading their work and it shows in their project grades.
- The report will be distributed to Operations, Research and Development and Accounting.
- Just as we finished eating the bell rang.
- Connie my Technical Communication instructor is looking forward to her winter break.
- One of the many possibilities, is editing the draft to earn a higher grade.
- I still need one thing to ace this assignment; a charming personality.
- My instructor asked me if I knew what I was doing for last week’s assignment?
- It is clear to me that this sweater is hers’.
- She replied “Yes, that’s the correct answer for the last question.”
- This textbook is a newly-acquired offering at the bookstore.
- My accounting instructor assigned 3 textbooks.
- 25 people showed up to the meeting on Thursday.
- The university I attend is located in mankato, Minnesota.
- I have always preferred pepsi over coke.
- The screw measured .025 inches.
English essay
INSTRUCTIONS:
1: Listen to the ““Racism is America’s Oldest Algorithm”: How Bias Creeps Into Health
Care AI” episode of the Color Code podcast (via Sound Cloud or Apple Podcasts). Take
notes.
2: Answer essay questions A & B.
FORMATTING: Overall, your essay answers should be no more than 2-3 pages double spaced
total (excluding the citations/references page(s)). The font should be Times New Roman (12-
point) & with normal margins (1”). Make sure that you cite all scholarly & journalistic
references you use as sources of information following the STS-UY 2144 citation style guide.
ESSAY QUESTIONS:
A: Ziad Obermeyer discussed a case of algorithmic bias that he & other medical
practitioners were working with the company that made a health care AI to produce
non-discriminatory solutions. Obermeyer found that the algorithm underscored Black
patients who were in need of medical care while fast-tracking white patients for
receiving medical care. What roles do flaws in model design play in producing this case
of algorithmic bias (For example, model design flaws can be due to: definitions of
success; variables within the model; the data being used to train the algorithm; etc.)?
Identify & explain one limitation of utilizing algorithmic audits to catch algorithmic bias.
B: Consider this scenario, a software engineer & data scientist has been hired to develop
a health care AI to identify patients who are at risk of developing heart disease, but the
designer doesn’t know much about the histories of racism nor how racist discrimination
works. Define care ethics & then explain how the engineer is failing to meet the
requirements to practice care? Identify & define the ethical elements of care that the
engineer is failing to meet
An Op-Ed in topic of Anti Corruption in Kenya
Write an Op-Ed in topic of Anti Corruption Policy in Kenya. — Please come up with a better title, catchy one.
The Op-Ed should be 700 words, no plagiarism. Thank you. — Please, the most important is the main argument, like what’s your “OPINION” of the anti-corruption policy in Kenya, and wwhat’s other argument of yours to support your main one? Is there any facts from creditable sources to back up
