Finance-AW-Q183

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ASSIGNMENT 1

 

 

Question 1

 

Table 1 shows the financial ratios for two different groups of firms; the most-admired firms (1) and the least-admired firms (2). The financial ratios are earnings before interest and taxes to total assets (EBITASS), return on total capital (ROTC), return on equity (ROE), return on assets (REASS) and market to book value (MKTBOOK).

 

Table 1: Financial Data for Most-Admired and Least-Admired Firms
 

FirmGroupMKTBOOKROTCROEREASSEBITASS
112.3040.1820.1920.3770.158
212.7030.2060.2050.4690.210
312.3850.1880.1820.5810.207
415.9810.2360.2580.4910.280
512.7620.1930.1780.5870.197
612.9840.1730.1780.5460.227
712.0700.1960.1780.4430.148
812.7620.2120.2190.4720.254
911.3450.1470.1480.2970.079
1011.7160.1280.1180.5970.149
1113.0000.1500.1570.5300.200
1213.0060.1910.1940.5750.187
1320.975-0.031-0.2800.105-0.012
1420.9450.0530.0190.3060.036
1520.2700.0360.0120.2690.038
1620.739-0.074-0.1500.204-0.063
1720.833-0.119-0.3580.155-0.054
1820.716-0.005-0.3050.0270.000
1920.5740.039-0.0420.2680.005
2020.8000.1220.0800.3390.091
2122.028-0.072-0.836-0.185-0.036
2221.2250.064-0.430-0.0570.045
2321.502-0.024-0.545-0.050-0.026
2420.7140.026-0.1100.0210.016

 

Your objective is to select the best of discriminating variables for forming the discriminant function that allow us to distinguish between the two different groups. Report your findings.
 
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Question 2

 

A Sample of 15 houses with similar square footage built by a particular housing developer throughout United States is selected and the heating oil consumption during the month of January is determined. It is believed that the average daily atmospheric temperature as measured in degrees Fahrenheit outside the house during that month and the amount of insulation as measured in inches in the attic of the house influence the heating oil consumption. The data is shown in Table 2.

 

Table 2 : Monthly heating oil consumption, atmospheric temperature and amount of attic insulation for random sample of 15 single-family houses.
 

HouseMonthly Consumption of Heating Oil (Gallons)Average Daily Atmospheric Temperature

()

Amount of Attic Insulation (Inches)
1275.3403
2363.8273
3164.34010
440.8736
594.3646
6230.9346
7366.796
8300.6810
9237.82310
10121.4633
1131.46510
12203.5416
13441.1213
14323.0383
1552.55810

 

  1. Fit a model of monthly heating oil consumption and interpret it.
  2. How much proportion of monthly heating oil consumption that is explained in the model?
  3. Evaluate the appropriateness of fitted model in (a) by analysing the residual.
  4. What can you conclude from the multicollinearity of the predictors?

 

 

Question 3

 

A marketing manager of a financial institution is interested in determining the probability that a financial institution is being most successful given the size and FP of the financial institution.  Consider the data given in Table 3 for a sample of 12 most-successful (MS) and 12 least-successful (LS) financial institutions (FI).

 

Table 3: Data for Most Successful and Least Successful Financial Institutions.
 

Most SuccessfulLeast Successful
SUCCESSSIZEFPSUCCESSSIZEFP
110.58212.281
112.80201.06
112.77201.08
113.50200.07
112.67200.16
112.97200.70
112.18200.75
113.24201.61
111.49200.34
112.19201.15
102.70200.44
102.57200.86

 

The value of SUCCESS is equal to 1 for the most successful financial institution and is equal to 2 for the least successful institution. The value of SIZE is equal to 1 for the large financial institution and is equal to 0 for a small financial institution.

 

  1. Construct a contingency table between success and the size of the FI
  2. Compute
  3. Calculate
  4. Fit a logistic regression model of a financial institution is being most successful given the size of the financial institution.
  5. Fit a logistic regression model of a financial institution is being most successful given the size and FP of the financial institution.
  6. Interpret (d) and (e) and make a comparison between them.

 

Question 4

 

The financial analyst on an investment banking firm is interested in identifying a group of customer that have similarities in terms of income (RM thousand) and education (years). Table 4 contains income and education in years for six hypothetical subjects.

 

Table 4: Hypothetical Data

Subject IdIncome

(RM thousand)

Education (years)
S155
S266
S31514
S41615
S52520
S63019

 

  1. Construct a similarity matrix containing Euclidean Distances.
  2. Determine the type of clustering technique to be used.
  3. Determine the number of clusters of the subjects and interpret your findings.

ASSIGNMENT 1

 

 

Question 1

 

Table 1 shows the average price for a number of foods in several cities in the United States. The main objective of this work is to form a measure of the Consumer Price Index (CPI). In particular, you need to form a weighted sum of the various food prices that would summarize how expensive or cheap are a given city’s food items. Use an appropriate statistical method to achieve the above objective. Justify your answer. Technique –Principle Component Analysis (Factors)

 

Table 1: Food Price Data

CityAverage Price (cents per pound)
BreadBurgerMilkOrangesTomatoes
Atlanta24.594.573.980.141.6
Baltimore26.591.067.574.653.3
Boston29.7100.861.4104.059.6
Buffalo22.880.665.3118.451.2
Chicago26.786.762.7105.951.2
Cincinnati25.3102.563.399.345.6
Cleveland22.888.852.4110.946.8
Dallas23.385.562.5117.941.8
Detroit24.193.751.5109.752.4
Honolulu29.3105.980.2133.261.7
Houston22.383.667.8108.642.4
Kansas City26.188.965.4100.943.2
Los Angeles26.989.356.282.738.4
Milwaukee20.389.653.8111.853.9
Minneapolis24.692.251.9106.050.7
New York30.8110.766.0107.362.6
Philadelphia24.592.366.798.061.7
Pittsburgh26.295.460.2117.149.3
St. Louis26.592.460.8115.146.2
San Diego25.583.757.092.835.4
San Francisco26.387.158.3101.841.5
Seattle22.577.762.091.144.9
Washington DC24.293.866.081.646.2

 

Question 2

 

Table 2 shows the financial ratios for a sample of 24 firms, the 12 most-admired firms and the 12 least-admired firms. The financial ratios are earnings before interest and taxes to total assets (EBITASS) and return on total capital (ROTC). Technique – Multiple Discriminant Analysis

 

Table 2: Financial Data for Most-Admired and Least-Admired Firms

Firm Number

(Dependent Variables)

Most-AdmiredFirm Number

(Dependent Variables)

Least- Admired
EBITASS

(Independent Variables)

ROTC

(Independent Variables)

EBITASS

(Independent Variables)

ROTC

(Independent Variables)

10.1580.18213-0.012-0.031
20.2100.206140.0360.053
30.2070.188150.0380.036
40.2800.23616-0.063-0.074
50.1970.19317-0.054-0.119
60.2270.173180.000-0.005
70.1480.196190.0050.039
80.2540.212200.0910.122
90.0790.14721-0.036-0.072
100.1490.128220.0450.064
110.2000.15023-0.026-0.024
120.1870.191240.0160.026

 

  1. Determine the best set of factors that significantly differentiate between the two types of firms. Justify your answer.
  2. Use the answer from (a) to classify the future observations.

 

 

Question 3

 

A market analyst is interested in determining if type of firms (Most-Admired and Least- Admired) has an effect on the financial ratios (EBITASS and ROTC). The data is tabulated in Table 2. Use MANOVA to achieve the above objective. Interpret the result.

 

 

Question 4

 

The internal Revenue Service (IRS) is trying to estimate the monthly amount of unpaid taxes discovered by its auditing division. In the past, the IRS estimated this figure on the basis of the expected number of field-audit labour hours. In recent years, however, field-audit labour hours have become an erratic predictor of the actual unpaid taxes. As a result, IRS is looking for another factor with which it can improve the estimating equation. The auditing division does keep a record of the number of hours its computers are used to detect unpaid taxes. By using data in Table 3: Technique – Multiple Linear Regression

 

  1. Fit a more accurate estimating model for the unpaid taxes discovered for each month.
  2. Determine the significance of each predictor.
  3. Find and interpret the Coefficient of Determination.
  4. How much in unpaid taxes do they expect to discover in November?

 

Table 3: Data from IRS Auditing Records During the Last 10 Months

MonthField-Audit Labour Hours (00s Omitted)Computer Hours

(00s Omitted)

Actual unpaid Taxes Discovered

(millions of dollars)

January451629
February421424
March441527
April451325
May431326
June461428
July441630
August451628
September441528
October431527

 

Product Code-Finance -AW-Q184

 
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