Concept explainers
To identify:
The signals for any buy or sell by using table
Introduction:
Industry Analysis: It is a most important puppet which was used to compare a specified company's performance with the other's market in the same industry market.
Explanation of Solution
Given:
Table
Detailed explanation / work out of the complete problem.
Days | Moving average formula | Moving average | Action |
Selling point | |||
Buying point | |||
We see that in
And further, we see that
From the above, the selling point is
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Chapter 12 Solutions
INVESTMENTS(LL)W/CONNECT
- Consider again the example introduced in Section 4.5 of a credit card company that has a database of information provided by its customers when the customers apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3). Figure 4.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? d. Recall the multiple regression in Figure 4.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts a, b, and c? What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? e. Add the coefficients of determination for the simple linear regression in parts a, b, and c, and compare the result to the coefficient of determination for the multiple regression model in Figure 4.23. What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? f. Add age, a dummy variable for gender, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 4.23. Code the dummy variable for gender as 1 if the customers gender is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?arrow_forward(This is what is in between the 1st and 2nd screenshot) Using these data, determine the following: Earnings per share. Round your answer to two decimal places. Price-to-earnings ratio. Round your answer to two decimal places. Book value per share. Round your answer to two decimal places. Market-to-book ratio. Round your answer to two decimal places. EV-EBITDA multiple. Assume the cost of sales includes $14 million in depreciation expenses. Assume there are no amortization expenses. Round your answer to two decimal places. How much of the retained earnings total was added during Year 1? Enter your answer in millions. For example, an answer of $1.2 million should be entered as 1.2, not 1,200,000. Round your answer to two decimal places.$ million Show Eastland’s new balance sheet after the company sells 1 million new common shares in early Year 2 to net $28 a share. Part of the proceeds, $12 million, is used to reduce current liabilities, and the remainder is temporarily…arrow_forwardDetermining gross profit During the current year, merchandise is sold for $8,780,000. The cost of the goods sold is $5,531,400. This information has been collected in the Microsoft Excel Online file. Open the spreadsheet, perform the required analysis, and input your answers in the questions below. Open spreadsheet a. What is the amount of the gross profit? Round your answer to the nearest dollar. b. Compute the gross profit percentage (gross profit divided by sales). Round your answer to the nearest whole number. C. Will the income statement always report a operating incomne? Yes No Previous earch 542 PM 5/3/2021arrow_forward
- 1. The annual net income of MAC Industries since 2005 is given below. Years t (since 2005) Net Income 1 4 6 12 15 $1,500 $2,500 $2,875 $2,850 $2,250 a. Create a scatter plot that includes your curve of best fit on top of the data. You should try several functions and make your choice based on best r-squared value. b. Find the year that Net Income was at its highest point. c. What was the income in that year?arrow_forwardDirection: compute the projected revenue by day, month and year based on your business concept. (Please refer to the picture.)arrow_forwardConsider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?arrow_forward
- Here is the operating data for Yalis Cleaning, Inc.: After analyzing the transactions, prepare a vertical analysis schedule for the company for 2021 and 2020 using service revenue as the base amount. Round percentages to two decimal places.arrow_forwardUse the following selected data from Business Solutions's income statement for the three months ended March 31, 2022, and from its Complete this question by entering your answers in the tabs below. March 31, 2022, balance sheet to complete the requirements. Computer services revenue Net sales (of goods) Total sales and revenue Cost of goods sold. Net income Quick assets Current assets Total assets Current liabilities Total liabilities. Total equity Required 1 Required: 1. Compute the gross margin ratio (both with and without services revenue) and net profit margin ratio. 2. Compute the current ratio and acid-test ratio. 3. Compute the debt ratio and equity ratio. 4. What percent of its assets are current? What percent are long term? Required 2 Complete this question by entering your answers in the tabs below. $ 25,307 18,693 44,000 Required 3 Gross margin ratio Net profit margin ratio 14,052 18,833 90,924 95,568 120,268 875 875 119,393 % Compute the gross margin ratio (both with and…arrow_forwardUse a spreadsheet and the following excerpts from Hileah Companys financial information to build a template that automatically calculates (A) inventory turnover and (B) number of days sales in inventory, for the year 2018.arrow_forward
- Use the following information to answer the questions that follow. A. Calculate the operating income percentage for each of the stores. Comment on how your analysis has changed for each store. B. Perform a vertical analysis for each store. Based on your analysis, what accounts would you want to investigate further? How might management utilize this information? C. Which method of analysis (using a dollar value or percentage) is most relevant and/or useful? Explain.arrow_forwardI Need help finding the number of days of sales.....arrow_forwardReview the select information for Bean Superstore and Legumes Plus (industry competitors), and then complete the following. Compute the accounts receivable turnover ratios for each company for 2018 and 2019. Compute the number of days’ sales in receivables ratios for each company for 2018 and 2019. Determine which company is the better investment and why. Round answers to two decimal places.arrow_forward
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