...offering (IPO). With SEO, corporations raise funds through the sale of stocks rather than the issuance of additional debt. Many articles have mentioned that investors may construe a seasoned issue as a sign that a company is having financial problems. This news can cause the price of both the corporation’s outstanding shares and the new shares to fall. This is the impact of SEOs to the issuer itself, then what is the impact of SEOs to the competitors in the same industry? Will the stock return of competitors decrease or increase, or the SEOs have no impact on other corporations? This study examines the abnormal return on the file date of competitors in the same industry to test the effect of SEOs to competitors by event study and Ordinary Least-Squares Regression. 1. Introduction This study observes the effects of seasoned equity offerings (SEOs) to the competitors in the same industry. Seasoned equity offerings may involve shares sold by existing shareholders (non-dilutive), new shares (dilutive) or both. This moment can be marked by the file date that the corporation announced to conduct a seasoned equity offering, and the reacts of the market can be shown from the return of the specific corporation and its competitors, defined as Return = { [Ending stock price (period 1) – Initial price]+Dividends} / Initial price. Here we mainly examine the excessive or abnormal return of the corporation which conducted the SEO, and its competitors, and check what kind of variables...
Words: 3113 - Pages: 13
...JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 23: 925– 948 (2008) Published online 7 November 2008 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/jae.1036 ECONOMETRICS OF AUCTIONS BY LEAST SQUARES LEONARDO REZENDE* PUC-Rio, Rio de Janeiro, Brazil; and University of Illinois at Urbana–Champaign, Illinois, USA SUMMARY I investigate using the method of ordinary least squares (OLS) on auction data. I find that for parameterizations of the valuation distribution that are common in empirical practice, an adaptation of OLS provides unbiased estimators of structural parameters. Under symmetric independent private values, adapted OLS is a specialization of the method of moments strategy of Laffont, Ossard and Vuong (1995). In contrast to their estimator, here simulation is not required, leading to a computationally simpler procedure. The paper also discusses using estimation results for inference on the shape of the valuation distribution, and applicability outside the symmetric independent private values framework. Copyright 2008 John Wiley & Sons, Ltd. Received 15 September 2006; Revised 1 July 2008 1. INTRODUCTION The field of econometrics of auctions has been successful in providing methods for the investigation of auction data that are well grounded in economic theory and allow for inference on the structure of an auction environment. Today, a researcher has a number of alternative structural methods, especially within the independent private-values paradigm...
Words: 12659 - Pages: 51
...Quantitative Business Valuation Other Titles in the Irwin Library of Investment and Finance Convertible Securities by John P. Calamos Pricing and Managing Exotic and Hybrid Options by Vineer Bhansali Risk Management and Financial Derivatives by Satyajit Das Valuing Intangible Assets by Robert F. Reilly and Robert P. Schweihs Managing Financial Risk by Charles W. Smithson High-Yield Bonds by Theodore Barnhill, William Maxwell, and Mark Shenkman Valuing Small Business and Professional Practices, 3rd edition by Shannon Pratt, Robert F. Reilly, and Robert P. Schweihs Implementing Credit Derivatives by Israel Nelken The Handbook of Credit Derivatives by Jack Clark Francis, Joyce Frost, and J. Gregg Whittaker The Handbook of Advanced Business Valuation by Robert F. Reilly and Robert P. Schweihs Global Investment Risk Management by Ezra Zask Active Portfolio Management 2nd edition by Richard Grinold and Ronald Kahn The Hedge Fund Handbook by Stefano Lavinio Pricing, Hedging, and Trading Exotic Options by Israel Nelken Equity Management by Bruce Jacobs and Kenneth Levy Asset Allocation, 3rd edition by Roger Gibson Valuing a Business, 4th edition by Shannon P. Pratt, Robert F. Reilly, and Robert Schweihs The Relative Strength Index Advantage by Andrew Cardwell and John Hayden Quantitative Business Valuation A Mathematical Approach for Today’s Professional JAY B. ABRAMS, ASA, CPA, MBA McGRAW-HILL New York San Francisco Washington, D.C. Auckland Bogota ´ Caracas Lisbon London...
Words: 214584 - Pages: 859
...1. | What is the slope of the regression line? | | Answer | The slope is the coefficient before the x variable (D in this case). Thus the answer is 0.0138. | Points Earned: | 1/1 | Correct Answer: | 0.0138 | Your Response: | 0.0138 | 2. | Explain in specific language what this slope says about this penguin's dives. | | A. | If the depth of the dive is increased by one meter, it adds 0.0138 minutes to the time spent under water. | B. | If the depth of the dive is decreased by one meter, it adds 0.0138 minutes to the time spent under water. | C. | If the depth of the dive is increased by 0.0138 meter, it adds one minute to the time spent under water. | | In the equation of a line, ŷ = a + bx, b is the slope. The slope is the amount by which y changes when x increases by one unit. | Points Earned: | 1/1 | Correct Answer: | A | Your Response: | A | 3. | According to the regression line, how long does a typical dive to a depth of 180 meters last? Answer to 3 decimal places. | | Answer | The predicted value of the dive duration (DD) to a depth D = 180 is given by the regression equation DD = 2.69 + 0.0138D = 2.69 + 0.0138 × 180 = 5.174 | Points Earned: | 1/1 | Correct Answer: | 5.174 | Your Response: | 5.174 | 4. | The dives varied from 40 meters to 300 meters in depth. Plot the regression line from x = 40 to x = 300. Which of the lines in the figure below is the correct regression line? | | A. | Blue | B. | Yellow | C....
Words: 5441 - Pages: 22
...139 Part 2 Costs and Decision Making Chapter 5 Cost Behavior and Relevant Costs Chapter 6 Cost-Volume-Profit Analysis and Variable Costing Chapter 7 Short-Term Tactical Decision Making Chapter 8 Long-Term (Capital Investment) Decisions 140 Chapter 5 Cost Behavior and Relevant Costs Chapter 5 U 141 Cost Behavior and Relevant Costs nderstanding the behavior of costs is of vital importance to managers. Understanding how costs behave, whether costs are relevant to specific decisions, and how costs are affected by income taxes allows managers to determine the impact of changing costs and other factors on a variety of decisions. This chapter introduces concepts and tools that will be used in Chapters 6 through 8. Chapter 5 begins with a definition of cost behavior and illustrates the concepts of fixed costs, variable costs, and mixed costs. Next, the chapter revisits the concept of relevant costs (introduced in Chapter 1) as it applies to variable and fixed costs. The chapter also describes the impact of income taxes on costs. Learning Objectives After studying the material in this chapter, you should be able to: 1 Describe the nature and behavior of fixed, variable, and mixed costs Analyze mixed costs using regression analysis and the high/low method 2 Distinguish between relevant and irrelevant costs and apply the concept to decision making 3 Illustrate the impact of income taxes...
Words: 13858 - Pages: 56
...Linear Regression and Correlation Chapter 13 McGraw-Hill/Irwin ©The McGraw-Hill Companies, Inc. 2008 GOALS Understand and interpret the terms dependent and independent variable. Calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate. Conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero. Calculate the least squares regression line. Construct and interpret confidence and prediction intervals for the dependent variable. 2 Regression Analysis - Introduction Recall in Chapter 4 the idea of showing the relationship between two variables with a scatter diagram was introduced. In that case we showed that, as the age of the buyer increased, the amount spent for the vehicle also increased. In this chapter we carry this idea further. Numerical measures to express the strength of relationship between two variables are developed. In addition, an equation is used to express the relationship. between variables, allowing us to estimate one variable on the basis of another. 3 Regression Analysis - Uses Some examples. Is there a relationship between the amount Healthtex spends per month on advertising and its sales in the month? Can we base an estimate of the cost to heat a home in January on the number of square feet in the home? Is there a relationship between the miles per gallon achieved by large...
Words: 2248 - Pages: 9
...DELHI TECHNOLOGICAL UNIVERSITY SELF – STUDY Ranesh Shevam 2k12/EC/139 Self study on : Object Tracking ( Structural partial least square for simultaneous object tracking and segmentation.) Report : * Definition * Applications * Challenges * Simplification of Tracking DEFINITION : * Tracking can be defined as the problem of estimating the trajectory of an object in the image plane as it moves around a scene * Three steps in video analysis: 1. Detection of interesting moving objects 2. Tracking of such objects from frame to frame 3. Analysis of object tracks to recognize their behavior 1) Applications : * motion-based recognition * human identification based on gait, automatic object detection, etc * automated surveillance * monitoring a scene to detect suspicious activities or unlikely events * video indexing * automatic annotation and retrieval of the videos in multimedia databases * human-computer interaction * gesture recognition, eye gaze tracking for data input to computers, etc. * traffic monitoring * real-time gathering of traffic statistics to direct traffic flow * vehicle navigation * video-based path planning and obstacle avoidance capabilities. Challenges : * loss of information caused by projection of the 3D world on a 2D image * noise in images * complex object motion * nonrigid or articulated nature of objects ...
Words: 657 - Pages: 3
...empec, Vol. 13, 1988, page 223-249 Nonparametric Estimation and Hypothesis Testing in Econometric Models By A. Ullah ~ Abstract: In this paper we systematically review and develop nonparametric estimation and testing techniques in the context of econometric models. The results are discussed under the settings of regression model and kernel estimation, although as indicated in the paper these results can go through for other econometric models and for the nearest neighbor estimation. A nontechnical survey of the asymptotic properties of kernel regression estimation is also presented. The technique described in the paper are useful for the empirical analysis of the economic relations whose true functional forms are usually unknown. 1 Introduction Consider an economic model y =R(x)+u where y is a dependent variable, x is a vector o f regressors, u is the disturbance and R(x) = E ( y l x ) . Often, in practice, the estimation o f the derivatives o f R(x)are o f interest. For example, the first derivative indicates the response coefficient (regression coefficient) o f y with respect to x, and the second derivauve indicates the curvature o f R(x). In the parametric econometrics the estimation o f these derivatives and testing 1 Aman Ullah, Department of Economics, University of Western Ontario, London, Ontario, N6A 5C2, Canada. I thank L Ahmad, A. Bera, A. Pagan, C. Robinson, A. Zellner, and the participants of the workshops at the Universities of Chicago...
Words: 5119 - Pages: 21
...Contents Introduction 2 3.1 & 3.2 Produce graphs using spread sheets and draw valid conclusions based on the information derived. Create trend lines in spread sheet graphs to assist in forecasting for specified business information 3 3.3 Prepare a business presentation using suitable software and techniques to disseminate information effectively. 11 3.4 Produce a formal business report 12 4.1 Use appropriate information processing tools 17 4.2 Prepare a project plan for an activity and determine the critical path 20 4.3 Use financial tools for decision-making 22 Conclusion 26 Introduction In order to enhance the competitive advantages of Highlands Coffee in Ho Chi Minh City over other competitors, the report will show some information that might help the company find out appropriate strategy to maintain their position in market. At the first task is the forecasting technique, which shows various way to calculate the actual sales of the bank in the future, and then show the formal report for the Sales director. In the next task in the reports, there is the information processing tools which shows the decision level and information types in each department and the recommendation for the software for each part of department. In the third task, the project planning have been show the way to prepare the schedule the number of employee should have in this project. In the last task, it is the evaluation of two projects – “Project 1” and “Project 2”. The calculation...
Words: 4198 - Pages: 17
...The Production Function for Wilson Company By using the EViews software, we get the result below by using Least Square method: Dependent Variable: Y | | | Method: Least Squares | | | Date: 06/18/12 Time: 03:24 | | Sample: 1 15 | | | Included observations: 15 | | | | | | | | | | | | Variable | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | C | -130.0086 | 129.8538 | -1.001192 | 0.3365 | L | 0.359450 | 0.245593 | 1.463601 | 0.1690 | K | 0.027607 | 0.006051 | 4.562114 | 0.0007 | | | | | | | | | | | R-squared | 0.838938 | Mean dependent var | 640.3800 | Adjusted R-squared | 0.812094 | S.D. dependent var | 227.9139 | S.E. of regression | 98.79645 | Akaike info criterion | 12.20086 | Sum squared resid | 117128.9 | Schwarz criterion | 12.34247 | Log likelihood | -88.50643 | F-statistic | 31.25263 | Durbin-Watson stat | 1.458880 | Prob(F-statistic) | 0.000017 | | | | | | | | | | | 1. In standard form the estimated Cobb-Douglas equation is written as: Q= α Lβ1 Kβ2 The multiplicative exponential Cobb-Douglass Function can be estimated as a linear regression relation by taking logarithm: Log Q = log α + β1 log L + β2 log K Therefore: log(y) = -130.0086 + 0.359450*log(L) + 0.027607*log(K) The output elasticity of capital is 0.027607 and the output elasticity of labor is 0.359450. 2...
Words: 387 - Pages: 2
...產經一B 403540981 高子翔 「毒」家新聞：Q彈、久煮不爛的真相-順丁烯二酸酐化製澱粉 報導內容(相關資料統整): 2013年5月13日，爆發「毒澱粉事件」（後來行政院更名為違法食品添加物事件）。此事件重創台灣食品業，媒體報導衛生署食藥局早在今年2月就接獲舉報，但調查到5月才公布，指衛生署隱匿吃案。衛生署食藥局局長康照洲憤怒駁斥並非事實。他說，今年2月初食品組同仁在會議上聽聞有澱粉添加違法物質，當時連是什麼都不清楚，但已著手調查，期間從建立檢驗方法、市面抽驗、產品下架、追查源頭，直到掌握事證立即對外公布，外界指隱匿並非事實。 康局長表示，食藥局同仁警覺性很高，一得知立即著手調查，經過不斷研究，3月15日建立檢驗方法，18日調查市面上74件產品，檢出粉圓、黑輪、粄條等五件含有順丁烯二酸，立即著手要求相關業者將產品下架回收，確保產品不會流入市面，期間也持續追蹤。康局長表示若沒有掌握實質證據就對外公布廠商，恐怕會牽涉到司法問題；且擔心一旦消息公布，很多廠商可能會藏匿相關產品、證據，增加調查困難，所以才沒有在一開始就公布。 由於從獲得訊息到對外發布，期間長達三個月，外界認為衛生署可能誤判情勢，延誤新聞發布才導致這起食品安全事件如雪球般愈滾愈大，還讓毒澱粉更流向國外。 針對順丁烯二酸酐化製澱粉事件，食品藥物管理局聯合各地衛生局持續查察，稽查進度如下：日前涉案之澱粉包括地瓜粉、蕃薯粉、酥炸粉、黑輪粉、清粉、澄粉或粗粉等，受影響之市售產品包括粉圓、芋圓類、板條、魚肉煉製品類（關東煮、黑輪）、肉圓、豆花及粉粿等。以上經查證屬實之違規產品，已由轄區衛生局勒令下架停止販售，並限期退回上游經銷業者或製造廠，由衛生局儘速銷毀。截至(2013,5,28)，違規產品回收、封存、銷毀共計約239公噸。 何謂順丁烯二酸和順丁烯二酸酐： 「順丁烯二酸酐」又名馬來酸酐或水蘋果酸酐，常簡稱順酐。遇水變成「順丁烯二酸」；「順丁烯二酸」加熱去水後，又可變回成「順丁烯二酸酐」。若「順丁烯二酸酐」與澱粉或蛋白質作用，會產生多樣化合物，結果非常複雜莫測。 我國法律不允許「順丁烯二酸酐」被添加到食品中，不法業者仍將它添加到澱粉中以增加食品的彈性、黏稠度與穩定度，從而產生Q彈口感、久煮不爛、常溫下可防腐等特性。 至於「順丁烯二酸」，有些合法的食品添加物便含有微量的這種物質。未成熟的蘋果、蕃茄、櫻桃都含有微量的「順丁烯二酸」。製藥業用它來增加藥劑的穩定度。在印度，它被添加到食物、茶葉、橘汁、糖漿以及運動飲料等以增加芳香風味。在生物領域，它屬於微生物可分解的物質，也不會在生物體內聚集殘留，没有致癌性，亦無使基因變異的毒性。因此，微量的「順丁烯二酸」不危害人類健康。 總之，順丁烯二酸和其酸酐都不是核准的食品添加物，但美國及歐盟有限度的允許使用順丁烯二酸酐在和食品直接或間接接觸的包材中，美國也允許將順丁烯二酸用為化妝品中的酸鹼調和劑。 以產業經濟角度分析此事件對社會之衝擊： 食品是生活在社會中最重要的一環，每個人每天一定多多少少會攝取到，除非只吃單純無調理，只用水燙過的「食物」，但如此一來，我們將會吃的了無新意，吃飯就不再是種享受，而是一種填飽肚子的例行公事，生活中也少了種樂事。既然食品對社會而言如此重要，為什麼不肖商人們還要透過違法添加物來謀取暴利呢？而當人們得知自己吃了多年的食品竟然是危害健康的毒藥，又會作何感想呢？民眾會抵制、拒買？還是過了一段時間後全忘了，讓不肖商人們再度用創新的毒藥來毒害人們呢？這些都是值得我們省思的問題。 ...
Words: 278 - Pages: 2
...Using AIU’s survey responses from the AIU data set, complete the following requirements in the form of a 3-page report: TEST #1: Regression Analysis- Benefits & Intrinsic Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Copy and paste the results of the output to your report in Microsoft Word. Create a graph with the trendline displayed the regression. Copy and paste the results of the output to your report in Microsoft Word. TEST #2: Regression Analysis- Benefits & Extrinsic Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the EXTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Copy and paste the results of the output to your report in Microsoft Word. Create a graph with the trendline displayed for the regression. Copy and paste the results of the output to your report in Microsoft Word. TEST #3: Regression Analysis- Benefits & Overall Job Satisfaction Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the...
Words: 356 - Pages: 2
...Department of Economics, Spring 2016 Practice Midterm Questions (No Solution will be Provided) 1. Suppose the data generating process (the true relationship) is y = Xβ + ε, where E[ε|X] = 0, E[εε |X] = σ 2 I n ; and X includes an intercept term. You do not observe the data set Z = [y X]. Instead you observe 150 15 50 Z Z = 15 25 0 50 0 100 2 Compute the least squares estimators β, s2 , R2 and RAdj (the adjusted R2 ). Is there anything to be gained by observing the full data set? 2. Suppose you have the simple regression model with no intercept: yi = xi β+ i for i = 1, 2. Suppose further that the true value of β is 1, the values of xi observed in the sample are x1 = 2 and x2 = 3, and the distribution of i is Pr( i = −2) = Pr( i = 2) = 1/2 with 1 independent of 2 . (a) Find the least squares estimator of β. (b) What is it mean and variance? Is it BLUE? (c) Consider the alternative estimator β ∗ = y /¯, where y is the sample mean ¯ x ¯ of yi and x is the sample mean of xi . What is the mean and variance of ¯ β ∗ ? Is it unbiased? (d) Which estimator is more efficient, the least squares estimator or β ∗ ? 3. Suppose x1 , x2 . . . xn is an independent but not identically distributed random sample from a population with E[xi ] = µ and Var[xi ] = σ 2 /i for i = 1, 2, . . . , n. Consider the following class of estimators for the population mean µ: n µ= ˆ ci xi where c1 , . . . , c n are constants i=1 Each sequence {c1 , c2 ,...
Words: 1769 - Pages: 8
...2 Size 850 1450 1085 1232 718 1485 1136 726 700 956 1100 1285 1985 1369 1175 1225 1245 1259 1150 896 1361 1040 755 1000 1200 Rent 950 1600 1200 1500 950 1700 1650 935 875 1150 1400 1650 2300 1800 1400 1450 1100 1700 1200 1150 1600 1650 1200 800 1750 A real estate company in downtown Miami would like to be able to predict the monthly rental cost for apartments, based on the size of the apartment, as defined by square footage. A sample of 25 apartments in a particular residential neighborhood was chosen. Q-2a: Construct a scatter plot of rent/size. Q-2b: Find the equation of the least squares regression line that models the relationship between square footage and rental amount and interpret the meaning of the coefficients. Q-2c: Predict the monthly rent for an apartment with 1000 square feet. Q-2d: Explain why it would not be appropriate to use the model to predict the monthly rent for apartments that have 500 square feet. Q-2e: You are considering signing a lease for an apartment in this residential neighborhood. You are deciding between two apartments, one with 1,000 square feet that rents for $1,275 and the other with 1,200 square fee that rents for $1,425. Which is a better deal? Explain. Page 1 2 Page 2 2 Q-2a Rent vs. Size 2500 2000 Rent in $ 1500 1000 500 0 0 500 1000 1500 2000 2500 Size in sq. feet y = 1.0651x + 177.12 R² = 0.7226 Q-2b Regression equation = (slope) (x) + (y-intercept) = (1.0651) (x) + (177.12) The slope is 1.0651. This means the...
Words: 461 - Pages: 2
...SOWK 2502 Simple Regression Analysis Example Refer to the example given in the example from the previous lecture on correlations. Having established that there is a strong correlation between weeks in an assertiveness training program and scores on the Assertiveness Scale, Sheila is now interested in being able to predict scores on assertiveness based on number of weeks in the program. Using the same data set, she was able to create an equation which would allow her to make this prediction. This equation is called the Least-Squares Regression Equation. Client weeks score Mary 1 30 June 2 30 Lynn 3 40 Shelly 3 50 Liz 4 60 Carol 5 60 Debbie 5 60 Anne 7 90 Sue 8 80 Helen 8 90 X Y X² Y² XY 46 590 266 39300 3190 Least Squares Regression Equation Y1 = a + b(X) Y1 = predicted Y value from a particular X value a = The point where the regression line would intersect the Y axis, b = The slope of the line, where the amount of change in Y is directly related to amount of change in x. x = A selected value of the predictor variable used to predict the value of the outcome variable Slope Formula: b = N XY - (X)( Y) N X2 - (X)2 Where: b = Slope N = Number of cases XY = Sum of xy column X2 = Sum of x2 column Y2 = Sum of y2 column ...
Words: 406 - Pages: 2
