Analysis of Malaysian IPO market price performance by Lee Kai Liang. Abstract (Summary) An Initial Public Offering (IPO) is the first sale of a company’s common stocks to investors on a public stock exchange. It is widely believed that IPO’s price appears to have relationship with its listing market price. Investor’s general perception shows IPO is under pricing and likely to obtain capital gain if immediately sell their shares in secondary market on the first day of IPO’s trading. However‚ there
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The Impact of Dividend Policy on Shareholders’ Wealth of Selected Major Pharmaceutical Units in India CHAPTER IV ANALYSIS OF DIVIDEND PAY OUT TREND 4.1 Introduction Dividend policy has been a concern of significance in financial literature since inception of Joint Stock Companies. Dividends are commonly defined as the distribution of earnings (past or present) in real assets among the shareholders of the firm in proportion to their ownership.xxxix Dividend policy connotes to the payout policy
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and the pooled time regression method is used to analyze the data. Return model as well as Price model was used to determine the value relevance of financial statements’ information. It revealed that the value relevance of accounting information under the Price model has more explanatory power than Return Model. The empirical results of the study indicate that Earning Per Share (EPS) is the most value relevant variable in this study and it is significant at 0.01 level. Regression of earnings‚ book
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Applying Archetypal Analysis in Marketing Research © Paul Riedesel‚ Action Marketing Research 2008 Archetypal analysis is a mathematical procedure for decomposing a multivariate dataset as a function of a set of underlying archetypes or ideal types. While used for many years in the physical sciences‚ these methods were first introduced to marketing research practitioners by Louviere and Carson at the 1998 Advanced Research Techniques Forum. Little public use has been made in our field since then
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ISSUES IN ACCOUNTING EDUCATION Vol. 26‚ No. 1 2011 pp. 181–200 American Accounting Association DOI: 10.2308/iace.2011.26.1.181 A Case Study on Cost Estimation and Profitability Analysis at Continental Airlines Francisco J. Román ABSTRACT: This case exposes students to the application of regression analyses to be used as a tool pursuant to understanding cost behavior and forecasting future costs using publicly available data from Continental Airlines. Specifically‚ the case focuses on
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A Case Study on Cost Estimation and Profitability Analysis at Continental Airlines Francisco J. Román Introduction In 2008‚ the senior management team at Continental Airlines‚ commanded by Lawrence Kellner‚ the Chairman and Chief Executive Officer‚ convened a special meeting to discuss the firm’s latest quarterly financial results. A bleak situation lay before them. Continental had incurred an operating loss of $71 million dollars—its second consecutive quarterly earnings decline that year
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Reliability Test Results of Indicator Variables Moreover‚ regression analyses of collected data were completed by SPSS software
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slopes of the data in the periods before and after 911 were discovered and both periods are used. The data matches the usual modeling assumptions and thus‚ results are to be expected to be interpreted without contradictions. HD’ Market Rating Analysis (MRA): Jensen’s Alpha ( ) was largely in the positive range. Therefore‚ Home Depot return was greater than the S&P return and HD
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so in the initial analysis‚ we decided not to bin them. Also we observed the correlation of each variable; since we mainly want to make prediction‚ even though some variables are correlated‚ we didn’t eliminate them.Overall‚ we just eliminate several outliers of this data set. 2. Preliminary Models We use many models to make classification and prediction. The three models are multiple linear regression‚ classification tree and neural network. 2.1 Multiple linear regressions Based on
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number of Instagram followers ranged from 0-2122. By using MiniTab‚ we were able to analyze our data thoroughly and through a mathematical lense. For our R^2 value we got 19.2% (.192)‚ which would put our R value at -.443. As for our least-squares regression line‚ MiniTab outputted that the predicted number of Facebook Friends is equal to 588 plus 42.5 times the number of weekly visits to the Jig (Predicted # of Facebook Friends = 588 + 42.5(# of Jig weekly Visits)). We have attached our residual plot
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