...SYRACUSE UNIVERSITY WHITMAN SCHOOL OF MANAGEMENT MSF Program Financial Management (FIN855) Professor David Weinbaum Spring 2014 VALUATION HOMEWORK Please complete all three exercises individually. Please submit your assignment in hard copy in suite 500 no later than 10am on Friday March 21, 2014. Good luck! Exercise 1 You have been asked to value a firm with expected annual after-tax cash flows, before debt payments, of $100 million a year in perpetuity. The firm has a cost of equity of 10%, a market value of equity of $750 million and a market value of debt of $500 million (this is also the book value). The debt is perpetual and the after-tax interest rate on debt is 5%. The company has no non-operating activities. a. Estimate the value of the firm and the value of the equity based upon this value. b. Estimate the value of equity, by discounting the cash flows to equity at the cost of equity. c. Now assume that you had been told that the market value of equity was $850 million and that all of the other information remained unchanged. Answer parts a and b, using these new values. d. In practice, what needs to happen for the two valuation approaches (FCFF and FCFE) to give the same estimate of value? Exercise 2 a. Using the financial statements and other information that you have for MPR, and assuming a 5% perpetual growth rate in the FCFE, value the equity using the FCFE method. b. Does this value equal the estimated value...
Words: 414 - Pages: 2
...Data Mining: Concepts and Techniques (3rd ed.) Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. Adapted for CSE 347-447, Lecture 1b, Spring 2015 1 1 Introduction n n n n n n n n n n Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technologies Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary 2 Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n n n Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube n n We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 3 Evolution of Sciences: New Data Science Era n n Before 1600: Empirical science 1600-1950s: Theoretical science n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding...
Words: 3169 - Pages: 13
...IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 10, OCTOBER 2013 2257 iLike: Bridging the Semantic Gap in Vertical Image Search by Integrating Text and Visual Features Yuxin Chen, Student Member, IEEE, Hariprasad Sampathkumar, Student Member, IEEE, Bo Luo, Member, IEEE Computer Society, and Xue-wen Chen, Senior Member, IEEE Abstract—With the development of Internet and Web 2.0, large-volume multimedia contents have been made available online. It is highly desired to provide easy accessibility to such contents, i.e., efficient and precise retrieval of images that satisfies users’ needs. Toward this goal, content-based image retrieval (CBIR) has been intensively studied in the research community, while text-based search is better adopted in the industry. Both approaches have inherent disadvantages and limitations. Therefore, unlike the great success of text search, web image search engines are still premature. In this paper, we present iLike, a vertical image search engine that integrates both textual and visual features to improve retrieval performance. We bridge the semantic gap by capturing the meaning of each text term in the visual feature space, and reweight visual features according to their significance to the query terms. We also bridge the user intention gap because we are able to infer the “visual meanings” behind the textual queries. Last but not least, we provide a visual thesaurus, which is generated from the statistical similarity...
Words: 11319 - Pages: 46
