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1. |
Record Nr. |
UNISA996250822603316 |
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Titolo |
Clinical medicine insights Gastroenterology |
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Pubbl/distr/stampa |
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[Auckland, N.Z.], : Libertas Academica |
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Thousand Oaks, CA : , : SAGE Publications |
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ISSN |
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Disciplina |
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Soggetti |
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Gastrointestinal system - Diseases |
Gastroenterology |
Gastrointestinal Diseases |
Periodicals. |
Periodical |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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2. |
Record Nr. |
UNINA9910139618203321 |
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Autore |
Fridson Martin S |
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Titolo |
Financial Statement Analysis [[electronic resource] ] : A Practitioner's Guide |
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Pubbl/distr/stampa |
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ISBN |
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1-119-20148-9 |
1-283-17595-9 |
9786613175953 |
1-118-06418-6 |
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Edizione |
[4th ed.] |
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Descrizione fisica |
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1 online resource (400 p.) |
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Collana |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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BUSINESS & ECONOMICS / Finance |
Financial statements |
Ratio analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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FinancialStatementAnalysis; Contents; Preface to Fourth Edition; Acknowledgments; PART ONE Reading between the Lines; CHAPTER 1 The Adversarial Nature of Financial Reporting; The Purpose of Financial Reporting; The Flaws in the Reasoning; Small Profits and Big Baths; Maximizing Growth Expectations; Downplaying Contingencies; The Importance of Being Skeptical; Conclusion; PART TWO The Basic Financial Statements; CHAPTER 2 The Balance Sheet; The Value Problem; Comparability Problems in the Valuation of Financial Assets; Instantaneous Wipeout of Value; How Good Is Goodwill? |
Losing Value the Old-Fashioned WayTrue Equity Is Elusive; Pros and Cons of a Market-Based Equity Figure; The Common Form Balance Sheet; Conclusion; CHAPTER 3 The Income Statement; Making the Numbers Talk; How Real Are the Numbers?; Conclusion; CHAPTER 4 The Statement of Cash Flows; The Cash Flow Statement and the Leveraged Buyout; Analytical Applications; Cash Flow and the Company Life Cycle; The Concept of Financial Flexibility; In Defense of Slack; Conclusion; PART THREE A Closer Look at Profits; CHAPTER 5 What Is |
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Profit?; Bona Fide Profits versus Accounting Profits; What Is Revenue? |
Which Costs Count?How Far Can the Concept Be Stretched?; Conclusion; CHAPTER 6 Revenue Recognition; Channel-Stuffing in the Drug Business; A Second Take on Earnings; Astray on Layaway; Recognizing Membership Fees; A Potpourri of Liberal Revenue Recognition Techniques; Fattening Earnings with Empty Calories; Tardy Disclosure at Halliburton; Managing Earnings with Rainy Day Reserves; Fudging the Numbers: A Systematic Problem; Conclusion; CHAPTER 7 Expense Recognition; Nortel's Deferred Profit Plan; Grasping for Earnings at General Motors; Time-Shifting at Freddie Mac; Conclusion |
CHAPTER 8 The Applications and Limitations of EBITDAEBIT, EBITDA, and Total Enterprise Value; The Role of EBITDA in Credit Analysis; Abusing EBITDA; A More Comprehensive Cash Flow Measure; Working Capital Adds Punch to Cash Flow Analysis; Conclusion; CHAPTER 9 The Reliability of Disclosure and Audits; An Artful Deal; Death Duties; Systematic Problems in Auditing; Conclusion; CHAPTER 10 Mergers-and-Acquisitions Accounting; Maximizing Postacquisition Reported Earnings; Managing Acquisition Dates and Avoiding Restatements; Conclusion; CHAPTER 11 Is Fraud Detectable? |
Telltale Signs of ManipulationFraudsters Know Few Limits; Enron: A Media Sensation; HealthSouth's Excruciating Ordeal; Milk and Other Liquid Assets; Conclusion; PART FOUR Forecasts and Security Analysis; CHAPTER 12 Forecasting Financial Statements; A Typical One-Year Projection; Sensitivity Analysis with Projected Financial Statements; Projecting Financial Flexibility; Pro Forma Financial Statements; Pro Forma Statements for Acquisitions; Multiyear Projections; Conclusion; CHAPTER 13 Credit Analysis; Balance Sheet Ratios; Income Statement Ratios; Statement of Cash Flows Ratios |
Combination Ratios |
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Sommario/riassunto |
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An updated guide to the essential discipline of financial statement analysis In Financial Statement Analysis, Fourth Edition, leading investment authority Martin Fridson returns with Fernando Alvarez to provide the analytical framework you need to scrutinize financial statements, whether you're evaluating a company's stock price or determining valuations for a merger or acquisition. This fully revised and up-to-date Fourth Edition offers fresh information that will help you to evaluate financial statements in today's volatile markets and uncertain economy, and allow yo |
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3. |
Record Nr. |
UNINA9910483789203321 |
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Autore |
Vluymans Sarah |
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Titolo |
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods / / by Sarah Vluymans |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XVIII, 249 p. 23 illus., 10 illus. in color.) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 807 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Classification -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classification of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography. |
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Sommario/riassunto |
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This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields. |
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