This book is an introduction-level text that reviews, discusses, and integrates both theoretical and practical corporate analysis and planning. The field can be divided into five parts: (1) Information and Methodology for Financial Analysis; (2) Alternative Finance Theories and Cost of Capital; (3) Capital Budgeting and Leasing Decisions; (4) Corporate Policies and their Interrelationships; (5) Financial Planning and Forecasting.The theories used and discussed in this book can be grouped into the following classical theoretical areas of corporate finance: (1) Pre-M&M Theory, (2) M&M Theory, (3) CAPM, and (4) Option Pricing Theory (OPT). The interrelationships among these theories are carefully analyzed. Real world examples are used to enrich the learning experience; and alternative planning and forecasting models are used to show how the interdisciplinary approach can be used to make meaningful financial-management decisions.In this third edition, we have extensively updated and expanded the topics of financial analysis, planning and forecasting. New chapters were added, and some chapters combined to present a holistic view of the subject and much of the data revised and updated.

Advances in Quantitative Analysis of Finance and Accounting (New Series) is an annual publication designed to disseminate developments in the quantitative analysis of finance and accounting. The publication is a forum for statistical and quantitative analyses of issues in finance and accounting as well as applications of quantitative methods to problems in financial management, financial accounting, and business management. The objective is to promote interaction between academic research in finance and accounting and applied research in the financial community and the accounting profession. The papers in this volume cover a wide range of topics including corporate finance and debt management, earnings management, equity market, auditing, option pricing theory, and interest rate theory. In this volume there are eleven chapters, five of them are corporate finance and debt management: 1. Liquidity and Adverse Selection: Evidence from the Five-or-Fewer Rule Change; 2. Changing Business Environment and the Value of Relevance of Accounting Information; 3. Pricing Risky Securities in Hidden Markov-Modulated Poisson Processes; 4. An Empirical Assessment of Alternative Dividend Expectation Models; 5. Quantitative Market Risk Disclosure, Bond Default Risk and The Cost of Debt: Why Value At Risk? There are two of the other six chapters which cover interest rate theory: 1. Positive Interest Rates and Yields: Additional Serious Considerations; 2. Collapse of Dimensionality in the Interest Rate Term Structure. The remaining four chapters cover financial analysts earnings forecasts, equity market, auditing, and option pricing theory. These four papers are: 1. Investors’ Apparent Under-weighting of Financial Analysts’ Earnings Forecasts: The Role of Share Price Scaling and Omitted Risk Factors; 2. Predicting Stock Price by Applying the Residual Income Model and Bayesian Statistics; 3. Intertemporal Associations Between Non-Audit Services and Auditors’ Tendency to Allow Discretionary Accruals; 4. Put Option Portfolio Insurance vs. Asset Allocation.

This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

This rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset pricing, corporate finance, options and futures, and conducting financial accounting research. Divided into four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses; the role of binomial, multi-nomial, and log normal distributions in option pricing models; and the application of statistics analyses to risk management. The real-world applications and problems offer students a unique insight into such topics as heteroskedasticity, regression, simultaneous equation models, panel data analysis, time series analysis, and generalized method of moments. Written by leading academics in the quantitative finance field, allows readers to implement the principles behind financial econometrics and statistics through real-world applications and problem sets. This textbook will appeal to a less-served market of upper-undergraduate and graduate students in finance, economics, and statistics. ​

Security Analysis, Portfolio Management, and Financial Derivatives integrates the many topics of modern investment analysis. It provides a balanced presentation of theories, institutions, markets, academic research, and practical applications, and presents both basic concepts and advanced principles. Topic coverage is especially broad: in analyzing securities, the authors look at stocks and bonds, options, futures, foreign exchange, and international securities. The discussion of financial derivatives includes detailed analyses of options, futures, option pricing models, and hedging strategies. A unique chapter on market indices teaches students the basics of index information, calculation, and usage and illustrates the important roles that these indices play in model formation, performance evaluation, investment strategy, and hedging techniques. Complete sections on program trading, portfolio insurance, duration and bond immunization, performance measurements, and the timing of stock selection provide real-world applications of investment theory. In addition, special topics, including equity risk premia, simultaneous-equation approach for security valuation, and Itô's calculus, are also included for advanced students and researchers.

This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

The Encyclopedia of Finance comprehensively covers the broad spectrum of terms and topics relating finance from asset pricing models to option pricing models to risk management and beyond. This third edition is comprised of over 1,300 individual definitions, chapters, appendices and is the most comprehensive and up-to-date resource in the field, integrating the most current terminology, research, theory, and practical applications. It includes 200 new terms and essays; 25 new chapters and four new appendices. Showcasing contributions from an international array of experts, the revised edition of this major reference work is unparalleled in the breadth and depth of its coverage.