Risk analysis has become critical to modern financial planning Financial Forecasting, Analysis and Modelling provides a complete framework of long-term financial forecasts in a practical and accessible way, helping finance professionals include uncertainty in their planning and budgeting process. With thorough coverage of financial statement simulation models and clear, concise implementation instruction, this book guides readers step-by-step through the entire projection plan development process. Readers learn the tools, techniques, and special considerations that increase accuracy and smooth the workflow, and develop a more robust analysis process that improves financial strategy. The companion website provides a complete operational model that can be customised to develop financial projections or a range of other key financial measures, giving readers an immediately-applicable tool to facilitate effective decision-making. In the aftermath of the recent financial crisis, the need for experienced financial modelling professionals has steadily increased as organisations rush to adjust to economic volatility and uncertainty. This book provides the deeper level of understanding needed to develop stronger financial planning, with techniques tailored to real-life situations. Develop long-term projection plans using Excel Use appropriate models to develop a more proactive strategy Apply risk and uncertainty projections more accurately Master the Excel Scenario Manager, Sensitivity Analysis, Monte Carlo Simulation, and more Risk plays a larger role in financial planning than ever before, and possible outcomes must be measured before decisions are made. Uncertainty has become a critical component in financial planning, and accuracy demands it be used appropriately. With special focus on uncertainty in modelling and planning, Financial Forecasting, Analysis and Modelling is a comprehensive guide to the mechanics of modern finance.

Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.

How to interpret and evaluate economic forecasts and the uncertainties inherent in them.

This text summarizes the important new thinking on financial market forecasting and on the statistical modeling of non-stationary series in a clear and readable manner. The emphasis throughout is on real-life examples using data from a wide variety of countries and sources.

Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.

Provides step-by-step instructions for creating financial forecasts with Microsoft Excel 2010 so you can: Assess project feasibility, Fund company cash needs, Determine financial feasibility of a project before committing, Identify problems before they become major.

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.