Corporate and Project Finance Modeling: Theory and Practice (Wiley Finance)
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A clear and comprehensive guide to financial modeling and valuation with extensive case studies and practice exercises
Corporate and Project Finance Modeling takes a clear, coherent approach to a complex and technical topic. Written by a globally-recognized financial and economic consultant, this book provides a thorough explanation of financial modeling and analysis while describing the practical application of newly-developed techniques. Theoretical discussion, case studies and step-by-step guides allow readers to master many difficult modeling problems and also explain how to build highly structured models from the ground up. The companion website includes downloadable examples, templates, and hundreds of exercises that allow readers to immediately apply the complex ideas discussed.
Financial valuation is an in-depth process, involving both objective and subjective parameters. Precise modeling is critical, and thorough, accurate analysis is what bridges the gap from model to value. This book allows readers to gain a true mastery of the principles underlying financial modeling and valuation by helping them to:
- Develop flexible and accurate valuation analysis incorporating cash flow waterfalls, depreciation and retirements, updates for new historic periods, and dynamic presentation of scenario and sensitivity analysis;
- Build customized spreadsheet functions that solve circular logic arising in project and corporate valuation without cumbersome copy and paste macros;
- Derive accurate measures of normalized cash flow and implied valuation multiples that account for asset life, changing growth, taxes, varying returns and cost of capital;
- Incorporate stochastic analysis with alternative time series equations and Monte Carlo simulation without add-ins;
- Understand valuation effects of debt sizing, sculpting, project funding, re-financing, holding periods and credit enhancements.
Corporate and Project Finance Modeling provides comprehensive guidance and extensive explanation, making it essential reading for anyone in the field.
P/E Ratio with Value Drivers To derive the P/E or the EV/EBITDA ratio that theoretically would exist with changing growth rates and returns, transition factors can be included in the analysis. When variables have time differentiation such as the short-term and long-term values shown in Figure 34.3, it is generally not reasonable to assume that the change in the variable suddenly happens in one single year. Instead, the variable gradually moves from the short-term rate to the long-term rate.
that reflect prospective industry supply and demand (potential surplus capacity) and conversion of capacity, demand, and market share parameters into forecasts of revenues, expenses, and capital expenditures. Flexible incorporation of new historical financial data to accept updated financial statements when future financial data become available. Modeling projected depreciation expenses that accurately reflects asset retirements, deferred taxes, and net operating losses. Computing stable
the individual families through putting two countries together. You could also make an analogy to the European Union where synergies are supposed to improve an integrated system. An integrated merger model typically compares earnings per share and credit statistics in the scenario where a merger takes place to a different scenario without a merger. The prospective earnings of the merged company depend on how much is paid for the acquisition and how many synergies are generated from changing
items in valuation analysis, the efficacy of building a theoretical model that simulates accounting and cash flow elements over a long-term period is demonstrated. The theoretical modeling approach is used to derive simple formulas for applying the half-year convention with varying costs of capital in discounted cash flow models and to explain how valuation errors occur from things like incorrectly assuming that accumulated deferred tax should be treated like debt. A central idea in this part of
encompass a whole lot of different analyses, ranging from simply graphing an economic driver of your model and observing how that variable affects cash flows and value, to computing the probability distribution of cash flows using time series equations and Monte Carlo simulations. The different sorts of risk analysis can be classified according to their mathematical and programming complexity. At one end of the risk assessment scale is a purely qualitative list of risks that may include some