Johnathan Mun

Real options analysis : tools and techniques for valuing strategic investments and decisions

List of Figures.Chapter Summaries.PART ONE: Theory.Chapter 1: A New Paradigm?Chapter 2: Traditional Valuation Approaches.Chapter 3: Real Options Analysis.Chapter 4: The Real Options Process.Chapter 5: Real Options, Financial Options, Monte Carlo Simulation, and Optimization.PART TWO: Application.Chapter 6: Behind the Scenes.Chapter 7: Real Options Models.Chapter 8: Additional Issues in Real Options.PART THREE: Software Applications.Chapter 9: Introduction to the Real Options Valuations Super Lattice Software and Risk Simulator Software.Chapter 10: Real Options Valuation Application Cases.Chapter 11: Real Options Case Studies.Chapter 12: Results Interpretation and Presentation.Case Studies and Problems in Real Options.Answer to Chapter Questions.Notes.About the CD-ROM.Index.

The Contrarian/Overreaction Hypothesis: An analysis of the US and Canadian stock markets

Abstract The Contrarian/Overreaction Hypothesis implies simultaneously buying (long) previous losers and selling (short) previous winners in order to realize excess returns. The conventional wisdom is that extreme previous losers are undervalued due to investor overreaction possibly instigated by some adverse news and events. Given adequate time, previous losers will outperform the market. Conversely, the overvalued previous extreme winners will underperform the market in subsequent periods. This paper investigates the Contrarian/Overreaction Hypothesis as proposed by DeBondt and Thaler (1985, 1987) using a non-parametric methodology with a multi-factor asset pricing model, within both the US and the Canadian stock markets. Results from risk-adjusted, non-parametric, multi-factor bootstrap-simulated estimates show that, for the US, short-term and intermediate-term contrarian portfolios yield significant excess returns above the market. For the Canadian market, the intermediate-term contrarian portfolio works best.

Tests of the Contrarian Investment Strategy Evidence from the French and German stock markets

Abstract The Contrarian Investment Strategy (CIS) implies simultaneously buying previous losers and selling previous winners. This paper examines the CIS as first proposed by DeBondt and Thaler (1985) (Journal of Finance 40, 793–808) in an effort to expand and complement existing research. Results from our risk-adjusted, nonparametric, multifactor, bootstrap-simulated estimates show that, for both the French and German stock markets, short-term contrarian portfolios work best. Overall, the highest contrarian profits are obtained in the short run and the profits decrease over time. In addition, higher returns are not correlated to increases in the risk coefficients, which is consistent with investor overreaction.

The contrarian investment strategy: additional evidence

This paper tests the contrarian investment strategy, which predicts that stocks that consistently underperform (outperform) the market would in subsequent periods outperform (underperform) those stocks that have previously outperformed (underperformed) the market, using a revised nonparameteric estimator of excess returns and risk coefficients, specified in a time-varying risk multi-factor CAPM model. The conventional parametric approach is used as the control estimator for comparing the effectiveness of this nonparametric approach. Using bootstrap simulations, conventional CAPM estimates reveal that there exists a significant price reversal effect between the formation and test periods, as did the nonparametric estimates. However, one striking difference was that the nonparametric approach revealed more conservative but still significant estimates than did conventional parametric approaches. The multi-factor model reveals weaker results of price reversals and the results dissipate over time. Therefore, the contrarian strategy is only weakly supported and it is concluded that, ceteris paribus, the nonparametric approach yields significantly better estimates than do parametric approaches in estimating the parameters of both the single-factor and multi-factor CAPM.

Real Options and Monte Carlo Simulation versus Traditional DCF Valuation in Layman's Terms

Publisher Summary This chapter provides a contrast between traditional valuation methods and the new generation of valuation analytics, namely real options analysis and Monte Carlo simulation. In addition, it briefly illuminates the advantages as well as the pitfalls of using each methodology. This chapter highlights that a real options analysis is not an equation or a set of equations. It is both an analytical process and a decision analysis thought process, which leads to the second takeaway, that 50% of the value of real options is simply thinking about it. Another 25% comes from generating the models and getting the right numbers, and the remaining 25% of the value of real options is explaining the results and insights to senior management. It also provides the traditional views of valuation; value is defined as the single time-value discounted number that is representative of all future net profitability. In contrast, the market price of an asset may or may not be identical to its value.

Human Capital valuation and return of investment on corporate education

This paper presents the Attitude, Skills, Knowledge, and Experience-Knowledge Value Added (ASKE-KVA) methodology developed from the designed Individual Technical Competence (ITC) of a value chain to assess changes in the Human Capital of a company. It is based on the Knowledge Value Added (KVA) method, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key Process. This variable creates a relationship between the companys financial results and the resources used in each of the business processes. The KVA method uses an indicator that measures the result of knowledge per unit (K@m), which transforms costs and investments in the same unit. The ASKE-KVA methodology expands the previous concept, using fuzzy logic to measure the flow of knowledge associated with each ITC and, therefore, making it possible to obtain the return on investment of a particular business process.

Capturing the Strategic Flexibility of Investment Decisions Through Real Options Analysis

Business conditions are fraught with uncertainty and risks. These uncertainties hold with them valuable information. When uncertainty and risk become resolved or known through the passage of time, action, and events, decision makers can make the appropriate midcourse corrections through a change in business decisions and strategies. Real Options analysis incorporates this learning and flexibility model, akin to having a strategic road map, whereas traditional analyses that neglect this managerial flexibility and risk will grossly undervalue certain projects and strategies as well as lead to bad choices and decisions.

A Combined Lexicographic Average Rank Approach for Evaluating Uncertain Multi-indicator Matrices with Risk Metrics

This chapter presents a combined approach for evaluating resource planning projects considering a multicriteria decision-making process. The approach is based on a multi-indicator matrix with three synthetic attributes that take into account several criteria such as (1) economic and financial elements (attribute: base ranking); (2) uncertainty propagation (attribute: probability); and (3) risk evaluation (attribute: compliance). The final evaluation is derived by using a combined approach based on a nonparametric aggregation rule using the concept of average rank for attributes 1 and 2; a simple procedure for score assignment for attribute3; and a lexicographic decision-making rule. In addition, a preliminary analysis of the alternatives is performed by using Hasse diagrams. An application to resource planning projects illustrates the proposed approach.

Fuzzy inference systems, ASKE, knowledge value added, and Monte Carlo risk simulation for evaluating intangible human capital investments

This paper presents the ASKE-Risk method, coupled with Fuzzy Inference Systems, and Monte Carlo Risk Simulation to measure and prioritize Individual Technical Competence of a value chain to assess changes in the human capital of a company. ASKE is an extension of the method Knowledge Value Added, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key process, creating a relationship between the companys financial results and the resources used in each of the business processes.

Strategic Real Options Analysis

This chapter provides the basics of strategic real options and how it is used in various industries. It explains why only running simulations, forecasting, and optimization are not sufficient in a comprehensive risk management paradigm. Real options analysis, when applied appropriately, allows you to value risk, creating strategies to mitigate risk and showing how to position yourself to take advantage of risk. In the banking world of credit risk and credit engineering, strategic real options can be useful in setting up different options strategies and credit portfolios with different baskets of instruments to hedge certain types of risks, to evaluate each of the pathways or instrument combinations to determine the optimal and best strategy to execute. Real options are useful not only in valuing a firm through its strategic business options but also as a strategic business tool in capital investment decisions.