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Real Options: Does Theory Meet Practice?

Real Options: Does Theory Meet Practice?. Professor Alexander J. Triantis. Evolution of RO: Theory and Practice. Theory Over 750 papers during the last 20 years Accelerating research productivity Interdisciplinary research area Practice Took off in the mid-1990s RO usage at around 10%?

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Real Options: Does Theory Meet Practice?

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  1. Real Options:Does Theory Meet Practice? Professor Alexander J. Triantis

  2. Evolution of RO: Theory and Practice • Theory • Over 750 papers during the last 20 years • Accelerating research productivity • Interdisciplinary research area • Practice • Took off in the mid-1990s • RO usage at around 10%? • High attrition rate?

  3. New Insights and a New Vocabulary • Key insights: • Reactive and proactive management • Redesigning processes • Transacting in flexibility • Risk-return issue is complex • Management • Transformational insights • Insights confirmed, and new vocabulary

  4. Killer Aps • Natural Resources, R&D, Manufacturing, M&A, and Infrastructure • Some common characteristics • Large investments, low/no up-front cash flow • High uncertainty, but available data • Structured series of stages • Engineers and scientists

  5. Real Options in the Crosshairs • The internet bubble and Enron • “Old Wine, New Bottles” • Need to model reality, not perfection • The “extreme sport” view of real options RO

  6. Agenda for Research • Perfecting the models of perfection • Splitting options • Modeling managerial behavior • Developing heuristics • Valuing and managing the firm

  7. Perfecting the Models of Perfection • Modeling and Estimating Distributions • mean-reversion, jumps, stochastic parameters • Pricing risk • commodity-based applications • underlying project comparables • Powerful Computational Techniques

  8. Splitting options • Most real options not held exclusively or completely by a single company • Split across competitors • wide range of assumptions and models • Split along value chain • contract design to get to first-best solution

  9. Modeling Managerial Behavior • Tools don’t make decisions, people do! • Two key issues • Cognitive biases • Managerial incentives • Estimate sub-optimal behavior • Alter the behavior • Good luck vs. good decisions? • RO Twist: Flexibility can be misused

  10. Developing Heuristics • Accuracy vs. Simplicity • Simpler models are more likely to be used • Complex models can be used as benchmarks • Heuristics • NPV w. WACC; higher hurdle rate • Enables the technology transfer • Software - framing, computations, graphs

  11. Valuing and managing the firm • Managers respond to analysts’ metrics • All the other four pieces need to be in place • Modeling project interactions • Effects of capital structure and risk management • Goals • Internal management and valuation • External valuation

  12. Going Forward • Adoption of real options • organizational factors • quality and simplicity of tool • Real options  Capital budgeting • NPV special case • no longer a “supplementary” tool • Responding to critiques essential to bridging gap between theory and practice

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