Abstract
We study the optimal investment policy of a firm facing both technological and cash-flow uncertainty. At any point in time, the firm can decide to invest in a standalone technology or to wait for a technological breakthrough. Breakthroughs occur when market conditions become favorable enough, exceeding a certain threshold value that is ex-ante unknown to the firm. A microfoundation for this assumption is that a breakthrough occurs when the share of the surplus from the new technology accruing to its developer is high enough to cover her privately observed cost. We show that the relevant Markov state variables for the firm’s optimal investment policy are the current market conditions and their current historic maximum, and that the firm optimally invests in the stand-alone technology only when market conditions deteriorate enough after reaching a maximum. Empirically, investments in new technologies requiring the active cooperation of developers should thus take place in booms, whereas investments in state-of-the-art technologies should take place in busts. Moreover, the required return for investing in the stand-alone technology is always higher than if this were the only available technology and can take arbitrarily large values following certain histories. Finally, a decrease in development costs, or an increase in the value of the new technology, makes the firm more prone to bear downside risk and to delay investment in the stand-alone technology.
Keywords
Investment Timing; Technological Uncertainty; Optimal Stopping;
JEL codes
- C61: Optimization Techniques • Programming Models • Dynamic Analysis
- D25:
- D83: Search • Learning • Information and Knowledge • Communication • Belief
Replaces
Jean-Paul Décamps, Fabien Gensbittel, and Thomas Mariotti, “Investment Timing and Technological Breakthroughs”, TSE Working Paper, n. 21-1222, June 2021, revised December 2024.
Reference
Jean-Paul Décamps, Fabien Gensbittel, and Thomas Mariotti, “Investment timing and technological breakthroughs”, Mathematics of Operations Research, 2024, forthcoming.
Published in
Mathematics of Operations Research, 2024, forthcoming