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Kalman filter approach to real options with active learning.

Sebastian Sund1, Lars H Sendstad1, Jacco J J Thijssen2

  • 1Department of Industrial Economics and Technology Management, NTNU, 7491 Trondheim, Norway.

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|July 31, 2023
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Summary

This study introduces a decision tool for uncertain market profitability, incorporating learning and risk aversion. Risk aversion accelerates investment but reduces the overall value of learning from market signals over time.

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Area of Science:

  • Decision analysis
  • Financial modeling
  • Technological innovation

Background:

  • New markets from technological innovation create incentives to assess profitability.
  • Investment decisions involve uncertain profitability and decision-maker risk attitudes.
  • Existing models often do not fully account for the learning process and risk aversion.

Purpose of the Study:

  • To develop a decision-support tool for evaluating investment opportunities with uncertain profitability.
  • To incorporate the impact of learning and risk aversion into the decision-making process.
  • To analyze the optimal timing of investment under uncertainty and learning.

Main Methods:

  • Application of the Kalman filter for time-varying estimation of uncertain processes.
  • Valuation of investment options dependent on estimated process parameters.
  • Focus on linear stochastic processes with normally distributed noise.
  • Numerical analysis to explore decision-maker behavior and option value.

Main Results:

  • The marginal benefit of learning diminishes significantly over time.
  • Optimal investment timing tends to be early in the option holding period.
  • Risk aversion leads to earlier investment decisions.
  • Risk aversion decreases the value of learning and the benefit of waiting for more information.

Conclusions:

  • The developed tool effectively models learning and risk aversion in investment decisions.
  • Early investment is favored, especially for risk-averse individuals, due to diminishing learning benefits.
  • Risk aversion negatively impacts the value derived from learning and observing market signals over time.