Kalman filter approach to real options with active learning

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.