Optimal information acquisition for eliminating estimation risk

TL;DR

This paper develops a framework for investors to optimally acquire information to reduce estimation risk, deriving closed-form solutions and analyzing how timing and risk preferences influence information value.

Contribution

It introduces a novel utility maximization model for active information acquisition, providing explicit valuation criteria and strategic insights into timing and risk aversion effects.

Findings

Early information acquisition is more valuable for risk reduction.

Lower risk aversion leads to higher propensity for information gathering.

Closed-form solutions for value functions under CARA and CRRA utilities.

Abstract

This paper diverges from previous literature by considering the utility maximization problem in the context of investors having the freedom to actively acquire additional information to mitigate estimation risk. We derive closed-form value functions using CARA and CRRA utility functions and establish a criterion for valuing extra information through certainty equivalence, while also formulating its associated acquisition cost. By strategically employing variational methods, we explore the optimal acquisition of information, taking into account the trade-off between its value and cost. Our findings indicate that acquiring earlier information holds greater worth in eliminating estimation risk and achieving higher utility. Furthermore, we observe that investors with lower risk aversion are more inclined to pursue information acquisition.