Does learning by observing matter for M&As? International evidence from the insurance industry

ABSTRACT

This paper examines the role of learning by observing in insurance mergers and acquisitions (M&As) by employing an international sample across 18 countries spanning the years 1990 to 2014. The empirical results support a view of semi-strong market efficiency: the long-run improvements in financial performance and risk profiles are significantly related to past M&As in the insurance industry. Moreover, we find evidence of more accurate stock market predictions of long-run financial performance and risk profiles if there are more recent insurance merger activities. This paper provides a new explanation for the controversy observed in the failure of insurance M&As to create value.

KEYWORDS:

• Insurance M&As
• learning by observing
• semi-strong market efficiency

JEL CLASSIFICATION:

• G22
• G34

Acknowledgments

We are grateful to Jianzhong Tan and Fariborz Moshirian, and our colleagues for generous help with an earlier version of this paper. Any remaining errors are ours.

Liang would like to thank the National Natural Science Foundation of China for its financial support (Grant No. 71762005); Miao would like to thank the Fundamental Research Funds for the Central Universities (Grant No. 2017CDJSK02YJ06，2020CDSKXYJG007) for its financial support; Li also would like to thank the National Natural Science Foundation of China for its financial support (Grant No. 71873058).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. By contrast, learning by doing argues that the difficulty in valuing insurance M&As will decline in time as insurance companies learn by participating in more cases themselves.

2. We calculate the percentage changes in ROE and ROA associated with a 10% increase in LBYO(3) as follows:

$\mathrm{\Delta }ROE=\left(0.2174-0.0109\ast 14.604\right)\ast 1.652\ast 0.1/0.1313=0.0732$

$\mathrm{\Delta }ROA=\left(0.0279-0.0014\ast 14.604\right)\ast 1.652\ast 0.1/0.0338=0.0364$

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Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [2017CDJSK02YJ06]; National Natural Science Foundation of China [71873058]; National Natural Science Foundation of China [71762005]; Fundamental Research Funds for the Central Universities in China [2020CDSKXYJG007].