Does M&A activity spin the cycle of energy prices?

Abstract

This research investigates the predictive power of mergers and acquisitions (M&A) activity on returns and volatility in energy commodities from January 1997 to September 2023. Utilizing a novel time-varying robust Granger causality framework, we analyse the dynamic relationship between M&A activity and energy returns and volatility within the global oil and gas (O&G) industry. In addition, we examine the network structure of M&A activity and energy prices across different quantile regimes. We find that M&A activity exhibits significant time-varying forecasting ability for both energy returns and volatility. Specifically, M&A transactions led by oil acquirers, representing deals where both the acquirer and target are within the O&G industry, demonstrate stronger forecasting ability for energy returns than M&A transactions led by acquirers from non-O&G industries. Conversely, M&A activity by non-O&G acquirers shows greater predictive ability for energy volatility. Robustness checks support our main findings. First, our multi-horizon model reveals significant bi-directional causality between M&A activity and energy series for 3 and 6-month forecasting horizons, which affirms a lasting influence on energy returns and volatility. Second, the strength of connectedness at extreme quantiles surpasses that at the median, with its magnitude increasing over the forecasting horizon. Third, our baseline results remain stable across varying rolling window sizes. These findings have important implications for policymakers and investors, suggesting that M&A activity within the O&G industry should be considered when making decisions in the energy market, as it plays a crucial role in predicting the dynamic direction of energy prices.

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Identifiers

ORCID for Martin Enilov: orcid.org/0000-0002-7671-6975

ORCID for Renatas Kizys: orcid.org/0000-0001-9104-1809

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