When companies go on acquisition sprees, the headline risk is usually overpaying or failing to integrate. But new research suggests a deeper and less visible danger: default risk that spreads through business networks like a contagion.

In a new study published in The British Accounting Review, ‘Network contagion and post-M&A default risk: Interpretable machine learning evidence from an emerging market’, Macquarie Business School Professor Gary Tian and his colleagues from Guangdong University and Sun Yat-sen University examine what drives corporate defaults after mergers and acquisitions (M&A) in China.

Their conclusion is the single strongest predictor of post-M&A default is not the acquiring firm’s own leverage or profitability, but the default rate of other firms in its industry.

“After a deal, the biggest red flag may be outside the firm. When peer defaults rise in the same industry, the acquirer’s default risk increases sharply,” says Tian.

How it works

Using data from Chinese A-share listed companies between 2010 and 2023, the researchers built a predictive model of default risk following M&A transactions. They compare a traditional logistic regression model with a modern machine learning approach known as Light Gradient Boosting Machine (LightGBM). LightGBM substantially outperforms logistic regression across key metrics, especially in identifying firms that actually go on to default.

But predictive accuracy is only half the story. Machine learning models are often criticised as “black boxes”. To address this, the researchers apply SHapley Additive exPlanations (SHAP), a technique that breaks down how much each variable contributes to a specific prediction. This allows them to open up the model and see what is really driving default risk.

The dominant factor, by a clear margin, is the industry default rate in the previous year. When defaults spike among peer firms, the probability that a given acquirer will default rises sharply. This effect is stronger than traditional firm-level indicators such as leverage, return on assets, or audit opinion.

Broader implications

In China’s relationship-driven business environment, firms are deeply embedded in informal networks known as Guanxi. These networks can provide support in good times, including access to finance through connections and mutual guarantees. But they also create tightly linked credit chains.

When one firm fails, confidence can collapse across the network. Lenders pull back, liquidity dries up, and distress spreads. The study’s findings provide quantitative evidence of this network contagion effect.

“Guanxi can stabilise firms in good times, but in periods of stress it can also accelerate contagion,” says Tian. “That is why monitoring industry-level distress is critical for managing post-M&A default risk.”

The regression analysis reinforces the machine learning results. Even after controlling for firm characteristics and fixed effects, higher industry default rates are strongly associated with greater post-M&A default risk. The effect is amplified for firms more deeply embedded in Guanxi networks, such as those with extensive external guarantees or politically connected directors.

Firm-level fundamentals still matter. Higher leverage increases predicted default risk, while stronger profitability and clean audit opinions reduce it. But these factors are secondary to the broader industry environment. In other words, a firm’s fate after an acquisition is shaped as much by the health of its network as by its own balance sheet.

The study also finds that predictive performance is strongest for smaller, less profitable, and highly leveraged firms, and for those operating in less developed financial or lower-marketisation regions. In these contexts, distress signals are more visible and more tightly linked to financial indicators.

By combining advanced analytics with institutional insight, the research shows that in emerging markets, default risk is not just a matter of numbers – it is also a matter of networks.

Gary Tian is a Professor of Finance, Department of Applied Finance at Macquarie Business School.