Trade war and tariffs: Anatomy of winners and losers

This article was written by Steve Hou, PhD, Quantitative Researcher at Bloomberg.

Trade wars are back. Underneath rising trade tension lies the most dramatic geopolitical realignment of the global order since perhaps the end of World War II. While the intensity of the trade tensions may ebb and flow from day to day, the underlying economic and political structural drivers may persist and potentially intensify in the years to come. Investors can benefit from understanding the transmission mechanisms of tariffs and trade wars and therefore adapt better to these significant structural changes.

Trade war as an event study

Which companies are likely to win or lose from tariffs and the ongoing trade war? To find out, we created an event study, using “Liberation Day”—April 2, when President Donald Trump announced a broad set of tariffs—as a news shock. We sorted stocks in the Bloomberg US 1000 Price Return Index into quintiles based on their beta-adjusted cumulative excess returns from that day through April 17. The announcement of the tariff package at the beginning of this period provides a high degree of confidence that stock price movements within this short window were primarily attributable to the tariffs.

Next, we constructed equal-weighted portfolios from these quintiles and simulated their returns in two “out-of-sample” periods: the approximately two-year period in Trump’s first term during which he embarked on his first trade war and the roughly two-year runup to the 2024 election, when he was elected to a second term.

A remarkable pattern emerges: The top quintile of stocks (tariff winners) from the event study also did the best during the 2018 trade war period and, curiously, were the top performers in the two years leading up to Trump’s re-election (Fig. 1). Likewise, the tariff losers did the worst during those periods. Moreover, in the “out of sample” periods, the quintiles strikingly perform in order from worst to best, suggesting that the event study identified a latent “tariff factor” affecting the cross-section of stocks.

Both out-of-sample windows preserve quintile ordering but reveal distinct market reactions. In 2018–19, the gap in excess returns is largest between the bottom quintile and the rest, implying that investors chiefly penalized the most tariff-exposed firms. In the pre-election window (Jan 2023–4 Nov 2024), dispersion widens to both tails: well-insulated companies are rewarded while laggards are further punished. A plausible explanation is that firms have shifted supply chains since the 2018 trade war or COVID by onshoring or friend-shoring — so Liberation Day marks an acceleration, rather than reversal, of a trend toward a more de-globalized global order.

Factor footprints

There is a rich set of economic logic behind the tariff winners and losers. By running {TLTS <GO>} and loading the top and bottom quintile portfolios in the Tilts function, we can leverage the Bloomberg MAC3 equity factor risk model to show their factor footprints (Fig. 2). Tariff winners tend to be faster-growing, variable and volatile companies with sector tilts toward technology, industrials, health care and consumer staples (Fig. 3). The reason is likely that tariffs are first and foremost an economic growth shock that affects companies and sectors with the most international trade exposures. It’s therefore unsurprising that the biggest losers from tariffs tend to be highly cyclical and value stocks, especially those with heavy international dependence or poor ability to pass on costs.

Interestingly, the factor footprints of tariff winners don’t simply align with the “quality, growth and low volatility” combination that has been a key driver of secular outperformance in recent years. As discussed in our blog last month, companies with strong pricing power often exhibit quality growth and low beta, which can provide resilience against tariffs. While tariff winners show some overlap with higher growth stocks and defensive sectors like consumer staples and healthcare, the distinction between winners and losers isn’t neatly explained by style factors or sectors. Even within the same sector, tariff sensitivity varies significantly. Instead, tariff beneficiaries tend to be either cyclical, import-dependent sectors with a higher proportion of US revenue (such as industrials, materials, and consumer staples) or service-oriented sectors like communications, where tariffs have a less direct impact.

Domestic shares of revenue supply chains

To further validate the logic behind tariff winners and losers, we examined companies’ revenue sources and supply chain locations. Holding all else equal, we hypothesized that companies with a greater proportion of US-based business and suppliers would demonstrate more resilience to tariffs, potentially even benefiting from increased domestic demand. Using Bloomberg’s company-level revenue segmentation and supply chain linkage data from the Supply Chain (SPLC) function, our analysis confirms that tariff resilience indeed correlates positively with domestic revenue and supply chain shares, as anticipated.

Table 3 compares the median US revenue shares and US supplier shares across quintiles of increasing tariff resilience, segmented by select sectors. Overall, portfolios with greater tariff resilience tend to exhibit higher US revenue shares and a larger proportion of US-based suppliers. However, the relationship appears to be nonlinear, with a plateauing effect beyond a certain level. Moreover, within sectors with adequate data coverage, notable divergences emerge. In sectors intuitively more vulnerable to tariffs, such as consumer discretionary and consumer staples, tariff winners demonstrate higher US revenue and supplier shares. Conversely, in sectors less directly affected by tariffs, like communications, tariff losers show higher US revenue and supplier shares. Our findings are highly consistent with the research from Bloomberg Intelligence on related topic using the same data.

Next steps

This study opens several promising paths for future research and the creation of systematic tariff-sensitive equity portfolios. One potential area of exploration is using natural language processing algorithms to identify tariff shocks as a starting point for building market-implied thematic portfolios. Further inquiry could explore the relationship between tariff resilience and domestic revenue and supply chain shares. Additionally, is it possible to build equity portfolios responsive to the secular onshoring trend using revenue and supply chain data? Finally, a comparison of these potential portfolio construction methods would be valuable. We plan to address these questions in future articles.

Acknowledgment

This article benefited from helpful discussions and comments from Bloomberg colleagues Antonios Lazanas, Allison Stone, Jon Asmundsson, and Gina Martin Adams and the assistance on revenue and supply chain data from Claudio Fontana and Nathaniel Welnhofer.