Slowdown Fears and Revenue Geography Move Markets on Tariffs

President Trump's tariff announcement on April 2 sparked a dramatic sell-off across global equities, reflecting heightened investor fear over economic disruptions as well as policy and geopolitical uncertainty. The MSCI ACWI Investable Market Index, a gauge for global equities, dropped 10.8% in dollar terms wiping out more than USD 9 trillion in market value — the largest three-day decline since the COVID-19 pandemic.

In this blog post, we provide insights into the market’s reaction through the lens of the MSCI equity factor models and geographic exposure of company revenues.^[1] Both U.S. and developed ex-U.S. equities bore the initial brunt of investor concerns, with each market factor having declined over 10% in the first three days since the announcement. Within DM, Switzerland, which generates more than 30% of its revenues in the U.S., posted one of the steepest losses. Although Singapore has limited direct revenue exposure to the U.S., it suffered, as well, likely due to its critical role as a transit and processing hub, making it especially vulnerable to disruptions caused by a potential global trade slowdown.

In contrast, Norway, which earns less than 15% of its revenues from the U.S., exhibited relative strength. Emerging markets (EM) were less affected, declining just over 8%, with Brazil, South Korea and India notably outperforming their regional peers.

Developed markets led equity sell-off

Sector returns further highlight investors' growing fears of a global economic slowdown. Energy-related industries — including energy equipment and services, oil and gas exploration and integrated oil companies — as well as precious and diversified metals, experienced the sharpest losses, which were in general a lot more pronounced in the U.S. than in other markets. In contrast, defensive industries such as health-care services, food and staples retailing and telecommunication services continued to reflect the stability shown in past market dislocations. Mortgage-finance firms gained ground, likely buoyed by expectations of lower interest rates leading to refinancing activity in a slowing economy.

Interestingly, industries directly vulnerable to tariffs — including automobiles, consumer durables, pharmaceuticals, semiconductors and communications equipment — were less affected. The subdued reaction in these industries suggests that tariff risks may have already been anticipated and priced into the market. These industries were among the weakest performers in February and March 2025, each declining by approximately 3.5% to 6.5%, reflecting earlier reactions to tariff announcements on Feb. 1.^[2]

Defensive industries led cyclicals as slowdown fears mount

Within factor categories, the sell-off was largely confined to beta, liquidity and residual volatility, underscoring this sell-off as a core “risk-off” trade. Factors such as value, growth and traditional quality metrics — including profitability, leverage and earnings variability — had more muted responses. Conversely, the short-interest factor was the strongest performer, possibly driven by the continued unwinding of the tech trade.

Beta tumbles, short interest gains

To assess market reaction beyond traditional risk factors, and to link to our recent work on mapping tariff risk, we grouped companies into four buckets according to their revenue exposure (U.S. exposure for non-U.S. firms and international exposure for U.S. firms) and calculated median stock-specific returns for each grouping. Stock-specific returns represent the portion of a security’s return not attributable to systematic risk factors — such as country, market, industry or style — and are therefore idiosyncratic to individual companies.

Our findings revealed a strong relationship between companies' geographic revenue exposure and the resulting price impact, underscoring the importance investors placed on firms’ revenue sources in reacting to the announced tariffs.

Investor reaction accounted for firms’ revenue exposures

The level and universality of the tariff announcements have alarmed markets. While our style analysis illustrated the contribution of a “risk-off” trade by investors, the MSCI factor models and economic-exposure data also quantified differentiation between industries where the new tariffs were a surprise and where they had been partly priced. Just as economic-exposure data proved useful for highlighting tariff risks and conducting scenario analysis ahead of the tariff announcement, it has also been effective in explaining stock-specific returns during the subsequent market slide.

The author would like to thank Anurag Kumar for his contributions to this post.