Artificial intelligence and European firms: the cost of market-based financing

News originally published by

November 12, 2026, 14:06 GMT

Prepared by Irene Ferrari, Sofia Gori, Johannes Groß, Fabian Schupp and Sigurd Steffensen

Published as part of the ECB Economic Bulletin, Issue 7/2026.

Artificial intelligence (AI) has been the dominant theme in global financial markets since late 2022, with equities in the technology sector outperforming substantially (Chart A). This outperformance has been especially striking in the United States, led by a small group of hyperscalers, and is evident among European firms that occupy upstream positions in the global AI infrastructure buildout. However, AI’s influence on markets extends beyond a narrow set of infrastructure providers and stretches further back in time. Many firms integrate AI into their businesses, either by developing AI-enhanced products or by adopting AI-enabled processes that improve productivity.^[1] This box therefore introduces a new firm-level measure of AI exposure derived from AI-related discussions in earnings call transcripts. It uses this measure to examine how the AI exposure of European firms is reflected in their equity returns and corporate bond spreads.^[2]

Chart A

Performance of the STOXX Europe 600 and S&P 500 and their technology subsectors

Sources: LSEG and European Central Bank (ECB) calculations.
Notes: The chart shows the levels of European and US benchmark equity indices and their technology subsectors over time. The latest observations are for 15 September 2026.

Firms are classified as AI-exposed when their earnings calls link AI to infrastructure provision, product development, or improvements in business processes and productivity.^[3] These criteria are applied across STOXX Europe 600 constituents, identifying not only firms that supply AI technology but also firms across all sectors that embed AI in their products and operations. The approach also captures the evolving nature of AI: as the technology matures and diffuses across sectors, firms’ disclosures shift accordingly, allowing a measure grounded in contemporaneous statements to track this evolution. This is valuable given the reliance of analysis on timely identification of AI-related news. Accordingly, AI exposure is measured quarterly from 2017 onwards and treated as time-varying, allowing the analysis to control for firm heterogeneity and sector-specific time variation.^[4]

The share of AI-exposed firms has risen markedly since 2022, with AI predominantly mentioned in earnings calls as a source of product innovation and growth (Chart B). 75% of earnings calls mentioning AI refer to product innovation or growth opportunities from an AI-enhanced product offering, while 50% discuss AI-related productivity gains, with some mentioning both. There is clear heterogeneity across sectors and over time: IT firms mentioned AI-related product innovation as far back as 2017, whereas firms in other sectors mainly started to refer to productivity improvements resulting from AI after the launch of ChatGPT in late 2022. This is consistent with the findings of the ECB’s Survey on the Access to Finance of Enterprises in the euro area in June and December 2025, which show more innovation-oriented cases of AI use in the IT sector and greater emphasis on cost reduction in manufacturing, construction, trade and other services.^[5]

Chart B

Share of AI-exposed STOXX Europe 600 firms and breakdown by AI exposure type

Sources: LSEG and ECB calculations.
Notes: The sample includes all STOXX Europe 600 firms and spans from the first quarter of 2017 to the second quarter of 2026. Panel a): quarterly share of earnings calls in which AI is mentioned (left-hand side) and breakdown of earnings calls mentioning AI by exposure type over the sample period (right-hand-side). Panel b): annual share of firms in each Global Industry Classification Standard sector mentioning innovation exposure (left-hand side) and productivity exposure (right-hand side). The 2026 annual share is approximated using the shares for the first and second quarters. The latest observations are for the second quarter of 2026.

Whether AI exposure raises or lowers firms’ financing costs is theoretically ambiguous. AI adoption may boost expected cash flows, lifting equity valuations and reducing perceived credit risk, but only if investors believe that firms are genuinely advancing, and not merely making unsubstantiated claims. On the debt side, AI adoption often requires substantial upfront capital investment concentrated in intangible assets with limited collateral value, potentially raising financing costs. Since these mechanisms operate simultaneously, the analysis examines whether AI exposure is systematically associated with different equity returns and bond financing costs than those faced by firms not exposed to AI.

Panel regression evidence points to an equity return premium of around 1.3 percentage points per quarter for AI-exposed firms (Chart C).^[6] Quarterly stock returns are regressed on AI exposure flags, controlling for firm-level characteristics, earnings surprises, firm and sector-quarter fixed effects, and sector-specific time trends. This ensures that AI-exposed firms are compared with non-exposed firms in the same sector-quarter, netting out sector-wide movements.^[7] The regression results align with descriptive patterns in the data: AI-exposed firms recorded higher median annual stock returns than non-exposed firms in most years (Chart C, panel a) and also tended to trade at higher price/earnings ratios (Chart C, panel b). This is consistent with a longer-duration equity profile, whereby much of the value of AI firms is tied to earnings that are expected to accrue far into the future.

Chart C

Stock returns and price/earnings ratios for AI-exposed and non‑AI‑exposed STOXX Europe 600 firms

Sources: LSEG and ECB calculations.
Notes: Panel a): distribution of annual equity returns, weighted by end-of-year market value. Panel b): distribution of annual price/earnings ratios, weighted by end-of-year market value. The latest observations are for the second quarter of 2026.

a) Annual stock market returns	b) Annual price/earnings ratios
(percentages)	(percentages)

Non-financial corporate bond markets exhibit a broadly similar pattern to equity markets (Chart D). Panel regressions using quarterly option-adjusted spreads over 2019-26, with bond and issuer-specific controls, show that AI exposure corresponds to spreads that are around 5 basis points tighter – a modest but robust link between AI exposure and a lower market-implied cost of debt.^[8] This again aligns with descriptive patterns in the data. AI-exposed issuers consistently exhibit lower spreads than non-exposed issuers, with both the median and interquartile range shifted downward across most years (Chart D, panel a). Exposed firms have also issued a larger share of bonds with maturities exceeding ten years (Chart D, panel b), whereas issuance by non-exposed firms is more concentrated at intermediate maturities. This combination points to more favourable financing conditions, since longer maturities would typically command higher, not lower, spreads.^[9]

Chart D

Corporate bond spreads and bond maturity structure for AI-exposed and non‑AI‑exposed STOXX Europe 600 non-financial corporates

Sources: LSEG, ECB (CSDB, CSEC), ICE, Moody’s Analytics and ECB calculations.
Notes: Panel a): annual distribution of option-adjusted spreads over the swap curve. Spreads are winsorised and weighted by the outstanding amount in euro. The sample covers euro-denominated bonds with a residual maturity of between 1 and 30 years, an issued amount above €1 million, and spreads ranging from ‑100 basis points to 3,500 basis points. Panel b): new bond issuance by maturity bucket as a share of the outstanding amount in euro, with the yield to maturity calculated as the average weighted by the outstanding amount in euro. The sample covers euro-denominated bonds with a residual maturity of between 1 and 30 years and an issued amount above €1 million. The latest observations are for the second quarter of 2026.

The findings in this box suggest that AI exposure is positively reflected in equity and corporate bond pricing across the wider economy in Europe. AI’s association with lower market-based financing costs is more widely distributed than the concentrated equity rally in the IT sector suggests. However, this relationship rests on expectations about the technology’s returns. Should those expectations disappoint, valuations and spreads could reprice abruptly and broadly, extending well beyond the firms driving the AI investment cycle. Further analysis is warranted to establish the potential scale of this repricing and its effect on aggregate financing conditions in the euro area.^[10]

References

Andersson, M., Breckenfelder, J., Corradin, S., Nikolov, K. and Viola, M.A. (2026), “The AI boom: rational enthusiasm or the next dot-com bubble?”, The ECB Blog, ECB, 17 August.

Acemoglu, D. and Restrepo, P. (2020), “Robots and Jobs: Evidence from US Labor Markets”, Journal of Political Economy, Vol. 128, No 6, April.

Babina, T., Fedyk, A., He, A. and Hodson, J. (2024), “Artificial intelligence, firm growth, and product innovation”, Journal of Financial Economics, Vol. 151, January.

Bergeaud, A., González-Torres Fernández, G., Labhard, V. and Sellner, R. (2025), “AI can boost productivity – if firms use it”, The ECB Blog, ECB, 28 March.

Calvino, F., Dernis, H., Samek, L. and Ughi, A. (2024), “A sectoral taxonomy of AI intensity”, OECD Artificial Intelligence Papers, No 30, OECD, December.

Ca’ Zorzi, M., Lopardo, G. and Manu, A.-S. (2025), “Verba volant, transcripta manent: what corporate earnings calls reveal about the AI stock rally”, Working Paper Series, No 3093, ECB, August.

Dias da Silva, A., Lebastard, L. and Sondermann D. (2026), “AI adoption and the productivity promise: what workers report”, The ECB Blog, ECB, 26 August.

Duquerroy, A., Furtuna, O., Rahmouni-Rousseau, I. and Vaz Cruz, L. (2026), “Big tech, big debt: when US tech giants tap the euro area bond market”, The ECB Blog, ECB, 31 August.

Ferrando, A., Lamboglia, S., Rariga, J. and Schmidt, M. (2026), “Adoption and investment in AI across the euro area: insights from harmonised firm-level data”, Occasional Paper Series, No 395, ECB, July.

Ferrari, I., Groß, J., Schupp, F. and Steffensen, S. (2026), “AI Exposure and the Costs of Market-based Financing in Europe: Evidence from Earnings Call Transcripts”, mimeo.

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