Tag: Enterprise Tech and Data

Pricing Insights: Analyzing quote data across global credit markets | Insights | Bloomberg Professional Services
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Measuring quote and trade reference differences
To address this, we looked at 4 markets – U.S. Investment Grade, U.S. High Yield, EUR Investment Grade, and GBP Investment Grade, as they are among the most liquid credit markets in the world.

For the U.S. markets, we used TRACE as our trade source. For EUR and GBP markets, we used Bloomberg data as our source. We considered trades that are >250,000 (local currency) from August 2025, and after applying certain other filters and simplifying assumptions to keep the analysis succinct and viable, the remaining trades served as our collective “ground truth” for benchmarking.

Next, we calculated the difference between three benchmarks that were observable just prior to each trade, and the trade level itself:

IBVAL – pricing source available on the Bloomberg Terminal via PCS (IBVL) and for delivery through Bloomberg’s real-time market data feed, B-PIPE. It makes use of both quote and recent trade data to produce a trade level reference price and to help answer the question: If this bond were to trade right now, where would it most likely trade? It is explicitly targeting trade expectations.

CBBT – rules-based composite price which filters available executable quote levels in real-time to produce context for trading. It does not take trade information into account.

Median quote – a simple quote-based composite that reflects the median of available quote levels, without applying additional filters or weighting.

At charts presented below (Charts 1-4) those differences are aggregated and displayed as distributions (boxplots) by market. The Y-Axis is a measure of that difference in price. Positive results (the upper half of the chart) suggest that a trade was tighter than (inside, or better than) the benchmark level, or conversely, the benchmark level was wider than the trade level.

Negative numbers (the lower half of the chart) suggest that the trade was wider than (outside, or worse than) the benchmark, or conversely, the benchmark level was tighter than the trade level. The 0 line means that a trade level and benchmark level were the same.

The boxplot itself captures the distribution of differences, shown as the 25th and 75th percentiles (box), as well as the 10th and 90th percentiles (whiskers). The 50th percentile (median) is shown as a light gray line through the box.
How quote and trade benchmarks perform across global credit markets

Based on the U.S. Investment Grade results shown in Chart 1, we can observe how each benchmark performs versus traded levels. We can see IBVAL’s 50th percentile difference falls almost exactly on the 0 line, suggesting that its performance is tightly coupled with trade levels. This is by design, as IBVAL is intended to be a trade reference price for pre-trade workflows. Analogous values for CBBT (13.5 cents) and Median Quote (9 cents) are consistent with expectations for quote composites, which are away from traded levels.

Viewed slightly differently, since IBVAL’s light blue box is right on top of the 0 line, it is considered centered on the traded market; trades are as likely to be slightly too tight as too wide. For CBBT, its dark blue box is fully above the 0 line and we see 92% of trades occur inside (tighter than) this benchmark’s bid/ask. For the Median Quote, its purple box is also fully above the 0 line and we see 82% of trades occur inside (tighter than) this benchmark’s bid/ask.

Chart 2 (U.S. High Yield), Chart 3 (EUR Investment Grade), and Chart 4 (GBP Investment Grade) tell similar stories. Though the specifics match their respective market dynamics, in each case, we see IBVAL centered on the traded market and quote composites shifted away from traded levels.

How Bloomberg solutions can help with price discovery

Quote data and reference prices remain critical tools for traders to access real-time information on far more bonds than might trade in any period of time. The availability of a trade reference price such as IBVAL helps strengthen the pre-trade process, even if local dynamics might vary across markets. This enables traders to make use of a consistent set of tools across their global credit portfolio.

Interested to learn more about using IBVAL for your pricing needs? Click here.
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January 5, 2026
Where enterprise data is headed in 2026 | Insights | Bloomberg Professional Services

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The rise of interoperable and multi-cloud data infrastructure

The move to the cloud has been one of the key trends in in the financial services space over the last decade, but companies are now finding that optimal solutions require integration of multiple cloud platforms, says Neill Clark, Managing Director and Head of State Street Associates EMEA. “Five years ago, I would have said the key is to get all of your data on a cloud – ideally a single cloud – and once you’ve done that, you’ve got a set of cloud-native tools and you’re good to go,” says Clark.

“Now the notion of a multi-cloud strategy plus on-prem makes more sense. The reality is everyone ended up multi-cloud by default because they couldn’t get to one cloud. Now it’s the right choice to make, because new tools are coming out all the time and computing costs vary.”

Colette Garcia, Global Head of Enterprise Data Real Time Content at Bloomberg, confirms that this approach aligns with how firms are striving to balance flexibility and precision in their data strategies. “That resonates with us – getting you the data wherever you need it, cloud-agnostic, on-prem-agnostic. It’s the quality of the data and being able to deliver it wherever you need it,” she says.

Integration of AI into investment and research workflows

Another growing trend experts point to is the integration of agentic AI into core areas of business, including investment research and portfolio management. The goal is not to eliminate human insight and judgment, but to enhance it through automated data retrieval.

According to Grégoire Dooms, Head of Data Research & Development at Systematica, AI enriches the information sets that analysts and portfolio managers can incorporate into their decision-making. “AI has completely lowered the bar for access and scale of processing unstructured data – text data,” says Dooms. “It is enabling us to build feature-extraction pipelines that are sector-specific or asset-specific and scale them tremendously.”

State Street’s Clark adds that broader data plus natural language processing tools have significantly enhanced his organization’s research product. “We’ve seen significant metric improvements: 60% better measurement of some macro criteria, 20–30% better prediction outcomes in certain use cases,” he comments. “It’s a meaningful revision in how you can access unstructured data.”

Dawn of a new era for explainable and real-time data access

AI capabilities have revolutionized the relationship between financial services organizations and their clients, allowing firms to share data and insights with clients in real-time.  “We’re experimenting with open-architecture data sharing with our clients—with no barriers at all,” said State Street’s Clark.

“You’ve still got to assemble the control around it, with real-time access to data at a moment of your choosing, in a format of your choosing, integrated and delivered in a way that you can integrate with your other data sets. That feels like the way we’ll be exchanging information with our clients in the future, somehow.”

Notably, as AI’s capabilities grow, companies have to continually upgrade the guardrails that ensure regulatory compliance and ethical practices, turning strong data strategy and governance into a competitive advantage. And in a fast-approaching future where AI-enabled models may make investment decisions without human input, the need for these controls becomes even more critical.

Keeping up with a fast-changing environment

Technological capabilities are changing so fast that it’s hard to predict what AI will look like in a month, let alone a year or five years out. However, experts agree that technology already spreading into many aspects of the financial services industry would only become more ubiquitous.

“It’s not really about who’s going to use AI and who’s not, and who’s going to get left behind,” says Bloomberg’s McManus. The question really becomes: Who’s going to use it in the most intelligent way? Who’s going to be thoughtful, measured, and really understand how to derive real value from the AI they develop.

Explore how Bloomberg is using AI to deliver actionable insights that empower you to move faster, work smarter and achieve better results here. Learn more about Bloomberg Enterprise Tech & Data solutions here.

Insights in this article are based on panels and fireside discussions at the Enterprise Tech & Data Summit held in London in November 2025.

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December 29, 2025
Data Spotlight: Dividend forecasts, market signals & more | Insights | Bloomberg Professional Services

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1. Short interest data to enhance fundamental signal

Factor investing can be strengthened by looking beyond traditional fundamentals to gain a clearer understanding of market trends. In this analysis, we explore how factor investing can be strengthened by looking beyond traditional fundamentals to gain a clearer understanding of market behavior. To do so, we incorporate short-interest data from S3 Partners, delivered through its collaboration with Bloomberg Enterprise Data. This dataset provides point-in-time coverage back to 2015 for more than 62,000 companies globally across a range of proprietary metrics.

Our analysis begins with a standard quality factor—consensus estimates for Return on Equity (ROE)—to identify high-quality companies. We then combine this with S3 short-interest metrics to highlight companies with more favorable market positioning. The study universe is the Bloomberg B500 Index over the past 10 years.

Each S3 variable is transformed into a daily-change factor, with bearish movements defined according to financial intuition: negative changes for Short Interest Availability and positive changes for all other measures. These bearish signals are intersected with the sector-level bottom quintile of ROE to construct the short-leg screen, applied point-in-time to the B500 constituents.

The long side maintains full, unfiltered exposure to the index. A standalone ROE backtest serves as a benchmark, allowing us to assess whether short-interest dynamics offer incremental value beyond fundamentals alone.

Chart 1 illustrates how combining Short-Interest Borrowing Cost with ROE can help uncover potential market opportunities.

Building on the results from the Borrow Cost + ROE signal shown in the Chart 1, the second chart broadens the perspective to evaluate all seven short-interest overlays within the same framework. While the initial analysis illustrated how a single signal can influence cumulative performance over time, this cross-sectional view compares the end-of-period outcomes for each combined factor as of 31 December 2024.

Applying the same long-index/short-screen methodology—where a bearish short-interest move is added on top of the bottom-quantile ROE filter—reveals a consistent pattern: six of the seven short-interest dynamics generated higher cumulative returns than the ROE-only benchmark.

Themes: Short Interest, Investment
Roles: Equity Portfolio Managers, Quants, Strategists
Bloomberg Datasets: S3 Partners Short Interest Data, Company Financials, Estimates and Pricing Point-in-Time

2. The value of dividend forecasts in modern investment decisions

Dividends, especially dividend forecasts, have long played a vital role in trading strategies, portfolio management and risk management practices in both sell-side and buy-side institutions. This has become even more relevant for institutional investors seeking to invest in markets that have been recently supported by dividend distribution policies, such as China.

A robust, forward-looking dividend forecast model with regulatory overlays to anticipate how companies are likely to adjust their dividend payouts could provide an edge in navigating today’s fast-changing financial markets and help investors make well-informed investment decisions.

In this study, we first look at how Bloomberg’s Dividend Forecast (BDVD) data can help with derivatives pricing. For derivatives desks, accurate dividend forecasts are essential because dividends directly affect option pricing, index futures, and structured products. Derivatives traders can adjust option fair values based on forecasted dividends rather than flat or assumed yields.

Chart 1 shows the index points difference for various A share and H share indices, representing the difference between projected and realized dividend. Our results show that Bloomberg dividend forecast could maintain an accuracy within roughly 10 basis points of the index level for a variety of China Indices.

For Buy-Side investors, Bloomberg’s Dividend Forecast (BDVD) data can provide additional insight to support dividend-focused strategies. In our analysis of a monthly-rebalance backtest from 2020 to 2025, companies in the top quintile of forecasted three year dividend growth exhibited higher cumulative returns than those ranked by three-year actual dividend growth.

This suggests that the market may place greater emphasis on the potential for dividend growth than on realized dividend payouts. Bloomberg’s dividend forecast data can offer portfolio managers deeper visibility into expected dividend trends, supporting more informed portfolio management decisions.

Themes: Quantitative Trading, Alpha Generation
Roles: Portfolio Managers, Quantitative Researchers, Derivatives Traders
Bloomberg Datasets: Dividend Forecast

Intraday liquidity for accurate backtests

When performing backtests, quantitative researchers often treat the closing auction price as an executable level. This approach implicitly assumes that sufficient liquidity is available in the auction to support execution at that price. There is extensive evidence that market impact cannot be ignored, as it varies significantly with trade size, market capitalization, and time of day, for example, see Direct Estimation of Equity Market Impact (Almgren et al). In practice, trading with minimal market impact during the closing auction typically requires that the order size represent less than 5% of the auction’s total volume.

As illustrated in Chart 1, smaller-cap companies may exhibit closing auction volumes that are insufficient to support execution without market impact. More realistic backtesting results may be achieved by incorporating pricing from different intraday windows, particularly those characterized by higher volume and lower volatility. For this purpose, we consider the volume-weighted average price (VWAP) observed during each selected session.

To support more effective execution, traders should take into account the available trading volume throughout the day, as this can also contribute to more realistic simulation results, especially in the context of trading smaller capitalizations. Chart 2 shows the distribution of available trading volume (right axis) across market-cap segments, alongside the realized 5-minute volatility (left axis) for each session. We observe that trading volume tends to be higher in the afternoon, while volatility is typically elevated in the morning around the market open. These patterns suggest that execution may be more favorable during the afternoon sessions.

This type of analysis enables tick-data users to conduct more granular assessments and can help refine the implementation aspects of their trading strategies, thereby supporting an improved risk-return balance.

Themes: Transaction Costs, Liquidity
Roles: Equity Portfolio Managers, Quantitative Researchers, Traders
Bloomberg Datasets: Tick History

How can we help?

Bloomberg’s Enterprise Investment Research Data product suite provides end-to-end solutions to power research workflows. Solutions include Company Financials, Estimates, Pricing and Point in Time Data, Operating Segment Fundamentals Data and Industry Specific Company KPIs and Estimates Data products, covering a broad universe of companies and providing deep actionable insights. This product suite also includes Quant Pricing with cross-asset Tick History and Bars. Additional solutions such as Geographic Segment Fundamentals Data, Company Segments and Deep Estimates Data and Pharma Products & Brands Data products will be available in 2025. All of these data solutions are interoperable and can be seamlessly connected with other datasets, including alternative data, and are available through a number of delivery mechanisms, including in the Cloud and via API. More information on these solutions can be found here.

Bloomberg Data License provides billions of data points daily spanning Reference, ESG, Pricing, Risk, Regulation, Fundamentals, Estimates, Historical data and more to help you streamline operations and discover new investment opportunities. Data License content aligns with the data on the Bloomberg Terminal to support investment workflows consistently and at scale across your enterprise.

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Bloomberg

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December 5, 2025
How companies integrate private market data at scale? | Insights | Bloomberg Professional Services

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What’s driving the push to integrate private market data with public market workflows? How are investors and asset managers addressing persistent data frictions to create a unified view across portfolios?

This episode of Market Dialogues features Leila Sadiq, Global Head of Enterprise Data Content at Bloomberg, in discussion with Todd Hirsch, Head of Private Capital at Point72, Mark Neely, Director of Alternative Investments at GenTrust, and Avi Turetsky, Partner and Head of the Quantitative Research Group at Ares, on how private markets are evolving toward greater transparency, valuation consistency, and data connectivity.

Source: Enterprise Data & Tech Summit, October 16, 2025, New York

The Market Dialogues podcast series provides access to curated, thought-provoking discussions from Bloomberg global events. It offers in-depth insights from experts on key trends and themes driving the markets today and beyond.

Discover more conversations in the Market Dialogues series here.

Featured insights from this episode of Market Dialogues:

On technology changing valuation transparency

Todd Hirsch: Technology has enabled us to have much greater frequency of inputs for valuations. Whether they’re public or private… I think you can have a better sense of how often you want to mark your positions and how frequently you need to adjust those marks based on the inputs you have.

When new information comes in, it’s important that it gets incorporated. So if a building sells down the street and you now know there’s a new comparable, you can incorporate that in real time. The frequency of adjustments on the private side today is much better than it has ever been, and transparency for investors continues to improve as that frequency increases, and the supporting data and analytics become stronger.*

On the data gap between public and private companies

Avi Turetsky: We know that public markets show meaningfully higher vol[atility] than private market NAVs. We know the valuations are different: public market valuations represent marginal trades, while private market valuations are appraisal-based. We have a pretty good sense of the relationship between the two.

But whether privately owned companies are actually more stable than publicly traded companies, if you’re looking at revenue, EBITDA, or cash flows, as far as I know, no one knows the answer to that question…The prices are more stable… But whether the revenues are more stable to EBTIDA, no one knows because no one’s been able to get the data on large enough scale to my best knowledge.

On managing allocation across private and public assets

Mark Neely: I focus a lot of my time on the allocation model for clients, and it’s always challenging because we’re constantly comparing public and private markets and they don’t really compare. You look at private equity over a trailing two-year return, and it doesn’t track public equities over the last 10 years

Clients will say, “The public markets are really strong, I want to go into private,” and I’ll say, “Okay, but let’s look at what the EBITDA multiples are that private markets are purchasing at. What are things being marked at?” That transparency, or lack of it, makes it very challenging to take advantage of dislocations or to reallocate capital between private and public markets, or up and down the capital stack in direct lending, Broadly Syndicated Loans (BSLs), and term loans.”

*Quotations have been edited for brevity and clarity.

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Bloomberg

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November 4, 2025
How is investment research transformed by AI? | Insights | Bloomberg Professional Services

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How are investment firms rethinking data architecture to support advanced analytics and responsibly apply AI at scale?

At Bloomberg’s 2025 Enterprise Data & Tech Summit in New York, Angana Jacob, Global Head of Research Data at Bloomberg, spoke with Carson Boneck, Chief Data Officer at Balyasny Asset Management, C.J. Jaskoll, Chief Technology Officer at Russell Investments, and Nan Xiao, Chief Technology and Data Officer at Greenland Capital Management, about how investment firms are building AI-ready infrastructure, harmonizing data systems, and looking at ROI of their AI investments.

Featured insights from the discussion panel:

On AI transforming investment firms

Carson Boneck: What AI allows us to do is extract certain heuristics that can then be emulated and replicated. In a platform like ours, we have so many great investors. I think a lot of firms, including ours, in many places, are at the task level, but we could really help them improve these tasks. We can write research reports by using the deep research agent and compiling all the various information… My hypothesis is that firms like ours are going to get completely transformed [by AI] and the firms that win are those that are able to do the best job at using AI to extract the heuristics of the very best investors.*

On measuring AI investment ROI

C.J. Jaskoll: Gen AI is new, but change management is not. For all change at every buy-side firm I’ve ever been at, there are really two major ways that I think about ROI. Number one: operational efficiency. That means avoiding a trade error, reducing costs, and reducing risk. All of those things can be tangible and quantified. If you are able to quantify that, then you can pay for the platform you’re building or that you’ve bought.

The second one, which I think our industry has and almost no other industry has, is generating alpha. I was talking to a friend last week who asked the same question, and he said, “How do I bring this data into my firm?” I said, “One trade at your firm will pay for the entire platform and three developers.” But is ROI actually needed right now? I think we’re still in the honeymoon period. I don’t think many companies are actually thinking about ROI.

On scaling research with agentic AI

Nan Xiao: We’ve deployed [solutions that are in] production already, and a lot of things in experiments. We have research agents who do multi-steps research and come up with conviction, and go from there to generate sizing, suggestions, trading ideas, and essentially simulate outcomes. Those goals become proposals to PMs. A possible use case will also be… you can ask [AI] to think [about] things differently. Instead of having one research analyst, it’s like [having] five research analysts thinking from different angles. As a result, giving you five different proposals that you can…pick which you would like to go for or combine.

*Quotations have been edited for brevity and clarity.

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Bloomberg

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October 30, 2025
Data Spotlight: Revenue surprises, tariffs impact & more | Insights | Bloomberg Professional Services

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1. Tariffs: Using supply chain and facilities data to evaluate market shifts in North America

In an earlier study, we looked at the two weeks following the 2024 U.S. election and found that companies with U.S.-centric supply chains outperformed their globally exposed peers. That analysis highlighted how supply chain data can reveal investor sentiment during periods of policy change.

In this expanded study, we extend the analysis through July 2025 and across North America markets to capture the effects of the tariff war. Using Bloomberg’s Supply Chain and Facilities datasets, we grouped companies of North America based on presence of US operational exposure or not and examined how performance diverged since the US elections and around key trade announcements.

Chart 1 shows the overall average US operational exposure for a selection of Bloomberg Indices for North America. Outside the US, Canada and Mexico are quite exposed countries to the U.S. making them sensitive to any change of trade policy.

Focusing first on U.S. domiciled companies, Chart 2 shows relative performance of companies with high exposure to the U.S. versus those with low US exposure for companies of the Bloomberg US 1000 Index (B1000) comprised of the largest 1,000 market capitalization in the US. Interestingly, companies with very high U.S. exposure have been performing well from the US Election to April 2025 as the Policy of the Administration has supported US based industries. However after April 2, Liberation Day tariffs, we note a peak and a reversal of this movement – probably highlighting the administration willing to make deals that can be profitable to US companies with global footprint.

In addition to U.S. companies, we have examined the rest of North America’s largest markets (Canada and Mexico). Chart 3 summarizes the cumulative performance of companies exposed to the US against those without US exposure: it appears that U.S. trade war is translating into negative equity returns for companies in their neighbor doing business with them.

Looking beyond North America, we observe a consistent trend globally (Chart 4).

Themes: Macro Investing, Tariff
Roles: Equity Portfolio Managers, Quants, Strategists
Bloomberg Datasets: Supply Chain, Facilities

2. Tracking when guidance moves markets: the Japanese case

During each earnings season, companies release actual financial results and often provide forward-looking guidance for upcoming quarters or the full fiscal year. While markets – and especially systematic players – have traditionally focused on the difference between reported earnings and consensus expectations because of a lack of availability of company guidance in a machine readable format, our research underscores the increasing importance of monitoring guidance surprises — instances where a company’s outlook materially deviates from market forecasts.

To explore this further, we use Bloomberg’s Company Financials, Estimates and Pricing Point-in-Time dataset to examine the frequency of earnings guidance issuance across various regional indices (Chart 1). The findings reveal that companies in Japan are significantly more likely to provide forward EPS guidance compared to their counterparts in the U.S., China, and Europe — highlighting a notable regional difference in corporate disclosure practices.

We further used the data from the Japanese equity market to examine how equity markets respond to earnings guidance surprises — defined as the difference between a company’s issued EPS guidance and the consensus EPS estimate for the next fiscal year.

Our findings (Chart 2) show that positive guidance surprises tend to yield immediate next-day positive performance, with the magnitude of the surprise closely correlated to the size of the price move. In contrast, negative guidance surprises tend to trigger immediate declines in stock price — even when reported results exceed expectations.

This shows that investor sentiment can be more sensitive to forward-looking outlook than to trailing performance, with guidance acting as a forward-looking shock that reshapes market expectations and valuations.

Bloomberg Company Financials, Estimates and Pricing Point-in-Time product provides a comprehensive, point-in-time history of company-reported metrics, consensus estimates, and management guidance as well as pricing information. This data enables investors to backtest stock performance accurately around earnings releases, helping investors understand how actuals, consensus estimates, and company-issued guidance interact to drive market reactions.

Themes: Quantitative Trading, Alpha Generation
Roles: Equity Portfolio Managers, Quantitative Researchers, Traders
Bloomberg Datasets: Company Financials, Estimates and Pricing Point-in-Time

3. Analyzing transaction data analytics and estimates to anticipate earnings surprises

Analysts estimates set investor expectations for a company’s performance each period, and earnings surprises often trigger significant stock price movements. If company performance trends can be evaluated ahead of earnings release, it may create opportunities to identify and respond to surprise-driven price actions. Using Bloomberg Second Measure’s near real-time transaction data (available on a 3-day lag via feeds), investors can gain early insights into company performance well before official reports. When combined with consensus estimates from Company Financials, Estimates and Pricing Point-in-Time dataset, it empowers investors to build actionable trading strategies.

In our study, looking at a quarterly rebalanced backtest from Q2 2020 to Q1 2025 we see that companies in the top quintile of revenue surprises—where transaction data analytics from Bloomberg Second Measure show stronger sales than market expectations—generated higher cumulative returns than those in the bottom quintile.

The Quintile 1 basket delivered robust long-term performance, while the Quintile 5 basket (representing the most negative surprises) showed lower performance. A long–short strategy that takes a long position in Quintile 1 and shorts Quintile 5 produced modest but consistent gains, reinforcing the idea that upside surprises offer a stronger signal than downside disappointments.

As shown in Chart 2, there are a variety of sectors covered in this analysis. This type of analysis can be refined based on a dedicated sector analysis: indeed this type of strategy may perform differently according to the industry.

These results underscore the value of alternative data might have in anticipating market-moving fundamentals before official disclosures.

Themes: Equity Fundamentals, Alpha Generation
Roles: Equity Portfolio Managers, Quantitative Researchers, Traders
Bloomberg Datasets: Company Financials, Estimates and Pricing Point-in-Time, Bloomberg Second Measure

How can we help?

Bloomberg’s Enterprise Investment Research Data product suite provides end-to-end solutions to power research workflows. Solutions include Company Financials, Estimates, Pricing and Point in Time Data, Operating Segment Fundamentals Data and Industry Specific Company KPIs and Estimates Data products, covering a broad universe of companies and providing deep actionable insights. This product suite also includes Quant Pricing with cross-asset Tick History and Bars. Additional solutions such as Geographic Segment Fundamentals Data, Company Segments and Deep Estimates Data and Pharma Products & Brands Data products will be available in 2025. All of these data solutions are interoperable and can be seamlessly connected with other datasets, including alternative data, and are available through a number of delivery mechanisms, including in the Cloud and via API. More information on these solutions can be found here.

Bloomberg Data License provides billions of data points daily spanning Reference, ESG, Pricing, Risk, Regulation, Fundamentals, Estimates, Historical data and more to help you streamline operations and discover new investment opportunities. Data License content aligns with the data on the Bloomberg Terminal to support investment workflows consistently and at scale across your enterprise.

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Bloomberg

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September 23, 2025
IFRS 9 SPPI test and rising data needs: Sustainability Linked Bonds in focus | Insights | Bloomberg Professional Services
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What is the SPPI test under IFRS 9?

Under IFRS 9, the classification of a financial asset depends on:

The business model under which the asset is held, and

The contractual cash flow characteristics of the asset—assessed through the SPPI test.
If the cash flows are solely payments of principal and interest on the principal amount outstanding, the asset may qualify for amortized cost or fair value through other comprehensive income (FVOCI) classification. Otherwise, it must be measured at fair value through profit or loss (FVTPL).

This test ensures that instruments with leverage, equity-like features, or embedded derivatives that alter basic lending characteristics are excluded from amortized cost or FVOCI treatment.

IFRS 9 amendments impact on the SPPI test and coupon features
On 30 May 2024, the International Accounting Standards Board (IASB) issued amendments to IFRS 9 and IFRS 7 following its Post-Implementation Review (PIR) of the classification and measurement requirements. One significant area of clarification was the treatment of financial instruments with coupon adjustment features—a topic that has gained tremendous relevance with the rise of sustainable finance.

These amendments aim to provide clarity on whether and how coupon adjustment features, such as sustainability-linked interest rates, affect the SPPI assessment. Importantly:

The amendments emphasize that contractual terms that vary cash flows based on sustainability targets can still pass the SPPI test—provided they are consistent with a basic lending arrangement.

This includes Sustainability-Linked Bonds (SLBs), even though the standard doesn’t explicitly address them by name.

These changes will become effective for annual reporting periods beginning on or after 1 January 2026, giving firms little time to adapt their systems and methodologies.
Data challenge in SPPI testing for sustainability-linked bonds

While the accounting guidance is now more refined, it introduces an intensified data challenge. In particular, firms must capture detailed contractual terms—often buried in documentation—to determine compliance with the SPPI criteria for a current universe of over 1,100 Sustainability Linked Bonds.

Consider the bond issued by Wienerberger AG on October 4, 2023 (FIGI BBG01JHDRVZ4). This bond offers a 4.875% coupon and matures on October 4, 2028. It incorporates two Key Performance Indicators (KPIs): KPI 1 tracks GHG emission scope 1 & 2 intensity, and KPI 2 measures revenue from building products that support net-zero buildings.

A failure to meet the Sustainability Performance Target (SPT) for KPI 1 will result in a 25 basis point (bps) per annum increase in the coupon, effective October 4, 2027. Similarly, missing SPT 2 will trigger a 50 bps per annum coupon increase on the same date.

The maximum possible coupon step-up of 75 bps is significant, particularly for a bond with a mid-single-digit coupon. This represents a substantial relative impact of approximately 15.4% (0.75%/4.875%), indicating a material change to the bond’s effective yield, which would likely lead to a failure of the SPPI test.

In contrast, the Capital Airport Group bond, issued on August 27, 2021 (FIGI BBG012C4X0K3), included a step-up margin of 10 bps on a 3.45% coupon at issuance. This would likely satisfy the SPPI criteria.

Beyond the initial assessment, a further challenge lies in accurately tracking the observation date and the effective date of any coupon step-up. Once these dates have passed, the securities will meet the SPPI test, as no further step-ups need to be considered.

How can we help?
To prepare for the 2026 implementation date, Bloomberg’s Enterprise Data Regulatory team is reviewing its rule engine for SPPI classification of all instruments with non credit step up features. It will also add new fields that aim to quantify the magnitude and materiality of non credit linked coupon step-ups, for example:

Cumulative basis point step-up that would occur if non-credit events are triggered.

Cumulative coupon step-up as a percentage of the original coupon.
Both figures should be viewed in combination with the SPPI test result (IFRS9_SPPI_TEST) and attribute (IFRS9_SPPI_ATTRIBUTE)
Notably, firms can automate and scale their SPPI determination not only at the time of issuance but also throughout its duration.

Bloomberg’s Regulatory Data Solutions are available via Data License for scalable enterprise-wide use through Bloomberg’s ready-to-use data website, data.Bloomberg.com and can be delivered via SFTP, REST API or into a cloud environment.

To learn more about Bloomberg’s full suite of regulatory data solutions click here.
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Bloomberg

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September 2, 2025