Data Spotlight: Economic forecasts, leveraged loans & more

This article was written by the Bloomberg Enterprise Investment Research Data team: Frances Shi, Kevin Kwan, Jerome Barkate and Nakul Nair.

Welcome to Data Spotlight, our series showcasing insights derived from Bloomberg’s 8,000+ enterprise datasets available on data.bloomberg.com via Data License.

In this edition, we look at how investors can refine consensus forecasts using economic estimates data and how leveraged loans data can help with increasing portfolio diversification. We also examine how data helps understand the impact of interest rate changes on firms’ debt repayment, depending on their industry.

Looking for our other data-related findings? Explore these recent articles from the Data Spotlight series:

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For more articles in this series click here.

1. Refining consensus forecasts with economist estimates

Economic indicators, while often predictable to a certain extent, can sometimes produce surprising results that have far-reaching consequences across multiple asset classes. When these unexpected shifts occur in key economic indicators, such as GDP growth, inflation rate and employment figures, they can trigger immediate reactions in financial markets, affecting everything from equities to commodities and currencies. For instance, an inflation report that is lower than expected may lead the market to adjust its expectations for Federal Reserve interest rate changes. 

To demonstrate how investors can remain alert for economic surprises in financial markets using data, we use the Economist Estimates dataset to gauge surprise in US industrial production (IP CHNG Index). Looking at accuracy of the consensus (defined by median of all economists surveys) in Chart 1, one might be willing to improve the consensus accuracy.

With access to the individual survey predictions from economists we adopt a simple approach to refining the consensus estimate. This consensus is typically defined as the median of all economist estimates. Our refinement process involves selecting the top five economists based on their historical accuracy. Table 1 illustrates the improved forecast accuracy by displaying both median absolute deviation and directional correctness. As observed, forecast accuracy has improved in most years since 2008. Directional correctness has reached as high as 83%, particularly in 2020 and 2021 when the COVID-19 pandemic disrupted financial markets, leading to significant variability in economist predictions.

It is worth noting that median absolute deviation is determined by calculating the median of the absolute differences between predicted and actual values, while directional correctness is measured as the percentage of times the estimate with Top 5 aligns with the actual surprise in terms of direction.

Prior to the release of each economic indicator, surveys are typically conducted to collect forecasts from economists, analysts, and market participants. The Economist Estimates dataset encompasses approximately 2,500 economic indicators globally, with historical data extending back to 1997. The dataset contains economist estimates along with details about the economist name, their firm, and the survey date. This enables users to gain deeper insights into the consensus estimates for economic indicators at a more granular level. Chart 2 presents a detailed breakdown of the top 20 countries (out of 83) based on the number of economic indicators covered (period 1997-2025).

Theme: Macro Investing
Roles: Quants, Portfolio Managers, Risk Managers, Strategists
Bloomberg Dataset: Economic Estimates

2. Increasing diversification with leveraged loans data

As of February 2025, the US syndicated loan market – a market for loans provided by a group of lenders and structured into deals, which are divided into smaller portions called tranches – has posted a streak of 21 consecutive months of positive returns (the streak ended in March 2025), as evidenced by Chart 1. In an environment where traditional asset classes are unpredictable, it appears that syndicated loans might have quietly emerged as a source of consistent performance and diversification.

Chart 2 demonstrates that by computing a simple efficient frontier based on combining investment in a balanced index, such as Bloomberg US EQ:FI 60:40 Index, and the Bloomberg US Leveraged Loan Index, over the last five years, investors have been able to adjust their level of risk (represented by volatility) without without significantly reducing returns by including leveraged loans.

However, accessing the potential of this market requires more than just headline returns. Making informed, timely decisions in the syndicated loan space requires reliable and comprehensive data. Bloomberg’s Syndicated Loans Reference Data offers deep visibility into loan terms, facility structure and covenant details, allowing investors to better assess opportunities and manage risk with confidence. In markets with limited access and a need for transparency, accurate data is essential for effective analysis. As illustrated by Chart 3, global coverage includes various industries and regions, enabling a complete screening of opportunities in the syndicated loan market.

Themes: Diversification, Multi-Asset
Roles: Portfolio Managers
Bloomberg Dataset: Syndicated Loans Reference Data

3. Identifying equity dislocation driven by interest rate fluctuations

Interest rate changes impact firms’ ability to repay debt, but the effect varies across industries and individual companies. Financial firms are particularly sensitive to interest rate changes due to their loan-based business model. For example, banks’ net interest margin (NIM) tends to worsen when interest rates are cut by Central Banks as the revenue from their lending decreases at a sharper pace than the size of their interest payments.

Since September 2023, the Federal Open Market Committee (FOMC) has met 11 times. The first eight of these meetings kept the interest rate unchanged. The following three all resulted in cuts, with a 0.5% cut on Sep 18, 2024 and a 0.25% on each of Nov 7, 2024 and Dec 18, 2024.

To understand how these decisions have impacted the banks’ risk level, we have plotted the average probability of default (using Bloomberg DRSK) 15 days before each event (Chart 1). Our universe is all the Banks in the following two indices: Bloomberg High Yield Index and Bloomberg Corporate Investment Grade Index.

Chart 1 above shows this evolution of DRSK for firms in our universe, split into two equally sized quantiles. The split is based on the fully loaded total capital ratio, a measure of a bank’s financial strength. Firms with a high ratio fall into quantile 2. This ratio, calculated as total capital divided by risk-weighted assets, assesses overall capital strength.

We observe that firms in both quantiles see their probability of default progressively decrease towards the end of 2023. Dislocation appears in the middle of 2024 when rate cuts become a real possibility, with quantile 2 firm’s default risk rising.

To understand whether this is a localized phenomena around rate events or something the market is differentiating on more broadly, we use fully loaded total capital ratio as a factor and run a backtest from January 2023 to October 2024.

As the analysis shows, the credit-risk/interest rate cut pattern observed above translates well into a full fundamental backtest. We see differentiation between the quantiles starting at the end of 2023 and becoming more pronounced through 2024.

This methodology provides a structural framework for utilizing credit risk indicators and firm-specific key performance indicators to capitalize on future economic environments characterized by fluctuations in interest rates.

Themes: Interest Rates, Credit Risk, Industry Specific Fundamentals
Roles: Credit Analysts, Portfolio Managers
Bloomberg Dataset: Credit Risk Indicators, Industry Specific Company KPIs and Estimates PiT

How can we help?

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