Tag: Equity Indices

Thematic indices have proliferated as asset managers identify new trends that they believe will generate growth. Still, the process of moving from an idea to a rules-based index requires extensive data and analytics. 

Investors typically start with an existing index that broadly reflects their opportunity set, then customize it to target theme exposure more accurately. 

Market data and investment research guide the process. “You want to answer questions like: Which companies are most involved in this secular growth trend? What would this theme look like? What would exposure categories look like?” says Dougherty. “You need to go beyond standard industry classifications and speak with the real industry experts who follow these companies extremely closely to gather their intel and be able to engage that and involve that into our actual theme universes.” 

Media coverage can also inform and shape index composition, says Jigna Gibb, Head of Commodity and Crypto Index Products at Bloomberg. “Document search can be critical to developing themes. Clients need the ability to search across research, news, and transcripts to find out what executives, analysts, or reporters are talking about, and what really matters,” says Gibb. 

Constructing a thematic index can be an iterative process, involving multiple rounds of back testing and refinement. But to capture evolving opportunities, index construction also needs to happen quickly. Specialized index expertise, AI and machine learning tools, and access to extensive, high-quality data all make the process more efficient and effective.  

These analytical capabilities fuel product innovation, enabling index providers and ETF issuers to design new strategies around fast-evolving themes such as European defense and national security. 

European defense budgets have grown sharply over the last decade, as tensions with Russia rise and European governments increase their commitment to funding their own security. The European Council reports that member states’ defense spending rose to €343 billion ($398 billion) in 2024, up by 19% from 2023 levels and by 37% compared to 2021. At the same time, the nature of defense spending has shifted, as governments increase commitments to technology. 

The European Defense Thematic Index reflects changes in the way governments configure their military organizations, employing technologies like AI and unmanned drones as much as traditional hardware like tanks and missiles. Bloomberg’s Global Defense Index currently allocates 77% to industrial companies and 18% to technology firms. 

These thematic indices currently track public equities primarily, but as private markets grow, new tools can incorporate them as well. Dougherty explains, “When I think about building a theme, I’m not necessarily looking just at what a company has disclosed. I’m also looking at whether that company invested in any private companies of late? What are those private companies leaning in on? What does that tell me about where they’re expanding their operational exposure or where they might have some inherent exposure to an innovation boost?” 

Bloomberg supports thematic index creation with advanced analytics and Terminal tools. For instance, the TLTS function avialable via the Terminal can provide powerful insight into the competitive landscape for any given strategy, providing real-time, granular analysis of similar ETFs. Thematic indices with similar names can have significantly different holdings, industry exposures and value/growth characteristics. The TLTS function can easily discern differences, enabling marketing teams to sharpen their sales messages and differentiate their products from competing ETFs. 

Bloomberg data resources can also enhance trading efficiency by providing an instant read on liquidity in both primary and secondary markets for thousands of securities.  Tools like the ETF Strategy Optimizer enables portfolio managers and traders to efficiently and effectively manage large lists of ETF trades in a single click. The tool can analyze multiple ETFs and their underlying holdings, and quickly identify packages of correlated trades that can minimize market impact. 

To see more insights from ETFs in Depth conference, click here. To learn more about Bloomberg Thematic Indices, click here. 

Insights in this article are based on panels and fireside discussions at the Bloomberg ETFs in Depth event held in London in July 2025.   

At first glance, active bond funds seem to deliver

When measured against their stated benchmarks, many active bond funds—particularly in the Aggregate and Corporate segments—appear to outperform with positive median excess returns. However, performance in Government and High Yield categories are less encouraging: most funds underperform, with negative median excess returns and low success rates.

This divergence highlights the unique challenges within each sector. In the Government category, active strategies often rely on duration timing, which our data suggest is difficult to execute successfully and consistently. In High Yield, the dominant approach is fundamental security selection—similar in philosophy to active equity management. Yet, as with equities, our research suggests that this rarely produces durable alpha.

Digging deeper: alpha or systematic risk?

To better understand what’s behind active bond fund performance, we conducted both correlation analysis and multi-factor regressions. The findings were consistent:

• Nearly all active funds take on more credit risk than their benchmarks
• This higher credit exposure is structural and persistent over time
• Most outperformance is attributable to credit risk, after controlling for systematic risk, outperformance largely disappears

For example, rolling 1-year excess returns in Aggregate funds track closely with credit markets. This suggests that systematic credit exposure—not manager skill—is the primary driver of performance.

We quantify this this through regression analysis: regressing equal-weighted active returns on the yield curve and credit factor. For active Aggregate funds, we found an average annual excess return of 50 basis points. However, after adjusting for yield curve and credit factors, only 15 basis points remained as unexplained “alpha.” The rest was attributed almost entirely to credit exposure (47bps), with an R-squared of 88%.

Fund level regressions and attribution confirm the same pattern, both the mean and median fund’s outperformance can be mostly explained by the credit factor exposures. Finally, nearly 90% of active Aggregate funds show persistent positive credit exposure. This widespread, consistent tilt toward credit helps explain why conventional benchmarking tends to overstate true alpha.

The benchmark matters: aligning benchmarks with actual risk

A major contributor to overstated alpha is benchmark misalignment. Many active funds are benchmarked to the Bloomberg U.S. Aggregate Index yet take on significantly more credit risk.

To address this, we introduced “technical benchmarks” tailored to each fund’s actual risk exposures. The results are revealing: success rates fell sharply, and median excess returns turn negative. In short, much of the outperformance disappears once credit exposure is properly accounted for. For example, we find that only 22% of Aggregate funds overperform their technical benchmark over the last 10 years.

Implications for investors and fiduciaries

Here are a few takeaways those evaluating active fixed income strategies:

• Most active performance is beta, not alpha.
  Persistent exposure to credit risk—not dynamic skill—is the primary driver of excess returns.

• Benchmark mismatch can mislead.
  Evaluating funds against risk-aligned technical benchmarks yields a more accurate picture of true value-add.

• Excess credit risk can undermine diversification.
  Many active bond funds take on more credit risk than benchmarks suggest, making them behave more like pro-cyclical assets during market stress—potentially reducing their effectiveness as defensive allocations in multi-asset portfolios.

• Passive strategies can replicate active outcomes.
  Systematic exposures in active funds can often be matched using lower-cost passive or credit-tilted approaches.

While some active fixed income managers may demonstrate genuine skill and deliver alpha, our findings suggest that such cases are the exception rather than the norm. Identifying these managers requires a disciplined approach—supported by rigorous attribution analysis and benchmarks that accurately reflect true risk exposures.

Behind the Benchmark brings transparency to a historically opaque segment of the investment landscape. By aligning benchmarks with actual exposures and isolating systematic drivers, we enable more informed decisions, clearer assessments of manager skill, and stronger alignment with portfolio objectives.

This article was written by Vikas Jain, Index Quant Research and Yingjin Gan, Head of Index Research at Bloomberg and is reproduced from Pensions & Investments.

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