Navigating fixed income in 2025: key trends and insights

The global monetary policy hiking cycle that followed the COVID-19 pandemic was broadly synchronized. However, as central banks normalize monetary policies and cut interest rates, regional economic conditions are increasingly diverging.

Exhibit 1: Central bank policy rates in %

In Europe, the European Central Bank, the Bank of England and the Swiss National Bank are expected to cut interest rates further to support economic growth amid a disinflationary trend. The SNB has even indicated the possibility of moving to 0% or negative interest rates again due to the persistent deflationary pressures and a strong Swiss Franc. In contrast, the US Federal Reserve has paused its easing cycle recently, reflecting the resilience of the US economy and the progress towards its 2% inflation target. Meanwhile, the Bank of Japan has recently raised interest rates to the highest level in 17 years and signaled more hikes driven by concerns about rising inflation.

The growing divergence in the pace and extent of rate cuts will likely present attractive relative value opportunities across fixed income markets this year, rewarding investors with active, nimble styles. Diverse central bank policies have increased the appeal of active management, allowing skilled fund managers to exploit varying interest rates to generate alpha.

Beyond the varying approaches of central banks, default probabilities and return forecasts are crucial factors that investors need to consider.

The latest UBS AM Fixed Income Default Study predicts that 2025 defaults will mainly involve large, distressed capital structures. Default rates in developed markets are expected to rise compared to the previous year, but excluding distressed assets, they are significantly lower. Emerging markets are forecasted to have lower default rates than developed markets, driven by stronger economic growth and improved credit conditions.¹

Our total return forecasts for 2025 indicate a positive outlook for various fixed income segments. US high yield is expected to return 6-8%, EUR high yield 4-7%, Asia high yield 7-12%, and EM corporates 6-8%. These projections suggest that despite a more complex environment, the asset class continues to offer promising return prospects for active investors.²

The discussion on active versus passive investment management is evolving, particularly in fixed income. Although passive strategies have become more popular due to their cost-efficiency, liquidity and transparency, the emergence of active ETFs underscores the increasing acknowledgment of the benefits of active management.

Active fixed income ETFs combine the advantages of traditional ETFs with the expertise of active management. Active ETFs offer flexibility to adjust duration, credit quality, and sector exposure in response to market developments. In a market characterized by gradually lower trending yields and heightened volatility, active managers can leverage their expertise to identify mispriced securities, manage risks, and capitalize on market inefficiencies.

According to Morningstar, active ETFs still represent only a small fraction of the overall European ETF market, comprising about 2.5% of total assets under management.³ However, the strong growth momentum, which has quadrupled assets under management in five years to USD 52 billion in 2024, reflects the demand for tailored investment strategies that can adapt to changing market conditions and deliver superior risk-adjusted returns.

Exhibit 2: European active ETFs, asset under management in USD billion

The revival of yield in public fixed income has attracted strong flows into public credit funds recently. This trend is underscored by pension funds de-risking their portfolios amid improvements in their funded status or insurers increasing bond allocations to extend duration in anticipation of easing monetary policy.

Importantly, the growth of public credit markets has not come at the expense of private markets. Institutional investors are increasingly tapping into private credit for diversification and enhanced returns. Preqin forecasts that assets under management at private credit funds will grow from USD 1.5 trillion at the end of 2023 to USD 2.6 trillion in 2029.

We believe investors should adopt a balanced approach in both market segments, carefully considering the advantages and disadvantages. As central banks continue to lower interest rates, public credit markets are anticipated to benefit from enhanced liquidity and attractive relative value opportunities, offering potential for alpha generation amid macroeconomic volatility and geopolitical risks.

Meanwhile, private credit markets are expected to remain resilient due to their structural protections and reduced market volatility. Especially from the perspective of institutional investors, this favors an approach that leverages the liquidity and transparency of public markets while capitalizing on the diversification and enhanced returns offered by private credit.

Artificial intelligence (AI) is revolutionizing the asset management industry; its impact will be profound. AI-driven tools and algorithms can analyze vast amounts of data, identify patterns, and generate insights that were previously unattainable. According to Mercer, more than half of global investment managers are already using AI within their investment strategy or asset class research.

Fixed income managers have arguably been slower than equity investors to adopt AI in their research due to the complex structure of bond markets. Unlike equities, a single company can issue multiple bonds with different currencies, maturities, coupons, covenants, and risks. However, once fixed income managers cut through these complexities, AI should enhance decision-making processes, help optimize asset allocation, and improve risk management.

One example of AI use in fixed income is credit analysis. Credit analysts use machine learning models to analyze vast amounts of structured (financial statements, flows, prices) and unstructured data (news, earnings calls, sentiment) to assess credit risk more accurately and efficiently.

AI can help predict the probability of corporate bond defaults by identifying subtle patterns in issuer fundamentals and macroeconomic trends that traditional models might overlook. This saves time and allows portfolio managers to make more informed investment decisions, optimize risk-adjusted returns and dynamically adjust exposure to changing credit conditions.

Footnotes:

¹ This does not constitute a guarantee by UBS Asset Management.
² These forecasts are not reliable indicators of future performance.
³ Morningstar Direct. Data as of 31 December 2024.