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Getting a fixed income optimizer to return a solution is one thing. Knowing how to get a strong one when it pushes back, and reading what it's telling you once it solves, is another. Soft constraints are what make that possible: they let a bound flex at a price you set, so the optimizer keeps finding strong solutions even as conditions tighten.

In fixed income, the strongest returns come from closing the gap between a manager's best ideas and the portfolio that actually gets built. A well-configured optimizer does exactly that: every view and every constraint resolved in a single pass, so that what the manager intends is what the portfolio holds. Getting there has always taken more discipline in fixed income than in equities.

Author: Principal Solutions Engineer, SimCorp Every fixed income optimization setup runs into the same questions: how to encode analyst views into position bounds, when a tracking error bound beats individual characteristic constraints, and how to tell if a constraint earns its place. This article works through the ones practitioners ask most often, from expressing alpha to reading a constraint status report after the optimizer runs.

Using your optimizer to express yield curve views When you have a view on rates, the return depends on getting it into the portfolio at the size you intended. The same optimizer that keeps a portfolio neutral on rates can put a curve view on deliberately and precisely, so a macro call translates into the position you actually meant to take rather than an approximation assembled by hand.

Author: Principal Solutions Engineer, SimCorp This article shows how fixed income optimization builds liquidity filters, inventory caps, and transaction costs into the problem itself, so the trade list a desk gets is one it can actually execute. This article is part of SimCorp's practitioner's guide to fixed income optimization. Read the full whitepaper for the complete framework.

Gareth Morris Head of Portfolio Management and Trading, SimCorp Kate Ryan Head of Product Design, SimCorp Continued investment in the front office as SimCorp One expands unified portfolio intelligence, AI-powered decision support and integrated multi asset trading workflows. Gareth Morris joins as Head of Portfolio Management and Trading from BlackRock Aladdin; Kate Ryan joins as Head of Product Design from S&P Global.

Olivier d'Assier Lead Principal Investment Decision Research SimCorp Part 1 recap: AI demand may be real, but the buildout has become large, leveraged, and infrastructure-dependent. For investors, the question is not only whether AI succeeds. It is whether portfolios are being paid for the financing, concentration, power-delivery, and valuation risks they are taking.

Author: Senior Director Investment Decision Research, SimCorp A global sovereign debt adjustment has begun in Japan and is testing other major borrowers. This note argues that quasi-QE interventions may contain yields temporarily, while sovereign issuance and AI funding crowd out the wider economy. Three Axioma Risk scenarios examine managed, contested and failed intervention scenarios. If governments set the curve and hyperscalers set the spread, everyone else gets the bill.

Core view: AI demand looks real enough. The challenge is no longer proving demand. It is financing, powering and ultimately monetizing one of the largest infrastructure buildouts in modern business history. AI has become one of the few areas of the economy still expanding at full speed. Data-center construction continues to surge. Hyperscalers are spending at levels that would have seemed implausible only a few years ago. Equity investors have rewarded them accordingly.

On-Demand Webinar Adding alpha by shaping beta Fundamental portfolio managers invest on conviction, but even the best stock pickers can find that the way those names are selected can create a portfolio with unintended factor exposures. In many cases, small systematic adjustments to portfolio weights can meaningfully improve outcomes without changing a single underlying call or diluting manager conviction.