Total Portfolio Approach: The Operational Reality

Prior chapters in the Total Portfolio Approach (TPA) series explored the key challenges organizations face and the operational approaches they can take to support a TPA framework. The first two papers established the foundation by examining industry trends driving adoption of TPA and the architectural decisions required to enable it. The subsequent papers explored specific

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2026 Investment Performance, Risk, and Attribution Vendor Landscape

Trends in Performance, Risk, and Attribution Systems Introduction While a few years have passed since Meradia’s last Performance Vendor Landscape article, many of its stated trends still hold true. Data aggregation, quality control, and governance remain top priorities for clients evaluating performance platforms. Intuitive workflows and strong exception management continue to shape how efficiently operations

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After-Tax Benchmarking: Developing an Industry Standard to Level the Playing Field

After-tax benchmarks are increasingly critical for evaluating investment performance for taxable investors. While after-tax portfolio returns are well defined under existing standards, the development of consistent, defensible after-tax benchmark methodologies has lagged, creating challenges for firms seeking to accurately measure tax-aware investment strategies. This paper introduces Meradia’s recommended approach to after-tax benchmarks using a Shadow

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From the Next Generation Operating Model to the AI Operating Model

Executive Summary  AI is no longer a tooling question for investment firms. It is an operating-model question. The firms that win will redesign work, decisions, controls, and talent fast enough to turn AI from scattered pilots into institutional advantage. Building on Meradia’s Next Generation Operating Model (NGOM), this paper introduces the AI Operating Model (AIOM): the management system that converts

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Building Backwards: Delivering Total Portfolio Attribution, Analytics, and Liquidity

At its core, the Total Portfolio Approach (TPA) demands that firms consistently generate three outcomes: total portfolio attribution, integrated analytics, and forward-looking, total portfolio level liquidity forecasting. Together, these form the backbone of informed, portfolio-wide decision-making. Yet many asset owners struggle to produce these outputs in a way that is timely, coherent, and scalable across

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Why Target Operating Models Fail Before the First Mile

For those who know me outside of work, you know I’m a runner and always training for the next race. In distance running, elite athletes don’t start with race day strategy. They start with an honest assessment of current capability. Pace, endurance, recovery, and risk are measured before the plan is set. Organizations rarely apply

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The Investment Data Behind a Total Portfolio Approach

The first paper in this series examined the questions executives inevitably face in the wake of major macroeconomic events: How quickly can we rebalance the portfolio to a new strategic posture without breaching liquidity, leverage, or regulatory constraints? How does a shift in emerging markets impact our portfolio over the next 24 hours? Despite the

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Less Is No Longer More: FactSet’s Performance Solution Rewrites Historical Performance Data Migrations

Less history, less risk. For more than a decade, Meradia approached investment performance implementations with this mantra. Two key assumptions underpinned the “less is more” philosophy: Assumption 1 – Performance implementations become more difficult with longer histories and more granular data. Assumption 2 – The exponential increase in project risk from migrating longer and more

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The Evolution of Meradia’s Approach to Investment Performance Data Migrations

Less history, less risk. For more than a decade, Meradia approached performance implementations with this mantra. Conventional wisdom says to migrate as little historical performance data as possible, because anything more brings unnecessary complexity without a commensurate payoff. This defensive stance was born from hard-won experience. Too many projects became bogged down by large data

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