Strategic Portfolio Analysis

Are our portfolios still calibrated to a climate that no longer exists? This is a valuable topic to discuss with your investment consultant during your next strategic asset allocation review. This question is more complex than most climate disclosures indicate. Many capital market assumptions still implicitly assume that the climate is stationary. Strategic asset allocations (SAA) are based on decades of historical data. Diversification assumptions may hold in typical years but can fail during critical periods. Physical risks are often treated as tail events, even as such risks become more frequent. This is not a fringe concern. The USS / University of Exeter No Time To Lose report and the Institute and Faculty of Actuaries' Emperor's New Climate Scenarios have made this case; many climate scenarios used by financial institutions may understate risk because they fail to capture tipping points, compound events and non-linear damages. Climate scenario analysis has improved significantly, but in many cases it remains separate from the strategic asset allocation process rather than fully integrated. It primarily supports reporting requirements. However, does it influence capital market assumptions, portfolio construction, or the strategic asset allocation itself? For funds with long-term, intergenerational mandates such as pensions, sovereign wealth funds, and endowments, the current El Niño is not the primary concern. The greater concern is the shifting baseline underlying future El Niño events and whether portfolio assumptions have adapted accordingly. Four questions worth exploring with your consultant at the next SAA review, borrowed from the world of cyber resilience: Anticipate: Do our scenarios address specific physical pathways such as multi-breadbasket failure, monsoon disruption, grid-cooling stress, and wildfires, or do they focus mainly on transition risk? Withstand: Where might hidden correlations exist? For example, Australian, Brazilian, and Indian agricultural exposures may appear diversified in typical years but can become highly correlated during an El Niño event. Recover: Do we have the governance, conviction, and liquidity to act as a stabiliser when assets and markets reprice? Adapt: Are climate-resilient infrastructure, energy systems, food systems, transport, water, and adaptation technologies considered core allocations over a 30-year horizon, or are they still treated as peripheral? At your next away day, ensure climate scenarios are integral to the strategic asset allocation process. A practical first step is to work with your investment consultant to review the climate scenario set used in the previous strategic asset allocation exercise, assess the severity of excluded scenarios, and evaluate how those exclusions influenced the final allocation. This discussion may reveal where the most future risks may lie. David Friedberg provides a useful four-minute overview of the developing El Niño on the All-In Podcast. 

The main goal of portfolio selection and construction is to create a profitable portfolio; however, this task is difficult, otherwise we would all be millionaires or billionaires. Markets are dynamic and influenced by numerous factors, while static historical data often fails to capture these dynamics. Investors seek portfolios that optimize the trade-off between risk and return, requiring robust asset allocation. Such requirement is challenging because stock returns are highly unpredictable due to the stock market's nonlinearity, noise, and chaotic nature, making asset selection difficult. To enhance portfolio selection and construction, researchers have incorporated multi-source and multi-aspect data to supplement fundamental and technical stock price data. They have also developed hybrid models involving statistics, econometrics, signal processing, and machine/deep learning (ML/DL) in recent years, which have been shown to outperform single models. DL models like LSTM and CNN excel at capturing temporal and spatial patterns in stock data, improving predictions of returns and volatility. Hybridizing CNN and LSTM (CNN-LSTM) leverages their strengths; CNN for spatial data and LSTM for time series, enabling them to handle complex market dynamics effectively. In [1] which is shared in the comments, the authors proposed a framework combining the essence of DL for stock selection through prediction and optimal portfolio formation through the mean-variance (MV) model. Their proposed framework involves a hybrid CNN-LSTM model in the first stage, which blends the benefits of the CNN and the LSTM. The framework combines feature extraction with sequential learning to analyze temporal data fluctuations. In their experiments, they used 13 input features, combining fundamental market data and technical indicators to capture the nuances of the highly volatile stock market data. The shortlisted stocks with high potential returns, identified during the selection phase, are advanced to the second stage for optimal stock allocation using the MV model. Their proposed hybrid framework is validated through comparison with four baseline strategies and relevant studies, demonstrating superior performance in terms of annual cumulative returns, Sharpe ratio, and average return-to-risk ratio, both with and without transaction costs. #QuantFinance The workflow is depicted in Fig. 3 on page 8, and its detailed description is covered on pages 7 and 8. It is straightforward to implement.

In our latest Global Strategy Paper, "Investing in Everything, Everywhere, All at Once”, we map out the 'World Portfolio'—the sum of all investable assets globally, which we estimate at roughly US$250 trillion (or 200% of world GDP). The World Portfolio acts as a de facto benchmark for global investors, and its composition reveals powerful macro trends. Currently, we see a heavy dominance of US assets in both equities and bonds, a rising weight of equities relative to bonds since the GFC (but not at Tech Bubble levels yet), and growth in alternatives. These are not just abstract trends; they are directly reflected in how investors are allocating their capital today. Why does this matter? Simply following this benchmark is not always a good idea. Our analysis shows that the World Portfolio has seldom been optimal and its performance varies materially with structural macro regimes. Its current concentration in US assets, while a tailwind in recent years, now presents significant risks from a diversification and valuation standpoint. This report provides a framework for investors to actively improve upon this global benchmark. We offer strategies for: 1. Strategic Tilting: Actively managing the equity/bond/Gold mix to navigate different economic environments. 2. Managing US Dominance: Assessing the sustainability of US outperformance and managing the associated FX risks for non-US investors. 3. Broader Diversification: Harvesting benefits from smaller assets and alternatives that are often missed by value-weighted benchmarks. In today's complex market, understanding the limitations of global benchmarks is crucial for effective strategic asset allocation. #assetallocation #gsmacro Read the report here: https://lnkd.in/eGxqZizt

EA Series #13 - Application and IT Portfolio Rationalization/Management Organizations don’t realize how much money is trapped in their IT. Over time, layers of tech build up including legacy systems still running “because someone uses it,” new cloud tools added on, and overlapping apps no one wants to touch. This also gives rise to the notorious “shadow IT”. The result? Rising costs, security risk, slower innovation, and tech complexity that makes change painful. Application and IT portfolio rationalization fixes that. Think of it as a financial audit for your technology to see what you own, what it costs, and evaluate what value it’s adding to the business. The steps to rationalize your IT portfolio are straightforward: 1. Define criteria – Set governance and decision rules. 2. Inventory – Gather all app, cost, and usage data. 3. Clean & validate – Standardize data with SME input. 4. Assess – Score apps for business value, technical health, and cost. 5. Analyze – Find redundancies and low-value systems. 6. Model the future state – Design the simplified, modern architecture. 7. Plan transitions – Use safe migration patterns. 8. Execute & monitor – Track progress and realized savings. Enter Enterprise Architecture (EA); the discipline that connects business strategy with reality. From the previous posts in my series, we know that EA maps: - Business Architecture: what the business does (capabilities, value streams). - Application Architecture: which systems support them. - Technology Architecture: the infrastructure behind it all. - Data Architecture: how information flows between them. Once this picture is clear, each app can be judged using the TIME model (from Gartner): - Tolerate – Keep as-is for now. - Invest – Modern, valuable, worth enhancing. - Migrate – Valuable but needs a modern platform. - Eliminate – Low value or redundant. This structured assessment reveals where to cut cost, where to modernize, and where to double down on what truly supports your business. Rationalization often leads to a future-state architecture. The simpler, more scalable, cloud and AI-ready target to aim for. Transitioning isn’t a big-bang event. Patterns like the strangler approach let you replace legacy systems gradually by surrounding them with new services (or micro-services) until the old is rationalized away. AI can help by scanning portfolios, finding redundancies, and enriching data but the real decisions still come from architectural judgment, not purely algorithms. Most importantly, this shouldn’t be a one-time effort. The goal is to mature into an App Portfolio Management (APM) practice; an ongoing discipline that ensures your technology always fits your business strategy, operating model, and cost priorities. Financial transparency drives better business decisions. Architectural transparency drives smarter technology investments. It’s how enterprises stop paying for yesterday’s tools and start funding tomorrow’s advantage.

Animal health is leaving a frightening amount of value on the table by treating products like pets instead of a portfolio. Most companies still “add one more SKU” or “buy the shiny thing” without designing how the whole portfolio composes into outsized, leveraged returns. Buying and selling company assets and products just hit the big time. Treat it like a market, not a museum. Modern portfolio theory for products is not academic. It is operating math. When you design a portfolio, you stop chasing orphan wins and start compounding system effects. A few moves that separate the leaders from the collectors: —Build to a yield curve. Balance near-cash generators, mid-risk growers, and long-dated options so shared costs and channels get cheaper per dollar over time. —Buy only where your platform multiplies value. Sell where you are a tourist. If it does not increase cross-sell, data gravity, or capacity utilization, it is inventory, not strategy. —Create synthetic returns. Royalty stacks, milestone swaps, co-promotes, and out-licenses that turn non-core science into cash flow while preserving upside. —Make the portfolio P&L explicit. Track the lift from shared sales force, manufacturing headroom, service lines, and data products. If the product does not improve the portfolio P&L, you are subsidizing it. —Enforce kill discipline. Cut assets that do not improve the whole. Rebalance quarterly. Treat features like options and prune the ones you would not buy today. —Design channels as assets. Own at least one route where you control pricing, data, and customer relationship, then let partners rent access. Animal health can do this today. Companion, production, diagnostics, software, services, and therapeutics become a designed stack that throws off cash, insights, and optionality. Human biotech and pharma benefit from the exact same calculus, especially as pipelines get modular and tradable. This is not M&A theater. This is portfolio design that turns science, channels, and contracts into a compounding machine. If your roadmap reads like a shopping list, you are donating returns to the competitor who treats their portfolio like a fund. Time to reward active portfolio design. #AnimalHealth #Biotech #Pharma #PortfolioStrategy #CapitalAllocation #ProductStrategy #CorporateStrategy #MergersAndAcquisitions #LifeSciences #VeterinaryMedicine #GoToMarket #RAndD #Licensing #Valuation

Project Portfolio Governance isn't just about tracking status - it's about answering the four critical questions that determine success. Every project in your portfolio must pass this simple but powerful test. Ask these four questions: Question 1: Are we undertaking the right projects? Question 2: Are we working the right way? Question 3: Is work getting done well? Question 4: Are we seeing the expected benefits? In my experience leading enterprise portfolios, these four questions serve as your early warning system. When projects start to fail, it's usually because we've lost sight of one of these quadrants. Question 1 validates strategic alignment. Question 2 ensures effective execution. Question 3 confirms quality delivery. Question 4 measures actual value realization. Review your current portfolio. Map each project against these four questions. If you can't answer all four with confidence, it's time to reassess. #ProjectManagement #PortfolioGovernance #Leadership