Project Portfolio Management Techniques

Portfolio optimization as a sequential decision problem This one is for my followers in finance… For anyone who is solving nonlinear (Markowitz-style) portfolio models, there is an immediate way to improve the performance of your model.   First, you have to recognize that your portfolio model is a *policy*  for making decisions over time managing your portfolio. While finding an optimal solution to your model is nice, what matters is how the *policy* performs over time (see graphic below). Typically the policies are tested on historical data (“backtesting”).   Solving a Markowitz model would produce an optimal policy if there were no transaction costs, but this is not the case. There has been considerable attention devoted to using approximate dynamic programming to solve the dynamic program, but this is not necessary.   What you want to do is to parameterize your Markowitz model. For example, the importance of transaction costs depends on the volatility of the asset. Imagine multiplying the transaction cost for asset i times a coefficient \theta_i. Using \theta_i = 1 gives you the solution you already have, so optimizing \theta (I am not saying this is easy) is guaranteed to produce a better solution.   The idea of parameterizing a Markowitz model-policy is described in section 13.2.4 of my book at https://lnkd.in/dB99tHtM  (“tinyurl.com/” with “RLandSO”). I recommend using Jim Spall’s SPSA algorithm (see section 5.4.4) for optimizing \theta.    Yes, this is stochastic optimization. No, you don’t need Bellman’s equation or scenario trees. :)

Could strategic misalignment be keeping you and your organization away from attaining maximum value? Executives and project managers are often rowing in different directions. The boat moves, but not necessarily toward value. From my doctoral research, and work with several clients, three pillars of strategic alignment consistently separate high-performing organizations from the rest: 1️⃣ Common Goals – A shared definition of success at both the strategic and operational levels. 2️⃣ Shared Language – Clear communication that bridges “executive speak” and project management terms. 3️⃣ Mutual Understanding – Executives gain insight into project realities, while PMs understand the strategic trade-offs leaders are balancing. The challenge? Most organizations talk about alignment but rarely make it a living system. That’s why I created the ALIGN™ Framework as a practical roadmap: 🪀 A – Assess the Value Chain → Define where value is created and lost. 🪀 L – Listen Across Levels → Build the “bilingual dictionary” across teams. 🪀 I – Integrate Strategy into Planning → Include PMs early in design, not just delivery. 🪀 G – Guide with Goals & Guardrails → Establish clarity with KPIs, OKRs, and constraints. 🪀 N – Navigate with Data & Confluence → Create mutual understanding with dashboards, forums, and collaboration tools. 🔑 ALIGN™ isn’t just an acronym. It’s the operating system for embedding the three pillars of Common Goals, Shared Language, and Mutual Understanding into everyday practice. When organizations apply it, strategy stops being a lofty document and becomes a lived reality. 📌 Question for you: In your organization, which of these three pillars: common goals, shared language, or mutual understanding requires the most urgent attention? Let's create the bride to ALIGN! ♻️Share to elevate others and follow🎙️Fola F. Alabi for more! #FolaElevates #StrategicLeadership #ProjectManagement #SPL #StrategicAlignment #Align #ExecutionExcellence #StrategicConfluenc

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.

💡 The AI honeymoon is over, and most organizations have little to show for it. After years of pilots, proof-of-concepts, and innovation theater, BCG reports only 26% of companies have deployed working AI products—and a mere 4% see meaningful returns. The problem isn't technology. It's the absence of disciplined strategy married to human purpose. I've spent three decades watching brilliant technologies fail not from technical shortcomings, but from organizational incoherence. AI is no different. What separates companies that generate real value from those burning resources on experiments that go nowhere? Two things: strategic discipline and portfolio thinking. In our recent Harvard Business Review articles, we explore how organizations can move beyond the chaos: First, balance innovation with governance using practical frameworks. Our OPEN and CARE framework provide structured ways to ask the right questions early — questions that align AI with genuine business priorities while protecting against risks that emerge when we automate without thinking. This isn't about slowing down or creating bureaucratic bottlenecks. It's about moving forward with intention, ensuring every AI initiative serves both business value and human purpose. Second, treat AI as a portfolio, not a collection of pet projects. Organizations like Northrop Grumman, PepsiCo, and Lloyds Banking Group have proven that structured portfolio management—complete with prioritization frameworks, resource allocation discipline, and clear buy/sell/hold decisions—transforms AI from cost center to strategic asset. When you combine these approaches, something fundamental shifts. AI stops being something bolted onto strategy and becomes inseparable from it. The result: better returns, less waste, and organizations that remain distinctly human even as they become more technologically capable. The question isn't whether to invest in AI. It's whether you're managing those investments with the same rigor you'd apply to any other strategic portfolio. 🔗 Read further @ 📍 "Two Frameworks for Balancing AI Innovation and Risk" → https://lnkd.in/edHnUzGK 📍 "Manage Your AI Investments Like a Portfolio" [with/ Tom Davenport, Paul Scade, PhD, Erik Nelson] → https://lnkd.in/gEJ_WnyM What's blocking your organization from moving AI from experiments to enterprise value? I'm curious what you're seeing.

Why the new Public-Private Partnership (PPP) law is a breakthrough for Ukraine’s recovery investment strategy. 👏 Today, the Ukrainian Parliament passed a landmark PPP law in its second reading — a decision we’ve been working toward for years. This means infrastructure in Ukraine will no longer be built or restored solely “from the state budget.” Instead, we are now aligned with global standards: delivering projects in partnership with business and international donors through robust PPP frameworks. While the concept of PPP has existed in Ukraine for some time, only two major concession agreements have been signed to date. With this reform, we expect PPPs to finally scale — potentially unlocking up to $1 billion in investments over the next few years. Priority sectors include ports, hospitals, and municipal infrastructure. Here’s what makes this law transformative: 1️⃣ A hybrid PPP model Combining budget funding, donor grants, and private capital — making it more cost-effective for the state, faster to deliver, and safer for investors. 2️⃣ Accessibility for smaller communities No expensive feasibility studies required — a simple concept note will suffice. This enables small-scale projects like rehab centers, kindergartens, or affordable housing. State companies like Ukrzaliznytsia and Ukrenergo can now launch PPPs without bureaucratic delays. 3️⃣ Fast-track recovery mechanism A simplified PPP procedure for sectors like health, energy, transport, education, and social services — active during martial law and for 7 years beyond. It applies to projects of all sizes. 4️⃣ Investor safeguards & legal alignment Stronger protections for investors (e.g., contract stability) and alignment across 30+ sectoral laws — enabling PPPs in previously restricted areas such as healthcare, education, culture, transport, and housing. Grateful to the Parliamentary Committee on Economic Development, Ihor Marchuk for leading the drafting process, and to Dmytro Natalukha and Halyna Yanchenko for their continued advocacy and support. This is just the beginning. Now we move forward — from legislation to real projects.

For aspiring quants: Fifteen years ago, I was trying to break into quantitative portfolio management from a different industry. I studied for CFA exams. I learned CAPM, Fama-French, Sharpe ratios. I could calculate betas and alphas. But when I got to MSCI-Barra, I realized I was missing something critical: I didn't understand how it all connected. How did we get from Markowitz's 1952 mean-variance optimization to the sophisticated risk systems institutional investors use today? Nobody taught me the evolution. CFA taught me concepts in isolation. Grad school taught me math. But understanding how it all fits together? I had to piece that together myself over years. This is why I built this introductory Portfolio Management Course. My previous post showed leveraged ETFs deliver 67% of expected returns for their risk. That required CAPM, factor attribution, and variance decomposition. These aren't separate topics. They're chapters in the same story. The evolution of quantitative portfolio management: Markowitz (1952): You can't evaluate returns without considering risk. Sharpe, Lintner, Treynor (1960s): CAPM - market beta explains returns. Barr Rosenberg (1970s): Built the first commercial multi-factor risk models at BARRA - tracked dozens of factors across thousands of stocks. Fama & French (1992): Academic confirmation that size and value factors matter. Carhart (1997): Added momentum. Grinold & Kahn (1990s): Formalized portfolio construction, attribution, and risk management using factor models. Modern institutional models from MSCI-Barra, Axioma, Wolfe Research: Measure risk across dozens of factors Update daily across thousands of stocks Built for portfolio construction and risk management Result of 70 years of research, tested on trillions of dollars You can't understand these tools without understanding how they evolved. This is why our (with Edgar Mauricio Alcántara López ) course follows the intellectual journey: Module 1: Risk-return tradeoff (Markowitz) Module 2: Portfolio optimization Module 3: Regression (statistical foundation) Module 4: CAPM (first factor model) Module 5: Fama-French & momentum Module 6: Attribution (decomposing returns and risk) Each module builds on the last. By the end, you understand not just WHAT modern risk models do, but WHY they evolved this way and HOW to use them. For those breaking into quant portfolio management: When you see a risk factor model report, recognize: → Beta from CAPM (Sharpe, 1964) → Style factors from Fama-French (1992) → Attribution from Menchero (2000s) → Optimization from Markowitz (1952) That separates someone who uses the tools from someone who understands them. Check it out: https://lnkd.in/ebsTPNQ7 All Python code. Open source. Built for people making the same career transition I did. Created independently. All views are my own. What concept took you longest to understand? #QuantFinance #PortfolioManagement #RiskManagement

📈 𝟭𝟬 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗤𝘂𝗮𝗻𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 And the ones I’ve personally learned along my journey 👇 Whether you're building quant projects, preparing for interviews, or working on real-world portfolios, mastering these optimisation techniques is a must. Here are the core models I’ve studied so far — explained simply 👇 🔹 𝟭. 𝗠𝗲𝗮𝗻-𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗠𝗣𝗧) Maximize return for a given risk. Classic. Efficient frontier stuff. 🔹 𝟮. 𝗕𝗹𝗮𝗰𝗸-𝗟𝗶𝘁𝘁𝗲𝗿𝗺𝗮𝗻 𝗠𝗼𝗱𝗲𝗹 Blend market equilibrium with your own views to stabilize weights. 🔹 𝟯. 𝗥𝗶𝘀𝗸 𝗣𝗮𝗿𝗶𝘁𝘆 Let every asset contribute equally to portfolio risk—not return. 🔹 𝟰. 𝗠𝗶𝗻𝗶𝗺𝘂𝗺-𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 Just want the least possible risk? This one’s for you. 🔹 𝟱. 𝗙𝗮𝗰𝘁𝗼𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗔𝗣𝗧) Build exposure to risk factors like value, size, and momentum. 🔹 𝟲. 𝗖𝗔𝗣𝗠 Expected return = risk-free rate + beta × market premium. 🔹 𝟳. 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 Simulate thousands of outcomes. Stress-test everything. 🔹 𝟴. 𝗖𝗩𝗮𝗥 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Minimize expected loss in the worst-case scenarios. 🔹 𝟵. 𝗥𝗼𝗯𝘂𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Worried your inputs are wrong? This model protects against that. 🔹 𝟭𝟬. 𝗠𝗲𝘁𝗮𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗠𝗟 Think Genetic Algorithms or Deep Learning—for complex, constraint-heavy scenarios. 🧠 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Portfolio optimisation is at the heart of risk management, strategy design, and real-world alpha generation. The better you optimise, the better you allocate—not just capital, but conviction. 💬 I’m curious — Which of these have you used or found most useful? Or do you know any others I should explore? Drop them in the comments 👇 🔁 Repost to help fellow quants 🔔 Follow Puneet Khandelwal on quant, ML & finance-tech #QuantFinance #PortfolioOptimization #InvestmentStrategy #FinancialEngineering #Quant #RiskManagement #MLInFinance #AssetAllocation #FinanceCareers #Finance Disclaimer: All views I share are my opinions and don't represent the views of my employer.

"I literally have it up on one screen and do everything else on the other two." That's a VP of Facilities & Capital managing 2,500 projects and a $150M budget talking about his new Smartsheet portfolio dashboard. We rolled it out this week. Within 24 hours, it became his mission control. Before this, he relied on his team to manually filter spreadsheets and send him weekly updates. That meant six days of operating blind between reports. Missing deadlines. Firefighting problems that could have been caught early. Now he has real-time visibility into every project. He can spot bottlenecks before they blow up timelines. He knows exactly who's making progress and who needs help. His take: "Now I can see where our problems are sitting. It's critical to have this." And his team? They're no longer spending hours compiling reports. They're focused on work that actually drives the business forward. When you're managing a portfolio this size, real-time visibility isn't optional. It's the difference between running your projects and scrambling to catch up. The right dashboard turns firefighting into forecasting. #ProjectManagement #CapitalProjects #DataCenterOperations #FacilitiesManagement #ProjectPortfolio

Markets now change in weeks. If your architecture takes quarters to update, you're not planning for the future; you're documenting the past. Models like the Strategic Alignment Model are great for a solid theoretical understanding of alignment but having a mature enterprise architecture practice makes it practical. Without alignment, strategy stays disconnected from execution and in many organisations, the enterprise architecture is underdeveloped or unclear. When you think of your organisation, is your enterprise architecture a roadmap to the future or a museum of last year's business strategy? I share 3 points in the attached visual as a reminder for enterprise architects to think about when creating models and systems for alignment: 1. Execute Strategy Explicitly bring business and IT together by clearly mapping goals and ensuring structured information management. Focus on value. 2. Make Alignment Visible Visualise alignment explicitly by treating architecture and strategy as one integrated discipline, supported by a shared modelling language. Let everyone understand what is meant by a "capability" in your enterprise context. 3. Govern and Continuously Improve Establish transparent governance, adopt proven frameworks, and regularly assess maturity to improve continuously. Great architects don't just understand alignment, they make it structured, visible, and actionable for others.

CSDM 5.0 added a domain most people are ignoring: Ideation & Strategy. Most teams rush to map their operational CIs in the "Run" stage. But they're skipping the part that answers: why does this service exist in the first place? This domain isn't about brainstorming. It's the structured home for the "Business Model" layer that defines 𝘸𝘩𝘺 a service exists before it's ever designed or built. ⸻ 𝟭. 𝗧𝗵𝗲 "𝗢𝗿𝗶𝗴𝗶𝗻 𝗦𝘁𝗼𝗿𝘆" 𝗼𝗳 𝗮 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 Think of this domain as the filter for your organization's potential value. From the CSDM 5 White Paper: "Imagine this… You have a new idea or concept for the business. It may be a new product or an enhancement to an existing capability." → If the business invests, the idea moves into Design & Planning → If it's shelved, it's still fully documented — no zombie projects cluttering your portfolio ⸻ 𝟮. 𝗜𝘁'𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 "𝗜𝗱𝗲𝗮𝘀" High-level diagrams show a couple of objects. The domain actually has 𝘀𝗶𝘅 𝗰𝗼𝗿𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 for Strategic Portfolio Management: • 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗹𝗮𝗻 (`sn_gf_plan`) — container for mission, vision, and values • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗜𝗱𝗲𝗮 (`sn_align_core_product_idea`) — initial concept or change proposal • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗜𝘁𝗲𝗺 (`sn_align_core_planning_item`) — work aligned to goals (demands, projects, epics) • 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 (`sn_gf_strategy`) — specific focus areas for the org • 𝗚𝗼𝗮𝗹 (`sn_gf_goal`) — qualitative statements guiding investment decisions • 𝗧𝗮𝗿𝗴𝗲𝘁 (`sn_gf_target`) — measurable outcomes tied to goals ⸻ 𝟯. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗖𝗠𝗗𝗕 Digital Portfolio Management (DPM) relies on this data to visualize full service health. Ignoring Ideation & Strategy severs the link between 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 and 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯. When a service enters Build & Integration or Service Delivery, it's already grounded in a value stream and tied to a customer outcome. ⸻ Is your org using this domain yet — or still treating strategy as separate from ServiceNow? If you're studying for CIS-DF, I run a free community called the ServiceNow Cert Study Gym — co-working sessions, study plans, accountability, and job postings. Link in comments. #ServiceNow #CSDM #StrategicPortfolioManagement