Portfolio Management

Building Wealth: People often ask me for tips, predictions, or targets. I try to resist that level of prediction. I don't focus on just one thing like that. I don’t claim certainty. I don’t try to predict exact outcomes. My strategy is about building a portfolio that can survive uncertainty and still compound sensibly over time. I want a portfolio which can: 1) Survive bad outcomes 2) Participate in good ones 3) Avoid catastrophic mistakes You don’t need to be right all the time. You just need to stay in the game. Equities: The single most important filter I apply is financial survivability. I strongly prefer companies that: 1) Are already profitable, or very close to it 2) Generate positive cash flow 3) Have manageable debt and a clear cash runway A company can own great assets, fantastic geology, or exciting technology — but if it lacks cash, all bets are off. Many investors underestimate this. In mining especially, history is littered with companies that ended up as holes in the ground with a promoter sitting on top. This is why I avoid businesses that rely endlessly on equity issuance or debt just to survive. Most people diversify by geography or industry. I also diversify by investment style. That means deliberately holding a mix of: 1) Growth stocks 2) Value stocks 3) Quality, cash-generative businesses 4) Cyclicals and commodities 5) Turnarounds and optionality Some of these will always be underperforming. That’s the point. Different styles work at different times, and diversification by style reduces the need to constantly “get the timing right”. Don't go "all-in" in one trade on a stock. Buy each stock gradually (the 33% rule). One of the biggest drags on performance is over-trading. I don't trade. After buying a stock, I do not reassess it for at least 12 months. Every company has good news and bad news over a year. Constantly reacting to headlines usually leads to poor decisions. In my experience, trading feels productive, but it rarely is. When I do review holdings after at least a year, I ask one key question: Would I buy this today? If the answer is no, it’s probably a sell. If the stock is performing well, I consider whether to skim part of the profit (top-slicing) but often keep at least the original capital invested. This avoids a common trap: selling all your winners and being left with only losers. Over time, that destroys portfolios. Top slicing winners and exiting losers usually generates enough cash to fund the next year’s investments without constant churn. Other assets: In a world of rising debt, inflation, and political stress, real assets matter: Commodities Precious metals Property But structure matters as much as exposure. Physical assets sit outside the financial system but come with storage and cost issues. Paper instruments are cheaper and liquid but remain inside the system. There is no single “perfect” solution — only trade-offs.

So you're thinking of building an #electrolyzer to make green #hydrogen. But how much #wind, #solar and #battery capacity do you need to power the electrolyzer in order to minimize the cost of hydrogen it produces? BloombergNEF has just the tool you need to find out - the Hydrogen Electrolyzer Optimization Model (H2EOM). A vastly enhanced version 2.0 was published yesterday by my brilliant colleagues Xiaoting Wang and Ulimmeh-Hannibal Ezekiel. For an example project in #California, the optimal setup for a 1MW electrolyzer is to power it by 1.14MW of wind and 0.83MW of solar, skipping the batteries. That gives you a levelized cost of hydrogen (LCOH) or $4.63 per kilogram and a utilization rate of 65% on your electrolyzer (excluding any #IRA #45V #taxcredits). If you wanted to increase the utilization rate to 90%, you'd need to be happy with a #LCOH of $7.28 per kilogram as you pay for batteries, as well as more solar and wind capacity. Users can do this modeling for any location on the planet by using BNEF's Solar- and Wind Capacity Factor Tool to get 8,760h of capacity factor data anywhere. Users can tweak any cost and financing assumption to suit their project, making this a super versatile tool for #H2 modeling. Oh, and did I say you can model up to 50 projects at once? BNEF clients can download the model here: https://lnkd.in/e9vTYc7G

Cash bond yields tempt. The small print is duration. You earn carry, but you also wear a long fuse. When the back end twitches, months of income can vanish in a day. That’s not drama. That’s math. Here’s the uncomfortable truth: most investors don’t choose duration; spreads choose it for them. A tight spread on a long bond feels safe until rates move. Then you find out your “income” was leverage in disguise. If you can’t hold through a rate shock, you didn’t buy yield. You rented risk. Carry you can keep beats yield you can’t hold. I’d rather own short-dated IG with clean balance sheets than stretch for a few extra basis points in long HY with thin covenants. I want duration where I pick it, not hidden inside credit. If I add length, I pair it with liquid hedges and clear exits. Pride doesn’t pay coupons. Cash does. The curve still matters. Front end gives you carry and optionality. The belly can work when cuts arrive on schedule, not hope. The very long bond is a tool, not a home. Use it for a reason: liability matching, a hedge, or a defined trade. Not because the yield looks neat on a slide. Know your DV01. If you don’t know how much a 25–50 bp move costs you, you’re not managing risk. You’re guessing. A portfolio that bleeds on small rate moves won’t be around for the big win. Size like you plan to survive boredom and shock. Credit spreads look calm—until they don’t. They don’t give you a countdown. They gap. If growth cools or policy bites, refinancing risk shows up fast at the weak end. That’s when owning quality feels “boring” right up until it saves the month. Boring is a strategy. Tactics I like now: keep a T-bill sleeve for dry powder. Skew to short IG over long HY. Add a measured belly position where valuations are fair. Use simple hedges instead of cute structures you can’t exit. If volatility is cheap, rent some. If it’s rich, cut size and wait. And remember: income is not a trophy. It’s a stream that needs defense. Rebalance winners. Trim length into rallies. Add only when the tape gives you paid risk, not just risk. The goal is steady compounding, not yield cosplay. Are you choosing duration, or is it choosing you? What’s your portfolio DV01 on a 50 bp bear steepener? Which bonds still pay you for the credit risk? Where would you cut first if the long end jumps? What lets you hold through a bad week without panic? For more see our Nomura CIO Corner: https://lnkd.in/e4TCax_g Appreciate @Tathagata @Anuragh @Dhrumil for the sharp back-and-forth #fixedincome #bonds #rates #duration #yield #credit #carry #treasuries #riskmanagement #portfolio #CIO #Nomura

Discover → Control → Trust → Scale Governance is not a tool. It’s a layered system: Catalog – discover, tag, and connect data + AI assets. Quality – enforce correctness, freshness, and reliability. Policy – codify who can do what, where, and how. AI Control – govern models, prompts, and usage. Break one layer → trust breaks. Good governance doesn’t slow data down — it makes it usable, trusted, and AI-ready. With so many tools out there, the real question is simple: what helps your team trust data faster? Here's the breakdown to adapt and integrate with Data Governance: ⚙️ 1. ENTERPRISE GOVERNANCE TOOLS Collibra – Enterprise‑grade governance platform for glossary, lineage, and policy‑driven stewardship. Atlan – AI‑powered data catalog that enables self‑service discovery and governance‑as‑code. Informatica Axon – Unified governance hub for policies, lineage, and MDM‑integrated data. Alation – AI‑driven catalog and search engine built for analyst‑centric discovery. OvalEdge – Governance and compliance platform focused on sensitive‑data detection and templates. Secoda – Lightweight AI catalog for modern data teams with simple issue tracking. ☁️ 2. CLOUD‑NATIVE GOVERNANCE Databricks Unity Catalog – Single governance layer for data and ML across the Databricks lakehouse. Google Cloud Dataplex – Unified data governance and profiling layer for GCP data lakes. Microsoft Purview – Cross‑Azure catalog, classification, and sensitivity‑label governance engine. Snowflake Horizon – Native governance and access control layer built into Snowflake. Google Cloud Data Catalog – Metadata discovery and integration layer for BigQuery and Vertex AI. 🔄 3. PIPELINE + QUALITY LAYER dbt Labs – Transformation‑forward framework that enforces data contracts and testing in pipelines. Great Expectations – Validation framework that codifies data quality expectations and tests. Soda – Observability tool for monitoring data freshness, distribution, and anomalies. ⚡How to decide, where to begin with? Single platform  → Start with Unity Catalog / Dataplex / Purview / Snowflake Horizon. Multi‑cloud  → Add Atlan / Collibra as cross‑platform governance. Data quality issues  → Enforce contracts with dbt + Great Expectations. The smartest governance stacks don’t rely on one tool, Instead they combine catalog, quality, lineage, and policy where each matters most. #data #engineering #AI #governance

I have spent years building data pipelines, and governance was always the hardest part. SOC 2 audits. PII handling. Lineage documentation. We always treated them as afterthoughts, something to “add later.” That’s why Express by Nexla feels different. It’s built with compliance at the core, not as a feature, but as a foundation. Here’s what stood out to me in their governance layer 👇 SOC 2 compliance baked in:  Every pipeline runs with enterprise-grade controls and encrypted operations. PII masking by default: Sensitive data gets identified and protected automatically. Data lineage visibility: Every transformation and flow is tracked, versioned, and auditable. Policy automation:  Access, validation, and monitoring rules run silently in the background. It’s the kind of compliance that doesn’t slow teams down.  It empowers adoption. When governance becomes invisible, innovation accelerates. If you’ve ever battled the friction between speed and control, this is worth a look: https://lnkd.in/dDEhWF3e

Amazon is quietly rewriting the rules of vendor profitability in 2026. Across Consumables, Softlines, and Hardlines, shipping costs on <$15 items are eating Amazon's Contribution Margin on every order. As a result, Vendor Managers are raising Net PPM targets and pushing vendors for new selection with higher ASPs. But Amazon’s shifting demands haven’t met the reality of most 1P vendors (yet), who still treat Amazon as a bolt-on to their other channels. They still list the same products and price-pack architecture across Walmart, Target, and Amazon and then wonder why pressure on Net PPM keeps rising. Amazon needs entry-level price points to win shoppers, but also higher ASP items to counterbalance the CM hit those lower ASP units cause. Without both, Amazon's economics don't add up. Which is exactly why your VM keeps coming back for more. So if you haven't worked on premiumising your Amazon assortment, you may want to recalibrate your priorities. Here's how: 1️⃣ Test price points and demand through virtual bundles 2️⃣ Convert your best-performing VBs into hard bundles 3️⃣ Launch differentiated assortment where possible 4️⃣ Prioritise SIPP packaging when launching new products 5️⃣ Ask for a lower account-level Net PPM target in return 6️⃣ Make premium-priced selection part of your trade negotiations Yes, hard bundles and value packs incur upfront investments. But the annually compounding opportunity cost of inaction far outweighs the one-time development cost. We're entering an era where Amazon protects its invisible bottom line below Net PPM. Is your assortment strategy ready for it? ♻️ Repost to share, and 💭 Comment your thoughts below. #amazonvendor #amazonstrategy

If you had unlimited human resources, would your projects actually be more successful? Let’s break down one of the most overlooked yet critical aspects of project success: resource management. Getting resource management right is the difference between projects that stumble and projects that deliver with precision. It’s about more than just assigning people or tools, it is about understanding these: ✅ What resources are needed to achieve project goals. ✅ When they’re needed so timelines stay intact. ✅ Who is responsible for driving outcomes. ✅ How resources depend on one another, ensuring smooth flow and reducing bottlenecks. When you align these elements, you do not just meet deadlines you deliver within the parameters set by your client and create sustainable value. The truth is, projects do not fail because of lack of talent or effort; they fail when resources are misaligned or mismanaged. Strategic resource management is the glue that keeps planning and execution together. Key Action Points for Effective Resource Management: 1. Map resources early: identify people, tools, budget, and tech before execution begins. 2. Define roles clearly : assign ownership so there’s no confusion on “who does what.” 3. Align timing: ensure resources are available exactly when needed, not sitting idle or arriving late. 4. Check dependencies: spot where one task or resource relies on another to avoid bottlenecks. 5. Balance capacity: don’t overload team members; match tasks with realistic capacity. 6. Monitor continuously: track usage and adjust in real time when priorities shift. 7. Communicate often: keep everyone updated to prevent gaps and overlapping efforts. When you apply these steps consistently, resource management stops being a back-office checklist and becomes a strategic advantage, delivering projects on time, on budget, and with client trust intact. The truth is, success is not about having more people, it is about managing the right resources at the right time #FolaElevates #StrategicProjectLeadership #ResourceManagement #ProjectExecution

We love to talk about ore grades, jurisdictions, and capex—but when it comes to why mining investments really succeed or fail, the answer is often simpler: people. A new piece in Commodity Insights Digest laid it bare. Interviews with 42 investors revealed that 93% believe the management team is more critical than the asset itself. And yet, here’s the paradox only 17% actually use a comprehensive suite of due diligence tools to assess management. Tools like blind referencing, psychometric testing, skills gap matrices, and structured interviews are shockingly underused. In fact, 43% are aware of psychometric testing, but only 12% apply it. Most still rely on informal chats, gut feel, and dinners. One investor even admitted: “We shoot more from the hip on management due diligence than any other deal aspect.” The consequences are clear: roughly 50% of CEOs in PE-backed companies are replaced within four years, and half of those changes are due to underperformance. And for mining, that number is likely even higher. The cost? Time, capital, and sometimes the entire project. From cultural misfits and overpromising execs to poor communication and hubris—many common failure modes have nothing to do with geology, and everything to do with leadership. This piece outlines 10 actionable best practices from formal interviews and incentive plan reviews to structured team evaluations. What struck me most was how often investors apply rigorous hiring processes internally, but don’t extend the same scrutiny to their portfolio companies. If we want to back successful projects, we need to stop assuming management is a black box and start doing the work to open it up. Because in mining, the real assets don’t just sit in the ground. They sit at the table.

I spent the last few weeks building something that's been on my mind for a while. Managing a global portfolio of legal entities is genuinely hard. Directors, board meetings, regulatory licenses, capital requirements, compliance deadlines — all scattered across spreadsheets, email threads, and institutional memory in people's heads. So I built GovernanceOS — an open-source platform that centralises all of it. Entity registry with parent/subsidiary hierarchy. Director management and tenure tracking. Board meeting scheduling, quorum tracking, minutes. License expiry alerts. Regulatory capital monitoring. And an AI-powered Terms of Reference generator that produces jurisdiction-aware governance documents for 10 countries. Built for compliance and legal teams at regulated financial institutions. The part I'm most proud of: it ships with a CLAUDE.md file. Clone the repo, open it in Claude Code, type "set this up for me" — and you're running in minutes. No reading docs, no debugging config. Claude handles the database, migrations, and seed data automatically. Open source. MIT licensed. Free to use, fork, and adapt. I have attached some screenshots of sample data. Nium now uses this across 28 entities and all our licenses. https://lnkd.in/gAZ79N5A Would love feedback from anyone in legal ops, compliance, or fintech who's dealt with this problem. Shout out to Chandrasekhar Cidambi for guiding me on my first build.

Too many brands accept every PO from Amazon in order to recognize the revenue This approach often has compounding consequences months and even years down the line. The brands excelling at managing this are taking a different approach If Amazon has 8 weeks of cover on hand and orders the equivalent of 10 more weeks, you shouldn’t fill that PO in full Unless you’re right before peak season, you’ll be overstocked by 8 or so weeks Amazon’s algo doesn’t like you being overstocked Amazon will mark down your inventory if it’s not selling at a high enough rate relative to how much inventory they are holding We’ve also seen that Amazon has essentially put storage limits on vendors during peak selling periods like they do for sellers It makes sense because Amazon isn’t going to hold endless inventory from a brand that isn’t going to sell short term, especially when Amazon is tight on square footage As a result, Amazon stops ordering or slows down ordering on best sellers too, which can be crippling for a business We saw this happen to multiple vendors in Q4 last year The best operators look at how many weeks Amazon has on hand and in transit between LTL/FTL and DI for each ASIN and compare that to what Amazon is ordering At the same time, they overlay a demand forecast to ensure they are adjusting up and down for any seasonal trends If Amazon is ordering 5 weeks worth of inventory and they already have 14 weeks of cover and it’s normal season, the strong brands aren’t fulfilling that PO for that particular ASIN I believe there’s two issues that lead to brands mismanaging this The first is lack of data analytics They merely don’t have the capability to analyze all this data We’ve invested millions of dollars in our platform to do this…it’s not easy Second, some employees have the wrong compensation incentives that cause them to want to accept POs to recognize the revenue to juice sales goals performance even though it’s not what’s best for the business That’s easily fixed by changing performance incentives If you’re struggling with this, odds are it’s the first issue over the second. However, you should look at both