Analyzing Market Volatility

Let’s talk about copper imports and some of the complexity right now in anticipating overall effects on users (both in terms of timing and magnitude of effects). Two charts below (one my own, one reproduced from Bloomberg, originally from https://lnkd.in/g__Rvwet). Thoughts: •The top chart shows metric tons of imported copper cathodes & sections of cathodes (HTS 7403.11.0000), which is by far the largest imported type of manufactured copper product (HTS 74), with 2024 imports totaling $8.47 billion dollars (out of $17.2 billion in imports for all of HTS 74, or about 49.4%). In 2024, the average month saw ~75,000 tons of imports. April and May 2025 (last two data points) saw imports of 201,434 and 218,133 tons, respectively (or 2.7x and 2.9x prior year average monthly imports). This frontloading means there is a large stockpile of copper already in the USA that won’t be hit with tariffs. •However, before spiking the inflation football and saying “well, then there will be no inflation”, you need to look at the second chart. This shows the percent premium for US copper futures (Comex) relative to the London Metal Exchange. Normally, that premium is quite low. However, it exploded in 2025, reaching over 20% since 7/8 (when the 50% copper tariffs were announced). For reference, LME copper trades around $10,000 a ton today. What this means is that US users of copper have been paying a 5-15% premium for copper relative to firms in other countries over the past few months, which has now increased to above 20% (and this is before tariffs take effect). •Why does that price premium matter? Simple: higher copper prices in the USA reduce the competitiveness of US exports that contain copper. Moreover, it’s important to remember that far more people are employed in industries that use copper versus the entire copper mining, smelting, refining, and product industrial complex. Simple example: electrical equipment and component manufacturers (NAICS 335) employ 400,000 workers (https://lnkd.in/gEXCTusE), with electrical products extensively using copper. In contrast, the USGS reports just 13,000 workers in the entire copper industrial complex in the USA (https://lnkd.in/gU-pftdr). Implication: Copper tariffs are another example where we are tariffing an upstream intermediate input used by far more workers than employed in the industry that makes the upstream intermediate input. Such trade policies are net job killers, and have even been termed self-harming trade policy (https://lnkd.in/gWgxQjtY). #economics #markets #shipsandshipping #supplychain #construction #supplychainmanagement #manufacturing 

One of the most concerning developments is the growing divergence between professional and retail investors. Institutional investors have quietly reduced risk, shifting toward defensive sectors and fixed income, while retail traders continue chasing speculative trades. Sentiment surveys confirm this imbalance, showing extreme bullishness among small traders, especially in options markets. With these risks building under the surface, prudent investors should proactively protect their portfolios. No one can predict precisely when the market will correct, but the ingredients for a sharp downturn are clearly in place. Savvy investors should use this period of complacency to reduce risk exposure before the cycle turns. Here are six practical steps investors should consider: ▪️ Rebalancing portfolios to reduce overweight exposure to technology and speculative growth names. ▪️ Increasing cash allocations to provide flexibility during periods of volatility. ▪️ Rotating into more defensive sectors like healthcare, consumer staples, and utilities that tend to outperform during corrections. ▪️ Reducing exposure to leverage by avoiding margin debt and leveraged ETFs. ▪️ Using options prudently—not for gambling, but for protecting portfolios through longer-dated puts on broad market indexes. ▪️ Focusing on companies with strong balance sheets, stable earnings, and reasonable valuations. ▪️ The explosion of zero-day options trading is not a sign of a healthy market. It is a symptom of an unhealthy market increasingly driven by speculation rather than investment discipline. Retail traders have moved from investing to gambling, chasing fast profits while ignoring the mounting risks. Greed is rampant, leverage is extreme, and complacency is near record levels. Markets can remain irrational longer than expected, but history tells us these speculative periods always end in a painful correction. Bull markets do not die quietly; they end with euphoric retail excess followed by painful corrections. Investors who recognize the signs early will avoid the worst of the fallout and be positioned to capitalize when value opportunities return.

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

As illustrated by this Bloomberg chart, the price shock emanating from the Middle East War has shifted market expectations toward a "higher-for-longer" rate environment across nearly all systemically important central banks. (The outlier remains the Bank of Japan, which continues to inhabit its own paradigm—though less so recently. However, identifying the changed rate trajectory is merely the opening act of the analysis.) The current situation represents more than a simple price shock; it also involves a "second-round" adverse demand shock. Beyond these immediate economic effects, there is the lingering risk of spillovers into financial instability. All of this underscores the uncertain outlook: central banks will be navigating a series of judgments which, I suspect, will likely (or should) be adjudicated by a single, sobering question: "Which is the least unrecoverable mistake we can make?" The answer to this question is less complicated for single mandate central banks, such as the BoE and ECB, than it is for the dual-mandate Fed. #economy #markets #centralbanks

With public equity and fixed income markets in turmoil in recent weeks the traditional 60:40 portfolio model has again been challenged. There's little doubt uncertainty will pervade these markets for the foreseeable future. Therefore it is timely to release further research on the beneficial portfolio characteristics of private market assets. In this paper "Optimising private market asset allocations" we examine the integration of this asset class within traditional asset allocation strategies to  assess performance impacts across investor risk profiles. We believe that including private market assets can significantly enhance portfolio returns for investors who adopt a risk-based utility-maximising strategy in portfolio construction. Additionally, we find that unlisted infrastructure has the most potential of the private market assets considered to improve portfolio Sharpe ratios, especially for ‘Defensive’ and ‘Balanced’ investors. Our research applies a utility maximisation framework which facilitates risk appetite aware optimisation to tailor portfolios to match specific investor risk preferences and lifecycle stages. A novel two-stage returns unsmoothing approach is used to more accurately estimate true private market return volatility. We show that even after returns unsmoothing, private markets can significantly enhance portfolio outcomes. This study finds that defensive investors benefit from allocations  to infrastructure and private credit, achieving lower volatility and higher returns. Balanced investors see similar advantages with  a stable allocation to infrastructure, while growth investors lean towards private equity for higher risk-reward profiles. This analysis adds further weight to our assertion that private market assets have a material role to play in optimising investor portfolios. With IFM Investors Economics & research Frans van den Bogaerde, CFA and Christopher Skondreas #investment #assetallocation #risk #privatemarkets #portfolioconstruction

Dear ladies and gents, The Ukrainian grain season for 2024/25 may see a significant downturn in agro exports. Due to abnormal July heat, Ukrainian farmers are harvesting sunflower seeds earlier, potentially causing a 25-35% yield drop. The Odessa and Dnipro regions have reported yields of 0.79 tons per hectare, down from 2.17 tons last year. The global sunflower seed consumption and processing estimate was also reduced by USDA to 54.9 million tons and 50.8 million tons, respectively. Bulgaria, despite increased planting, lower yields are expected due to intense heat affecting plant development, seed size, and oil content. Bulgarian crushers are anticipating rising prices. In Moldova, maize crops are reported to be compromised at 70-100%, and sunflower crops in the south at 60-80%. Prices in Ukraine continue to rise, with CPT levels reaching $940-$950.Sunflower oil prices in Europe for October, November, and December loading have risen, with offers at $1085 PMT against bids of $1070 PMT. In Russia, the SFO export duty will remain zero in August. Refined SFO from Ukraine is still offered at $1000 FCA, with bottled oil prices growing to $1.08-$1.10 per liter. Rapeseed export prices in Ukraine are rising, with orders at 23000-23500 UAH/t (€485-€500/t) delivered to Black Sea ports. This is due to restrained sales and low yields, potentially reducing exports in the new season. Canola prices are rising due to weather concerns . PDN numbers remain stable, reflecting weather-driven market dynamics in Canada. Meanwhile, the current bid for rapeseed crude oil is between €830-€840 FCA for the EU market. In China, rapeseed oil bids have reached $1010. Soybean futures fell the most in a month as traders took profits and US weather forecasts improved. For Russia, ICAR expects a record soybean harvest this year at 7.5 million tons, with increased loads at oil extraction plants. Last year's processing volumes were 6.2 million tons, expected to exceed 6.4 million this year. The Ministry of Agrarian Policy of Ukraine predicts the soybean harvest in 2024/25 MY at 5 million tons . Palm oil stock is increasing, and logistic concerns are easing, which will likely lower freight costs from KL and Indonesia. This will widen the gap between palm and sunflower oil, making palm oil more attractive and increasing demand, which will support the price. – Market Outlook – The coming season, with increasing reports of bad weather, is becoming very risky and unpredictable. With fewer seeds, crushers will raise prices, and major players may try to dominate the Ukrainian market, as Ukrainian oil retains an advantage in EU due to Russian oil tariffs. Reduced vegetable oil production in the EU will make the Ukrainian market more oriented towards the EU and keep prices high. At the same time, the demand for vegetable oil is increasing globally, which is likely to support future prices. Thank you for your attention, and stay tuned in for the next update!

UKRAINE’S DEFENSE MARKET IS NO LONGER AN AID CHANNEL For many foreign suppliers, Ukraine has long looked like a difficult wartime buyer: urgent needs, fragmented signals, complicated procedures, and too much uncertainty around who actually buys what. That picture is changing. The EU’s new €90 billion support loan for 2026–2027 gives Ukraine a much larger financial runway, including for urgent defence and defence-industrial needs. But the more interesting story is not only the money. It is how Ukraine is changing the way that money can reach the battlefield. DOT-Chain Defence is becoming the clearest example. Instead of treating procurement as a slow top-down allocation process, the system lets military units order what they actually need through a controlled digital marketplace. According to public reporting and DPA leadership, more than 200 units now use this model, and equipment worth over UAH 30 billion has already reached the front through it, often in weeks, sometimes in days. That matters because modern battlefield technology expires fast. A drone, EW tool, ground robotic system, optic, battery pack, communications device or interceptor that takes months to procure may arrive into a battlefield that has already changed. A system that can be selected, contracted and delivered in days or weeks has a very different military value. This is why Ukraine is becoming attractive not only for drone startups, but also for serious legacy suppliers. The market is not “drone-only.” Ukraine still needs ammunition, vehicles, explosives, optics, communications, power systems, engineering equipment, spares, maintenance capacity and many other conventional categories. The drone war has not removed the need for the rest of the defence supply chain. It has made reliability, speed and delivery discipline even more valuable. The important shift is that Ukraine is becoming less of a black box. For fast-evolving categories, demand is becoming more visible. Units choose what works. Producers compete on performance, price, stock availability and delivery. Weak products are increasingly filtered out not by theory, but by battlefield demand. That does not mean Ukraine is an easy market. It is not. Serious suppliers still need clean documentation, realistic pricing, compliance, export permissions where relevant, clear delivery terms and local follow-through. DPA-facing work is not a brochure exercise. It is paperwork, discipline, and understanding how Ukrainian procurement actually functions. But the direction is clear. Ukraine is becoming one of Europe’s most important live defence markets: large enough to matter, fast enough to teach, and demanding enough to expose who can actually deliver. For companies that treat Ukraine professionally, this is no longer only a war-support channel. It is a serious market where Trident Forward can help.

High-Frequency Trading (HFT) involves executing thousands of trades per second to capitalize on fleeting price inefficiencies. In dynamic portfolio management, HFT is critical because it allows institutional investors to rebalance assets in real-time, maximizing risk-adjusted returns in highly volatile environments where a delay of milliseconds can result in significant losses. Some HFT management often relies on Deep Reinforcement Learning (DRL) paired with architectures like Recurrent Neural Networks (RNNs) or Transformers. However, these models face two major hurdles: 1). Non-Stationarity: Financial markets are 'linear time-varying' systems where patterns shift constantly (domain shifts), making static models obsolete quickly. 2). Asset Correlation: Standard models struggle to capture the complex, global interdependencies between different assets, which is vital for diversification. To address the challenges outlined above, the authors of [1] proposed AB‑SSM (Adaptive Bidirectional State Space Model), a framework that enhances the DRL paradigm by integrating a specialized state‑space model designed for high‑frequency data. Unlike standard SSMs, AB-SSM utilizes: • An Adaptive Linear Time-Varying Structure: This uses an input-dependent state transition matrix that updates in real-time to capture shifting temporal patterns and arbitrage opportunities. • A Bidirectional State Space Layer: This extracts asset correlations by compressing global context from both forward and backward temporal directions, ensuring the model "sees" the relationship between assets more clearly than unidirectional models. AB-SSM was benchmarked against SOTA (state-of-the-art) HFT baselines, including EIIE (Ensemble of Identical Independent Evaluators), DeepScalper, and standard Transformer-based RL agents. The results across U.S., Chinese, and Cryptocurrency markets demonstrate that AB-SSM significantly outperforms these baselines in both cumulative returns and Sharpe ratios (risk-adjusted profit). AB-SSM succeeds because it combines the low spatio-temporal overhead of SSMs (essential for the speed of HFT) with a superior ability to model market non-stationarity and cross-asset dependencies. This makes it a more robust all-weather framework for the high-speed demands of modern digital markets. The link to the paper [1] is posted in the comments.

The past weeks saw significant selling/profit taking in both equity and crypto markets with perhaps retail investors playing a bigger role than institutional investors. Retail investors appear to have sold both crypto and equity funds. And several proxies of the retail impulse into equities have downshifted over the past month such as those based on small traders’ option flows, on the relative performance of retail Investors' favorites vs. S&P500 index or on retail investors’ sentiment surveys such as the AAII survey.   In terms of institutional investors, it has been mostly momentum traders such as CTAs or other quantitative funds that appear to have taken profit on previous extreme long positions in equities, bitcoin and gold. For other institutional investors outside quantitative funds/CTAs we detect a more limited de-risking so far, thus leaving room for further position reduction from here.       

📰 The big story is the Fed and the questions around Fed independence. What do Portfolio Managers do about this? If you're a long/short or long only equity PM or CRO, the natural question will be whether there is exposure or "net macro beta" to Fed independence concerns. 1️⃣ How do you measure Fed independence fear? Right now this is straightforward. A perceived loss of Fed independence will show up as a weaker US Dollar & also quite cleanly as higher long term US inflation expectations. The 10y USD Zero Coupon Inflation swap market is liquid and directly measures long term inflation expectations. If the Fed's real commitment and ability to hit the long term inflation target is in doubt, then long term inflation expectations will rise. If US inflation is viewed as moving structurally higher, then reduces the long term real return on holding USD and thus one would expect USD weakness. 2️⃣ How do you check portfolio impact ? You may simply look at the correlation between your portfolio return and the USD for example (using the USD index, DXY). Or you might decide to look at the correlation of your portfolio to US 10y inflation expectations. But there is an issue here. This so called "univariate" approach makes it very hard to really see whats going on. 👉 That's because the variables of interest - rates, the USD, inflation expectations, energy prices, metals prices and so on - are themselves all correlated. So let's say you see a correlation between your portfolio, or some stock return, and the USD. Is that really a USD impact, or is that because the USD is being driven by some other factor such as rates (higher rates tend to boost the USD)? 👉 With a univariate approach, you are taking a 2-dimensional slice of a multi-dimensional relationship. ❌ This tends to be inaccurate at best and just dead wrong at worse. ✅ The answer is to get a "sensitivity" from a holistic model that includes a broad range of important macro factors and adjusts for the correlations between them all. The above is very easy to prove with a numerical example. The other risk here is that if a loss of Fed independence really gets priced fully, that's a structural shift that will probably take a quite some time to reverse. That in turn means that IF you have exposure, we are not talking about macro "noise" impacting your book. 👉 We are talking about a potentially longer term capital impairment. 🚨 Traditional equity factor models miss the macro dimension - style, sector and market neutral does not mean macro neutral. #fed #riskmanagement #factorinvesting