Market Research In Finance

A new study assessed carbon crediting mechanisms, addressing whether carbon credit projects lead to REAL emission reductions. Analyzing 2,346 carbon mitigation projects that account for nearly 1 billion tons of CO₂ (about 20% of all credits issued), researchers found that less than 16% of carbon credits issued constitute real emission reductions. Wind power projects in China and improved forest management in the US showed no statistically significant emission reductions. Cookstove projects achieved only 11% of claimed reductions, SF6 destruction 16%, and avoided deforestation 25%. Even the best-performing category, HFC-23 abatement, reached only 68% of claimed reductions. This assessment comes at a moment of carbon market expansion. The "offset achievement gap" identified by the study - 812 million credits that don't represent actual emission reductions - exceeds Germany's annual emissions. The research reveals three systematic issues: project developers often choose favorable data for their baseline or make unrealistic assumptions, methodologies sometimes use outdated data, and adverse selection leads to crediting projects that would have happened anyway (aka not "additional"). This evidence suggests carbon crediting mechanisms need reform to raise their potential for climate mitigation. It underscores the importance of scrutinizing carbon credit quality and prioritizing direct emission reductions over offsetting for businesses and investors. Kudos to Benedict Probst, Malte Toetzke, Andreas Kontoleon, Laura Diaz Anadon, Jan Minx, Barbara Haya, Lambert Schneider, Philipp Trotter, Thales A. P. West, Annelise Gill-Wiehl, Volker Hoffmann from great institutions.

**The Bloomberg Terminal: A Key Resource in Investment Banking?** The Bloomberg Terminal serves as an indispensable resource for professionals in investment banking, asset management, and trading, facilitating advanced financial analysis, extensive market data access, and real-time news updates. **1. Market Data & Real-Time Analytics** - Access comprehensive real-time datasets across equities, fixed income, foreign exchange, and commodities. - Analyze historical financial data alongside macroeconomic indicators to inform strategic decisions. - Monitor global market movements and key economic indicators in real time. 🔹 **Key Commands:** - **WEI** – Overview of World Equity Indices - **WB** – Comprehensive World Bonds Overview - **FXC** – Foreign Exchange Rates Insights **2. News & Research** - Stay updated with breaking news from Bloomberg News and other reputable sources, ensuring you have the latest market insights at your fingertips. - Conduct deep-dive analyses of industry reports and scrutinize company filings for informed decision-making. - Set customized alerts for significant market developments to maintain a competitive edge. 🔹 **Key Commands:** - **TOP** – Access to Top News Headlines - **BI** – Bloomberg Intelligence (in-depth research) - **BETA** – Beta Analysis for assessing stock volatility and risk profiles **3. Financial Analysis & Valuation** - Execute comparative analyses of companies utilizing detailed financial statements and key ratios. - Implement valuation methodologies, including DCF (Discounted Cash Flow), among others, to derive intrinsic values. - Track high-stakes M&A activity, IPOs, and credit ratings with precision. 🔹 **Key Commands:** - **FA** – Company Financial Analysis Tool - **RV** – Relative Valuation for Peer Comparisons - **MA** – Comprehensive Mergers & Acquisitions Database **4. Fixed Income, Derivatives & Portfolio Management** - Analyze bond pricing, construct yield curves, and assess credit risk using advanced analytical tools. - Utilize sophisticated pricing models for options, swaps, and other derivatives. - Manage and monitor portfolios with a suite of risk management tools, aiding in performance analysis. 🔹 **Key Commands:** - **YCRV** – Yield Curve Analysis Functionality - **SRCH** – Bond Search Tool for fixed income analysis - **PORT** – Portfolio Management Dashboard for tracking and optimizing investment strategies Feel free to add any additional insights or information.

The best business plans in energy aren't the most detailed. They're the ones grounded in market reality. Here's how market research transforms planning... The energy sector is massive, deploys a ton of capital, and needs years or decades for these moves to fully pay off. In that world, there is no getting around planning. Think about the different plans oilfield services firms may build: ● 𝐒𝐚𝐥𝐞𝐬 𝐩𝐥𝐚𝐧𝐬 – How much revenue are we going to generate? From what products/services? From what customers? ● 𝐂𝐚𝐩𝐞𝐱 𝐩𝐥𝐚𝐧𝐬 – How much capital are we going to deploy, and against which opportunities? What will the return be? How long will it take? ● 𝐍𝐞𝐰 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐩𝐥𝐚𝐧𝐬 – Which new products or services help us create the most value? How will we position these new offerings? ● 𝐍𝐞𝐰 𝐦𝐚𝐫𝐤𝐞𝐭 𝐞𝐧𝐭𝐫𝐲 𝐩𝐥𝐚𝐧𝐬 – Can we redirect our existing offering to new markets or customer segments? Do we need new capabilities? ● 𝐌&𝐀 𝐚𝐧𝐝 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐩𝐥𝐚𝐧𝐬 – Do we need to acquire or partner with someone to achieve our goals? What are the risks and rewards, and how do we analyze them? ● 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐧𝐬 – What improvements will drive the largest incremental efficiencies? What is the market value of these efficiency improvements? ● 𝐒𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐧𝐬 – Should we reconfigure our supply chain? What are the impacts to internal costs and customer service quality? Market research can help strengthen every single one, revealing actionable insights across a number of dimensions: ➤ What operators are spending today and in the future ➤ Where activity is most resilient and growing ➤ Which barriers are most challenging for operators ➤ Which goals operators have set and which bets they’re placing as a result ➤ How the performance of our sector is creating challenges and opportunities for everyone One of the cool parts of this whole dynamic is that we don’t have to engineer some super exotic market research program. A little rigor goes a long way. We quickly get into diminishing returns territory, where more time and money spent on research does not meaningfully reduce uncertainty. The lesson here is that as you work through your next plan, spend a little bit of time gathering and interrogating relevant market data. You’ll combat uncertainty that otherwise would have slowed you down, made it harder to enroll folks in the process, and/or confused your resulting execution. With 2026 planning season upon us, a little bit of market research effort can give your momentum a huge boost. ====== I help leaders use market research to see around corners, making plans that target the most attractive opportunities and are resilient to market turbulence. Reach out if you want to discuss how I can support you and your team.

Researchers studied 1,710 futures pair portfolios across equities, bonds, currencies, and commodities from Jan 1985 through Sep 2023. They found dynamic trading methods boost returns and reveal hidden interactions between asset classes. These strategies improve diversification and risk control. Results depend on data limits and need real-world tests before finance teams adopt them. This study shows that targeting top “base pairs” can triple average annual returns at fixed leverage. Key findings: 📈 Performance Boost: Focusing on the top 5% of base pairs lifts the “All” portfolio from 3.4% to 10.4% annualized returns at fixed leverage. 🔄 Diversification Edge: Cross-asset interactions across equities, bonds, currencies, and commodities reveal shifting risk-return dynamics and enhanced diversification. 🔍 Predictive Drivers: Cross-asset effects account for up to 55% of performance heterogeneity; signal-mean imbalances and correlations further shape pair returns. ⚙️ Strategy Revival: Underperforming momentum approaches convert into winners when high-θ pairs are selected each month. ✅Practitioner tips: Use monthly θ (risk-adjusted return strength) scoring to rank base pairs, prune the bottom 95%, and allocate equally to the top pairs. Rebalance each month, monitor cross-asset signals, and standardize leverage, start with a 5% selectivity threshold to boost returns and diversify risk. 🎓🏛✍️ Authors & affiliations: Christian Goulding, Auburn University Harbert College of Business Business, Auburn University Campbell Harvey, Duke University, National Bureau of Economic Research 👉 Read the full study on SSRN:5193565 ✅ If you are interested in keeping up with new papers and research in Quant Finance/AI/LLMs, Sign-Up to our Monthly Quant Finance and AI/LLM Research Newsletter, link in the comments. #Finance #Trading #Investing #PortfolioOptimization #RiskManagement #Diversification #QuantitativeFinance #FuturesTrading #AssetAllocation #InvestmentResearch #MarketAnalysis #DataDriven #TradingStrategies #FinancialMarkets #QuantTrading #AlternativeInvestments #FinancialModeling #SmartInvesting #FinancialInnovation #InstitutionalInvesting

Tech vs. Trees: Bridging the Data Gap in Carbon Markets The rapid growth of nature-based climate solutions has brought with it a surge of technology-driven approaches—advanced remote sensing products, AI models galore, even blockchain-based MRV (Measurement, Reporting, and Verification). Yet, despite these advancements, one surprising yet fundamental gap remains: we lack direct, long-term observations of tree growth in natural forests. Without robust, site-specific data on forest dynamics, carbon stock estimates will remain highly uncertain, risk of misallocating credits will remain high, and trust in nature-based solutions low. What’s missing? More empirical data from diverse, unmanaged forests, particularly in tropical regions. Long-term monitoring of tree growth, mortality, and recruitment across different ecosystems is essential for ensuring the integrity of carbon markets. While monoculture plantations are routinely measured in industry, natural forests—where the real long-term carbon benefits lie—are complex and poorly understood at scale. That's why I was excited to see this recent preprint by Nathaniel Robinson and colleagues: https://lnkd.in/gSabS_yT Can technology help? Absolutely. Technology should be used to improve data collection, not just refine models or build products. Innovations like sensor networks, automated dendrometers, and expanded eddy-covariance measurements can complement traditional fieldwork, scaling up direct observations while reducing uncertainty in carbon accounting. For carbon markets to succeed, we need more boots on the ground, more field measurements, and a stronger commitment to real-world data. Technology alone won’t fix the problem—it’s time to invest in better ecological monitoring, not just better algorithms. If you have ideas on how to unlock observations of natural tree growth at scale, please reach out. Let’s discuss! #CarbonMarkets #ClimateTech #ForestCarbon #MRV #EcosystemScience

🌌 𝗕𝗹𝗮𝗰𝗸𝗥𝗼𝗰𝗸 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝘀 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗕𝗼𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗯𝗼𝗻𝗱𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝘁𝗿𝗮𝗱𝗲. Some can go days or weeks without any buyers or sellers, making it hard to determine how much they're worth. It's like trying to value a house that hasn't been sold in 10 years – you'd look for recently sold similar houses based on size, number of bedrooms, school district etc. to estimate its value. A team of researchers at BlackRock compared two different methods to compute similarities of corporate bonds using publicly available datasets: ‘traditional’ machine learning method called Random Forests, and a novel quantum-mechanics-inspired AI method called Quantum Cognitive Machine Learning (QCML). An intuitive description of Random Forests is that it generates a group of decision trees that ask a series of yes/no questions—sort of like playing “20 Questions.” • Is the bond rating below investment grade? (Yes/No) • Is the company’s debt higher than a certain threshold? (Yes/No) The algorithm repeats this process hundreds or thousands of times with slightly different questions, hoping that the aggregate decisions over all these trees would get closer to the “correct” value of the target variable (in this case, bond yield). Then, the similarity between pairs of bonds from this final group of trees is simply the number of times the pair ends up being in the same end node. QCML — drawing on quantum theory’s ability to handle multiple possibilities — can outperform traditional bond-valuation methods at least for certain high-yield bonds by letting features interact more subtly, according to the study. Instead of forcing bond data into forming groups of decision trees as in Random Forest, in QCML, the researchers use quantum states, which are mathematical representations that measure how similar different bonds are to each other, such as risk, industry, country, yield, and liquidity—at once. Think of quantum states like chords in music: the same notes can produce different sounds depending on their combination. Similarly, quantum states allow bond features to interact with each other in subtle and sophisticated ways, capturing nuanced relationships that traditional binary methods miss. QCML creates a more balanced distribution of data points, which is especially helpful for evaluating bonds that rarely trade or have unusual characteristics. While random forests tend to push most bonds to maximum distance from each other (making similarity assessment difficult), QCML creates a more natural clustering where similar bonds remain visibly grouped together, even when handling outliers. Study: Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning Authors: Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan, Kharen Musaelian, Ryan Samson, Martin Wells, Dhagash Mehta, Ph.D., Stefano Pasquali Link below for the paper. 

Bond Types, Pricing Dynamics, and the Role of Machine Learning - Fixed income markets encompass a wide spectrum of instruments—government, corporate, municipal, high-yield, floating-rate, inflation-linked, and convertible bonds—each with distinct pricing drivers. Government bonds reflect the term structure of interest rates and expectations of future monetary policy, while corporate and high-yield bonds incorporate credit spreads, default probabilities, and recovery assumptions. Inflation-linked securities are tied to breakeven inflation, and convertibles embed option-like characteristics requiring hybrid valuation models. - Traditionally, bond pricing has relied on discounted cash flow analysis, spread models, and stochastic term structure frameworks. However, the increasing complexity of global markets has accelerated the use of machine learning to enhance these methods. - Applications include: -- Predicting credit spreads using supervised learning on macroeconomic and firm-level data -- Modeling yield curve dynamics with techniques such as LSTMs and Gaussian processes -- Enhancing default probability estimation through ensemble methods applied to balance sheet and market data -- Detecting mispricings across bond markets via anomaly detection and clustering algorithms - By combining financial theory with data-driven methods, practitioners can better capture nonlinear relationships, improve forecast accuracy, and manage risk in environments where traditional parametric models may fall short. - As bond markets evolve amid rate volatility and shifting liquidity conditions, machine learning offers a complementary toolkit for pricing, hedging, and portfolio construction. #FixedIncome #BondMarkets #MachineLearning #InterestRates #QuantFinance #Data #hedging #clustering

⚡ GreenMax Capital Group is proud to have co-authored with Verst Carbon and CLASP this newly released report on #CarbonCredit #Financing for #ProductiveUseAppliances (#PUAs). 💡 Key Findings ✅ A review of existing methodologies for carbon projects involving solar Productive Use Appliances (PUAs) revealed significant gaps, especially in terms of regional applicability, data consistency, and monitoring accuracy. ✅ These gaps limit the effectiveness of the methodologies, making it difficult to capture the true emissions reduction potential of solar PUA projects across different regions, particularly in Africa. 📜 Recommendations ☑️ Develop a new, consolidated solar PUA carbon methodology ☑️ Conduct robust, standardized baseline studies ☑️ Support innovation around digital monitoring, reporting, and evaluation ☑️ Develop and adopt an emission reduction calculation tool Many thanks to Verst Carbon and CLASP for the great leadership and collaboration and to the many stakeholders interviewed for their data and insightful contributions. Congratulations again to GreenMax contributors Daniel Kitwa, FCCA and Emily Lundberg for a job well done. https://lnkd.in/ekU4R8Qp

Energy data is useless if it doesn’t change decisions. Most teams still miss the signal. We drown in reports, not insights. Leaders want faster, sharper decisions. Here’s how top teams unlock real advantage: 𝟭 - 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘀𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗰𝗼𝘀𝘁 𝗱𝗮𝘁𝗮: Level the playing field for project costs. Reveal hidden inefficiencies and trends. 𝟮 - 𝗟𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗰𝗼𝘀𝘁 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻: Sync Rystad data with FieldTwin tools. See cost impacts live as designs evolve. 𝟯 - 𝗦𝘁𝗿𝗲𝘀𝘀-𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗺𝗮𝗿𝗸𝗲𝘁 𝗼𝘂𝘁𝗹𝗼𝗼𝗸: Model regional energy futures. Pressure-test your plan against demand swings. 𝟰 - 𝗗𝗲𝗰𝗼𝗱𝗲 𝘆𝗼𝘂𝗿 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗿𝗶𝘀𝗸𝘀: Spot bottlenecks and sourcing risks early. Map strategic supplier opportunities. 𝟱 - 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗮𝘀𝘀𝗲𝘁𝘀 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀: Compare wells, fields, and emissions. Support M&A and portfolio calls with evidence. 𝟲 - 𝗠𝗼𝗱𝗲𝗹 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗮𝗻𝗱 𝗷𝗼𝗯 𝗶𝗺𝗽𝗮𝗰𝘁𝘀: Link energy plans to GDP and employment. Turn data into stakeholder leverage. 𝟳 - 𝗥𝘂𝗻 𝗔𝗜 𝗼𝗻 𝘃𝗲𝘀𝘀𝗲𝗹 𝗮𝗻𝗱 𝗱𝗿𝗶𝗹𝗹𝗶𝗻𝗴 𝗱𝗮𝘁𝗮: Improve market forecasts and predict disruption. Use Rystad as your predictive engine. 𝟴 - 𝗧𝗿𝗮𝗰𝗸 𝗲𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗮𝘁 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱 𝗹𝗲𝘃𝗲𝗹: Support ESG reporting with credible numbers. Map carbon by operator, asset, and basin. 𝟵 - 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝘆 𝗱𝗲𝗰𝗮𝗿𝗯𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀: Track storage, hydrogen, and renewables rollouts. Spot stranded assets before they tank value. 𝟭𝟬 - 𝗚𝗲𝘁 𝗶𝗻𝘀𝘁𝗮𝗻𝘁 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝗻𝘀𝘄𝗲𝗿𝘀: AskRystad gives you expert-grade summaries. No more sifting through 400-page PDFs. 𝗥𝘆𝘀𝘁𝗮𝗱 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲. It’s a decision-making weapon. When connected to real workflows, it helps you move faster than the market. ♻️ Repost to help your network ➕ Follow Nick Curum for more