Prysmian – Prysmian to acquire Atkore to become a fully-fledged electrical solutions provider

EMR Analysis

More information on Prysmian: See the full profile on EMR Executive Services

More information on Francesco Gori (Chairman of the Board of Directors + Member of the Remunerations and Nominations Committee, Prysmian Group): See the full profile on EMR Executive Services

 

More information on Massimo Battaini (Group Chief Executive Officer and General Manager, Prysmian Group): See the full profile on EMR Executive Services

More information on Pier Francesco Facchini (Chief Financial Officer and Executive Director, Finance, Administration, Control and IT, Prysmian till end of 2026): See the full profile on EMR Executive Services

More information on Anna Tanganelli (Chief Financial & Information Technology Officer, Iveco Group till November 2, 2026 + Chief Financial Officer and Executive Director, Finance, Administration, Control and IT, Prysmian as from November 2, 2026 + Member of the Board of Directors, GVS SpA): See the full profile on EMR Executive Services

 

 

More information on Maria Cristina Bifulco (Chief Strategy, M&A, Investor Relations & Communication Officer, Prysmian Group): See the full profile on EMR Executive Services

 

 

More information on General Cable by Prysmian: See the full profile on EMR Executive Services

 

 

More information on Encore Wire by Prysmian: See the full profile on EMR Executive Services

More information on Daniel L. Jones (Chief Executive Officer, Encore Wire, Prysmian): See the full profile on EMR Executive Services

 

 

More information on Channell Commercial Corporation by Prysmian: See the full profile on EMR Executive Services

More information on William H. Channell Jr (Chief Executive Officer, Channell, Prysmian): See the full profile on EMR Executive Services

 

 

 

More information on Atkore: See the full profile on EMR Executive Services

More information on Michael V. Schrock (Chairman of the Board of Directors, Atkore Inc.): See the full profile on EMR Executive Services

More information on Bill Waltz (President and Chief Executive Officer, Atkore until a successor is appointed + Representative of non-voting Member and Board Member, NAED): See the full profile on EMR Executive Services

More information on John Deitzer (Vice President, Chief Financial Officer, Atkore): See the full profile on EMR Executive Services

 

 

 

More information on Morgan Stanley: https://www.morganstanley.com/ + Since our founding in 1935, Morgan Stanley has consistently delivered first-class business in a first-class way. Underpinning all that we do are five core values.

Morgan Stanley (NYSE: MS) is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in 42 countries, the Firm’s employees serve clients worldwide including corporations, governments, institutions and individuals.

More information on Ted Pick (Chairman and Chief Executive Officer, Morgan Stanley): https://www.morganstanley.com/about-us-governance/operating-committee 

 

 

 

More information on Wachtell, Lipton, Rosen & Katz: https://www.wlrk.com/ + Wachtell Lipton was founded on a handshake in 1965 as a small group of lawyers dedicated to providing advice and expertise at the highest levels.

We have achieved extraordinary results following the distinctive vision of our founders — a cohesive team of lawyers intensely focused on solving our clients’ most important problems.

We have experience in the fields of mergers and acquisitions, strategic investments, takeovers and takeover defense, shareholder activism, corporate and securities law and corporate governance. We handle some of the largest, most complex and demanding transactions in the United States and around the world. We counsel both public and private acquirors and targets. We handle sensitive investigation and litigation matters and corporate restructurings, and counsel boards of directors and senior management in critical situations. We have a track record of original and groundbreaking solutions and innovations that have had a dramatic impact on business and law. We are thought leaders.

Our distinctive structure defines our approach. We maintain a ratio of associates to partners significantly below that of other firms. We focus on matters that require the attention, extensive experience and sophistication of our partners. We limit the number and type of matters we undertake.  Our system of lock-step compensation promotes a careful selection of matters as well as the flexibility to bring the right expertise to bear without regard to factors extrinsic to providing the best service and advice. We work together on a task-force basis on all of our matters, bringing to bear the requisite mix of people and expertise across practice areas. Our structure and approach attracts talented and entrepreneurial lawyers, who enable us to achieve excellent results for our clients in complex and critical matters.

More information on Andrew J. Nussbaum (Co-Chairman, Wachtell, Lipton, Rosen & Katz): https://www.wlrk.com/attorney/ajnussbaum/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• AI – Artificial Intelligence:
  • Artificial Intelligence (AI) is the broad field of computer science focused on building systems that perform tasks requiring human-like intelligence, such as learning, reasoning, perception, and decision-making.
  • AI systems typically:
    • ingest large datasets
    • identify patterns
    • make predictions or decisions
  • AI is an umbrella term that includes machine learning, deep learning, and other approaches (rule-based systems, optimization, etc.), not just Machine Learning (ML).
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. Most modern AI systems. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Research stage. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. Does not yet exist. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state.
  • Machine Learning (ML):
    • Subset of AI that enables systems to learn from data without explicit programming.
    • ML uses historical data to detect patterns and make predictions.
    • ML is the dominant paradigm in modern AI, replacing most rule-based systems.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:
      • supervised learning,
      • unsupervised learning,
      • semi-supervised learning and
      • reinforcement learning.
  • Deep Learning (DL):
    • Subset of ML using multi-layered neural networks to learn complex representations.
    • DL is not always “more sophisticated” in all contexts—it is more powerful for unstructured data (images, text, audio), but classical ML can outperform it in structured/tabular data.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition. Face recognition is a good example.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • AI systems that generate new content (text, images, code, audio, etc.) based on learned patterns.
    • GenAI is typically powered by large deep learning models (e.g., transformers), not a separate paradigm.
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Broad AI field for interpreting visual data.
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • lndustrial application of Computer Vision. MV is a subset of CV, not a parallel category.
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates multiple data types (text, image, audio, video).
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
    • The defining feature of an agent is not just decision-making, but the ability to take actions toward a goal in an environment.
  • Agentic AI:
    • Agentic AI is a system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multi-agent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • AI executed locally on devices (IoT, sensors, cameras) instead of centralized cloud.
    • Edge AI refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. It is an infrastructure trend (AI data centers / GPU clusters), not a distinct AI category. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs.
    • While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of AI that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments via sensors and actuators.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy (still niche and mostly experimental.)
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.
    • “AI factory” is a conceptual term (not standardized), referring to industrial-scale AI production systems.

 

 

 

• EBIT:
  • Earnings Before Interest and Taxes (EBIT) is a measure of a company’s operating profitability before accounting for interest expenses and income taxes. It is also known as operating profit and shows how effectively a company’s core business is generating profit from its operations.
• EBITA:
  • Earnings before interest, taxes, and amortization (EBITA) is a measure of company profitability used by investors. It is helpful for comparing one company to another in the same line of business.
  • EBITA = Net income + Interest + Taxes + Amortization
• EBITDA: 
  • Earnings before interest, taxes, depreciation, and amortization (EBITDA) is an alternate measure of profitability to net income. By including depreciation and amortization as well as taxes and debt payment costs, EBITDA attempts to represent the cash profit generated by the company’s operations.
  • EBITDA and EBITA are both measures of profitability. The difference is that EBITDA also excludes depreciation.
  • EBITDA is the more commonly used measure because it adds depreciation—the accounting practice of recording the reduced value of a company’s tangible assets over time—to the list of factors.
• EV/EBITDA (Enterprise Multiple):
  • Enterprise multiple, also known as the EV-to-EBITDA multiple, is a ratio used to determine the value of a company.
  • It is computed by dividing enterprise value by EBITDA.
  • The enterprise multiple takes into account a company’s debt and cash levels in addition to its stock price and relates that value to the firm’s cash profitability.
  • Enterprise multiples can vary depending on the industry.
  • Higher enterprise multiples are expected in high-growth industries and lower multiples in industries with slow growth.

 

 

 

• Power Utility – Utilities:
  • Also known as an electric utility or power company, a power utility is an organization (public or private) responsible for the generation, transmission, distribution, and sometimes retail supply (sale) of electricity to consumers.
  • They often operate in regulated or partially liberalized markets, and are major providers of energy in most countries.
  • Depending on the market structure, these functions may be vertically integrated (one company does everything) or unbundled across multiple entities (e.g., generation companies, transmission system operators, distribution network operators, retailers).
  • Utilities are critical infrastructure operators, ensuring reliability, grid stability, and continuous power supply to residential, commercial, and industrial users.

 

 

 

• Earning Per Share (EPS):
  • Company’s net income attributable to common shareholders (net income minus preferred dividends) divided by the weighted average number of common shares outstanding.
  • The resulting number serves as an indicator of a company’s profitability on a per-share basis. It is common for a company to report adjusted EPS (e.g., excluding extraordinary or non-recurring items) and diluted EPS (including potential shares from options, convertible debt, or warrants).
  • The higher a company’s EPS, the more profitable it is considered to be (although EPS should always be analyzed in context—e.g., growth, industry, and capital structure).
  • Earnings per share value is calculated as net income divided by available shares. A more refined calculation adjusts the numerator and denominator for potential dilution (stock options, convertible securities, warrants).
  • The numerator of the equation is also more relevant if it is adjusted for continuing operations (excluding one-off or discontinued activities).
• Dividend Per Share (DPS):
  • DPS is the actual portion of those earnings distributed to shareholders as dividends (cash or sometimes stock dividends).
  • The actual cash paid out by the company to an investor for each share owned, calculated as:
    Total dividends paid to common shareholders / Number of common shares outstanding (or weighted average shares).
  • High-growth companies often have a high EPS but a DPS of $0 because they reinvest all profits. Established, mature companies tend to pay out a portion of their earnings (payout ratio) as a DPS.
  • A company’s DPS can exceed EPS in a given year (e.g., using retained earnings or debt), but this is generally not sustainable over the long term.
• => EPS vs. DPS:
  • EPS measures how much profit a company generates per share, while DPS shows how much of that profit is actually distributed to shareholders. EPS reflects profitability, whereas DPS reflects distribution policy.

 

 

 

• Securities (Foundation):
  • Securities are fungible, tradable financial instruments used to raise capital in public or private markets, representing either ownership (equity), debt (creditor relationships), or rights to ownership or cash flows (derivatives). Common examples include stocks, bonds, and investment funds (e.g., mutual funds, ETFs), which allow investors to own a piece of a company or loan money to entities or gain exposure to underlying assets without direct ownership (derivatives).
  • The four main types of financial securities are equity, debt, derivatives, and hybrid securities. These instruments represent either ownership, debt, or a contract based on an underlying asset, designed for trading in financial markets to offer income, capital appreciation, or risk management.
  • Securities form the foundation of a company’s capital structure, defining the hierarchy of claims (seniority), risk exposure, and expected returns for investors.

 

• Notes (Core Debt Instruments):
  • Debt instrument with a maturity date typically ranging from 2 to 10 years. Notes are a type of bond, and the terms “note” and “bond” are often used interchangeably, although bonds generally have a longer maturity period (usually over 10 years). Like other bonds, notes are issued by governments and corporations to raise money, and they promise to pay back the principal plus interest to the investor.
  • The distinction is primarily a market convention (maturity and naming), rather than a legal or structural difference in most jurisdictions.

 

• Bonds: 
  • Typically refer to longer-term debt, usually with a maturity of over 10 years.
  • Bonds are issued by governments and corporations when they want to raise money. By buying a bond, you’re giving the issuer a loan, and they agree to pay you back the face value of the loan on a specific date, and to pay you periodic interest payments along the way, usually twice a year (fixed or floating coupon depending on structure).
  • In simple terms, a bond is a loan from an investor to a borrower such as a company or government. The borrower uses the money to fund its operations, and the investor receives interest on the investment. The market value of a bond can change over time.
  • Bond prices fluctuate based on interest rates, credit risk, and market conditions (inverse relationship with interest rates is a key principle).

 

• Senior Note Offering (Capital Structure):
  • A senior notes offering refers to the issuance of senior-ranking debt securities by a company to raise capital. Typically, the announcement of a senior notes offering is accompanied by a legal disclosure of the amount the company is seeking to raise, and what the company plans to do with the money.
  • A senior note is a type of bond that takes precedence over other debts in the event that the company declares bankruptcy and is forced into liquidation (but may still rank below secured debt if unsecured). Because they carry a lower degree of risk, senior notes pay lower rates of interest than junior bonds.
  • Companies use Senior Notes when they want to avoid repaying debt principal each year (bullet repayment at maturity instead of amortization) and don’t mind the higher interest expense they’ll incur.
  • Senior Notes are standard corporate debt instruments (not a “compromise”) positioned between secured bank debt and subordinated/mezzanine instruments in the capital structure.
  • Subordinated Notes move even further into risky territory.
  • Senior subordinated notes are a type of corporate debt that ranks below senior debt but above equity in a company’s capital structure, meaning they get paid after senior obligations in bankruptcy but before shareholders, offering investors higher yields to compensate for the increased risk. These notes are often unsecured, carry higher interest rates than senior debt, and provide more flexibility for companies, sometimes used in M&A or buyouts.

 

• Loans vs Bonds (Private vs Public Debt):
  • Unsecured Term Loan Facility:
    • An unsecured term loan facility is a lump-sum loan that is not backed by any collateral (like property or assets) and is provided to a borrower for a fixed period, with a specified repayment schedule. Lenders determine eligibility based primarily on the borrower’s creditworthiness, including their credit history, income, and debt levels. Because they carry higher risk for lenders, unsecured term loans typically have higher interest rates and stricter qualification criteria than secured loans.
    • Unlike bonds, term loans are typically privately arranged (bank or institutional market), not publicly traded securities and often include covenants (financial and operational restrictions) negotiated with lenders.
  • Senior Secured term Loan B Facility:
    • A Senior Secured Term Loan B (TLB) facility is a type of syndicated corporate debt structured primarily for non-bank institutional investors like collateralized loan obligations (CLOs), hedge funds, and pension funds. It sits at the top of a company’s capital structure, providing lenders with first-priority claims on the borrower’s collateral, while offering borrowers maximum operational flexibility with minimal short-term payment obligations (typically limited amortization and covenant-light structures).
    • TLBs are typically floating-rate instruments, often priced using benchmarks such as Term SOFR plus a credit spread (margin).

 

• Hybrid Instruments (Debt + Equity Features):
  • Convertible Bonds (Hybrid):
    • A convertible bond is a fixed-income corporate debt security that yields interest payments but can be converted into a predetermined number of common stock or equity shares. The conversion from the bond to stock can be done at certain times during the bond’s life and is usually at the discretion of the bondholder.
    • As a hybrid security, the price of a convertible bond is especially sensitive to changes in interest rates, the price of the underlying stock, and the issuer’s credit rating.
    • Convertible bonds sit between debt and equity in the capital structure and therefore carry intermediate risk/return characteristics (downside protection from bond + upside participation via equity conversion).
  • Warrants: 
    • Bond with warrants gives investors the right to purchase shares separately, unlike convertibles.
    • Warrants are detachable instruments in many cases, meaning they can trade independently from the bond.
    • Convertible: bond converts into shares
      Warrant: additional right to buy shares without converting the bond itself (bond remains outstanding even if warrant is exercised)

 

• Regulatory / Bank Capital Instruments:
  • Capital Securities:
    • Capital securities are hybrid financial instruments combining features of both debt and equity, often issued by banks and insurance companies to meet regulatory capital requirements (e.g., Additional Tier 1 (AT1) or Tier 2 capital under Basel frameworks). They are deeply subordinated in the capital structure, typically paying higher yields than senior bonds and allowing issuers to defer payments (in some cases at the issuer’s discretion).
    • These instruments behave more like equity in stress scenarios due to their subordination and loss-absorption features (e.g., write-down or conversion to equity).
  • Callable Subordinated Capital Securities:
    • Callable subordinated capital securities are specialized, high-yield financial instruments that combine features of both debt and equity. They are primarily issued by banks, insurance companies, and other financial institutions to bolster their regulatory capital requirements (such as Tier 1 or Tier 2 capital).
    • These securities are subordinated, meaning they rank below senior debt in the capital structure, placing them at higher risk of loss in the event of issuer default. They are callable, giving the issuer the option—but not the obligation—to redeem or “call back” the securities at a specified price before their maturity date, typically after a non-call period (e.g., 5 years).
    • The call feature introduces reinvestment risk for investors (issuer will typically call when refinancing is cheaper).
  • Perpetual Securities (Hybrid):
    • Perpetual securities, often called “perps” or perpetual bonds, are fixed-income instruments with no fixed maturity date. They offer a stream of interest payments indefinitely, as long as the issuer remains solvent and continues to make coupon payments (which may be deferrable in some structures). Unlike traditional bonds, they don’t have a redemption date, meaning the principal isn’t repaid at a specific time.
    • This type of bond is a hybrid instrument with strong equity-like characteristics (not pure equity) and is often treated as partial equity by rating agencies.

 

• ESG / Thematic Debt:
  • Green Bonds: 
    • Green Bonds enable capital-raising and investment for new and existing projects with environmental benefits with proceeds earmarked for eligible green projects and subject to reporting/verification frameworks. The Green Bond Principles (GBP) seek to support issuers in financing environmentally sound and sustainable projects that foster a net-zero emissions economy and protect the environment.
  • Blue Bonds:
    • The World Bank defines blue bonds as “a debt instrument issued by governments, development banks or others to raise capital from impact investors to finance marine and ocean-based projects that have positive environmental, economic and climate benefits.”
    • Blue bonds work in the same way as traditional bonds but are different in that the entities issuing them are determined to use the resources generated – or a large proportion thereof – for the protection and conservation of marine ecosystems.
  • Green vs. Blue Bonds: 
    • Green bonds are broader (all environmental projects), while blue bonds are a niche subset focused specifically on ocean and water ecosystems — not limited to offshore renewables.

 

• Reference Rates (Not Securities):
  • Term SOFR:
    • Term SOFR is a forward-looking benchmark interest rate, published by the CME Group, that provides market expectations for the Secured Overnight Financing Rate (SOFR) over a specific period.
    • It is not a financial instrument, but a reference rate used to price loans and floating-rate bonds (typically expressed as: Term SOFR + credit spread).

 

• ESG + Hybrid Combination:
  • Green Subordinated Capital Securities:
    • The proceeds are strictly earmarked to finance or refinance projects with positive environmental impacts, such as renewable energy, clean transportation, or energy efficiency, in line with frameworks such as ICMA Green Bond Principles while also meeting regulatory capital requirements if issued by financial institutions.
  • Green Hybrid Subordinated Capital Securities:
    • Green hybrid securities are subordinated financial instruments that combine the equity-and-debt features of standard hybrid bonds with the environmentally friendly commitments of a green bond. When a company issues a green hybrid, it explicitly commits the raised capital to financing or refinancing sustainable projects, such as renewable energy, clean transport, or grid decarbonisation.
    • They provide companies with flexible “equity-like” (not just “risk”) capital while giving ESG-conscious investors a higher-yielding option than traditional senior green debt with increased risk due to subordination and hybrid features.

 

 

 

• Equity:
  • Equity means ownership value, or what is left of an asset’s value after you subtract any debts or liabilities tied to it. The basic formula is: Equity = Assets − Liabilities.

 

 

 

• A, B and C Shares:
  • Companies can issue different classes of shares (e.g., Class A, B, C) that provide different rights in terms of voting, dividends, and control.
    • Class A Shares:
      • Typically offered to public investors and usually provide standard voting rights (often 1 vote per share), along with rights to dividends and capital in case of liquidation.
    • Class B Shares:
      • Often held by founders, executives, or early investors and typically provide enhanced voting rights (e.g., multiple votes per share), allowing them to retain control over the company.
    • Class C Shares:
      • Usually issued with no or very limited voting rights, but still provide economic rights such as dividends.

 

• Treasury Shares:
  • Treasury shares (or treasury stock) are a company’s own shares that it has repurchased from the market and held on its balance sheet rather than being canceled.
  • These shares:
    • are no longer considered outstanding shares
    • do not carry voting rights
    • do not receive dividends
    • are excluded from earnings-per-share (EPS) calculations
  • Companies may later:
    • reissue them
    • use them for employee stock plans
    • or cancel them permanently

 

• Share Buyback:
  • A share buyback, also known as a share repurchase, occurs when a company purchases its own outstanding shares from the market, reducing the total number of shares in circulation.
  • This can:
    • increase earnings per share (EPS) by reducing share count
    • return capital to shareholders (alternative to dividends)
    • signal management’s confidence in the company’s value
    • adjust ownership structure or defend against takeovers

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Financial Notes: 

• Major financial KPI’s since 2017 are available on EMR Executive Services under “Financial Results” and comparison with peers under “Market Positioning”
• Companies’ full profile on EMR Executive Services are based on their official press releases, quarterly financial reports, annual reports and other official documents like the Universal Registration Document.
• All members of the Executive Committee and of the Board have their full profile on EMR Executive Services
• Prysmian Half Year 2026 Results Release: https://www.prysmian.com/sites/www.prysmian.com/files/media/documents/PR_Q2%2726_EN_FINAL.pdf
• Prysmian Half Year 2026 Results Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/2026-07/Prysmian_2Q26_Presentation_def.pdf
• Prysmian Q1 2026 Results Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/2026-04/Prysmian_1Q26_Presentation_def.pdf
• Prysmian FY 2025 Integrated Results Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/2026-02/Prysmian%20FY25_Presentation_def.pdf
• Prysmian Capital Market Day 2025 Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/2025-03/250325_Prysmian_CMD_Static_conC_.pdf
• Prysmian Annual Integrated Report 2024: https://www.prysmian.com/sites/www.prysmian.com/files/2025-03/Integrated_Annual_Report_2024_Prysmian_ENG.pdf
• Prysmian FY 2024 Integrated Results Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/2025-02/Prysmia_FY24_Presentation_def.pdf
• Prysmian Annual Integrated Report 2023: https://www.prysmian.com/sites/www.prysmian.com/files/2024-03/Integrated-Annual-Report-2023.pdf
• Prysmian FY 2023 Presentation: https://www.prysmian.com/sites/www.prysmian.com/files/media/documents/investors/Prysmian-fy23-presentation-def.pdf
• Prysmian Annual Integrated Report 2022: https://www.prysmiangroup.com/sites/default/files/pr-2302-rsg-2022-integrated-annual-report-compr_0.pdf
• Prysmian FY2022 Presentation: https://www.prysmiangroup.com/sites/default/files/prysmian-fy22-presentation-def.pdf
• Prysmian Annual Report 2021: https://www.prysmiangroup.com/sites/default/files/pr-2302-rsg-2022-integrated-annual-report-compr_0.pdf
• Prysmian FY 2021 Presentation: https://www.prysmiangroup.com/sites/default/files/atoms/files/Prysmian%20FY21_Presentation_def_0.pdf

 

• The Atkore Second Quarter 2026 Earnings Presentation and Business Update can be found here: https://s202.q4cdn.com/690266772/files/doc_financials/2026/q2/Q2-2026-Earnings-Deck-vFinal.pdf
• The Atkore First Quarter 2026 Earnings Presentation and Business Update can be found here: https://s202.q4cdn.com/690266772/files/doc_financials/2026/q1/Q1-2026-Earnings-Deck-Final.pdf
• The Atkore Annual Report 2025 (Form 10-K) can be found here: https://d18rn0p25nwr6d.cloudfront.net/CIK-0001666138/23c8507e-12d3-4115-bfcf-22b1705ecffd.pdf
• The Atkore Fourth Quarter 2025 Earnings Presentation and Business Update can be found here: https://s202.q4cdn.com/690266772/files/doc_financials/2025/q4/Q4-2025-Earnings-Deck-vF-2025-11-19.pdf
• The Atkore Investor Presentation (November 2024) can be found here: https://s202.q4cdn.com/690266772/files/doc_presentations/2024/Nov/27/ATKR-November-2024-Investor-Presentation.pdf
• The Atkore Annual Report 2024 (Form 10-K) can be found here: https://d18rn0p25nwr6d.cloudfront.net/CIK-0001666138/3665b8a4-b586-4015-80ab-15b42659c7d2.pdf
• The Atkore Fourth Quarter and Full Year 2024 Results Presentation can be found here: https://s202.q4cdn.com/690266772/files/doc_financials/2024/q4/Q4-2024-Earnings-Deck-vF-2024-11-20.pdf
• The Atkore Investor Presentation & Company Overview May 2024 can be found here: https://s202.q4cdn.com/690266772/files/doc_presentations/2024/May/17/atkr-investor-deck-may-2024-vf.pdf
• The Atkore Annual Report 2023 can be found here: https://s202.q4cdn.com/690266772/files/doc_financials/2023/ar/2ee0cdca-081a-4cda-a0f9-6e6906a8ed1c.pdf
• The Atkore September 2023 Investor Presentation can be found here: https://s202.q4cdn.com/690266772/files/doc_presentations/2023/Sep/01/atkr-presentation-september-2023-vf.pdf
• The Atkore Annual Report 2022 can be found here: https://d18rn0p25nwr6d.cloudfront.net/CIK-0001666138/752ab06f-733f-4ef7-bd50-0481c6549152.pdf
• The Atkore Annual Report 2021 can be found here: https://d18rn0p25nwr6d.cloudfront.net/CIK-0001666138/e194f304-7460-418a-ad6a-65b594a6673e.pdf