Operational Alpha: The ROI of Streamlining Your Start-of-Day Process

The New Competitive Frontier: Beyond Market Alpha

In an era defined by compressing management fees, relentless market volatility, and escalating regulatory pressures, the traditional sources of competitive advantage for investment management firms are under siege. For decades, the pursuit of alpha, the excess return on an investment relative to a benchmark, has been the exclusive domain of the front office, a testament to the skill of portfolio managers and strategists. Yet, as generating consistent market alpha becomes increasingly challenging, a new, more durable frontier for value creation is emerging: operational excellence. This paradigm shift gives rise to the concept of Operational Alpha, a powerful form of competitive edge derived not from investment selection, but from the superior efficiency, resilience, and agility of a firm’s internal processes.

 

Introducing “Operational Alpha”

Operational Alpha is the value generated by building streamlined systems and processes to effectively reduce costs, improve decision-making, and manage risk, entirely independent of the investment strategy itself. It is a firm’s “edge” achieved through operational superiority, transforming the middle and back office from a traditional cost centre into a strategic asset that builds lasting profitability and resilience. This is not merely about incremental cost savings; it is a fundamental re-imagining of operations as a driver of competitive advantage. Firms that successfully capture operational alpha are better positioned to seize opportunistic investments, navigate market stress, and deliver superior service, creating a moat that is difficult for competitors to replicate.

 

The Urgency Imperative: T+1 and the End of “Good Enough” Operations

The theoretical importance of operational efficiency has been thrust into urgent reality by a powerful regulatory catalyst: the transition to a T+1 settlement cycle in North America. This is not a minor procedural adjustment but a fundamental compression of the entire trade lifecycle, rendering legacy systems and manual, high-touch workflows unsustainable. The shift from two days to one for settlement may sound simple, but for firms operating across different time zones, such as those in the APAC region, the effective processing window can shrink from a manageable 12 business hours to as few as two.

This compressed timeframe elevates the risk of settlement fails, which carry direct financial penalties and significant reputational damage. Consequently, the T+1 mandate serves as a critical strategic inflection point. It is no longer a compliance burden to be managed but a market-wide stress test that will separate the operationally excellent from the operationally fragile. Firms that have already invested in automation and straight-through processing will find the transition a source of competitive advantage, solidifying their market leadership. Conversely, those still reliant on manual reconciliations and fragmented data systems will face a sharp increase in operational risk, cost, and business disruption.

The historical hierarchy, where investment prowess could compensate for operational sluggishness, is being inverted. The move to T+1 elevates the cost of operational failure from a manageable expense to a mission-critical business risk that can directly impair a firm’s ability to transact. In this new environment, a firm’s operational infrastructure, its speed, accuracy, and resilience, becomes a primary determinant of its viability and profitability. This transformation of operational capability from a back-office support function into a front-line competitive weapon makes the pursuit of Operational Alpha a core strategic imperative for every financial institution.

 

The Three Pillars of Operational Alpha

Achieving this new form of alpha is not the result of a single technology purchase or process tweak. It requires a holistic and sustained investment across three foundational pillars: People, Data, and Technology.

  • People: The foundation of any successful operation is a skilled and empowered workforce. This involves not just hiring the right talent but investing in continuous training and fostering a culture of ownership and continuous improvement.
  • Data: Accurate, timely, and accessible data is the lifeblood of the modern investment firm. Establishing a centralised, single source of truth is paramount to enabling informed decision-making and eliminating the risks associated with fragmented or unreliable information.
  • Technology: Modern technology, particularly automation, serves as the engine that drives operational alpha. It is the critical enabler that digitises workflows, ensures data integrity, and provides the scalability needed for growth.

 

These pillars are deeply interconnected; weakness in one inevitably undermines the others. A highly skilled team cannot be effective if they are working with flawed data, and sophisticated technology is useless without a team trained to leverage it. The following sections will deconstruct the costs of failing to address these pillars, particularly in the critical start-of-day process, and provide a framework for justifying the investment required to build a high-alpha operating model.

Modern financial operations center at dawn

Deconstructing the Start-of-Day Scramble: The Anatomy of Hidden Costs

For any investment management firm, the start-of-day (SOD) process represents the daily “moment of truth.” It is the critical period where overnight data feeds from custodians, prime brokers, and fund administrators must be ingested, validated, and reconciled to produce an accurate, trusted view of positions, cash, and exposures. This foundational data set is the bedrock upon which every trading decision for the day is built. The first hour of trading, often termed the “power hour,” is frequently characterised by heightened volatility and liquidity, presenting the greatest opportunities for alpha generation, and the greatest risk of loss from indecision or error. When the SOD process is manual and inefficient, it creates a cascade of hidden costs that directly erode profitability and hamstring the front office.

Many firms still rely on what is known in the industry as a “flush and fill” or “refresh and forget” approach, where front-office systems are populated by batch-based, start-of-day snapshots. This method is inherently flawed, as it often misses intraday cash movements, corporate actions, or other transactions, resulting in an incomplete and unreliable position view at the most critical time of the day. The scramble to manually correct these discrepancies introduces significant direct, indirect, and opportunity costs that are often poorly understood and rarely quantified.

 

Quantifying the Pain: The Direct Costs of Manual Processes

The most visible costs of an inefficient SOD process are the direct financial outlays required to support manual workflows. While seemingly straightforward, their cumulative impact is often severely underestimated.

  • Labour Costs: The most immediate expense is the significant time spent by highly skilled operations and finance professionals on low-value, repetitive tasks. Industry reports indicate that finance teams can spend as much as 30-40% of their time on manual reconciliation activities. This involves a painstaking process of visually comparing data from different systems, manually investigating exceptions, and correcting errors. The true cost can be calculated by multiplying the number of hours spent on these tasks by the average fully-loaded cost (salary plus benefits) of a full-time equivalent (FTE) employee, revealing a substantial and recurring drain on the firm’s resources.
  • Error Correction Costs: Human involvement in repetitive data-entry and reconciliation tasks makes errors not just possible, but inevitable. Research has shown that data feeds from custodian banks can have daily error rates as high as 18%. These errors can range from mispriced securities and incorrect processing of corporate actions to improperly recorded cash and security transactions. Each error requires a costly and time-consuming investigation to identify the root cause and a remediation process that may involve unwinding trades, incurring transaction fees, and diverting senior personnel from their core responsibilities.
  • Compliance and Audit Costs: Manual processes inherently lack the robust, transparent, and immutable audit trails provided by automated systems. This operational opacity significantly complicates regulatory reporting and external audits. Auditors must spend more time validating data and testing manual controls, leading to longer and more expensive audit engagements. Furthermore, the risk of errors in regulatory reports increases, exposing the firm to potential compliance violations and hefty financial penalties.

 

The Compounding Effect: Indirect and Opportunity Costs

While direct costs are significant, the true financial damage of a flawed SOD process lies in the less visible indirect and opportunity costs. These represent value that is either destroyed or left unrealised due to operational friction, and they have a direct impact on investment performance.

  • Delayed Investment Decisions: This is perhaps the most damaging opportunity cost. When portfolio managers cannot trust the position and cash data they see at the market open, they are forced into a “wait and see” mode, holding off on executing trades until the operations team has manually verified the data. This period of hesitation and uncertainty can mean missing optimal entry or exit points for a security, particularly during the volatile opening hour. The difference between executing a trade at 9:31 AM versus 10:01 AM can translate directly into lost returns.
  • Missed Opportunities and Cash Drag: Inefficient reconciliation processes lead to an inaccurate and delayed view of a portfolio’s true cash position. This uncertainty forces portfolio managers to maintain unnecessarily large cash buffers to avoid overdrafts, a phenomenon known as “cash drag”. Every dollar held in cash as a precautionary buffer is a dollar not invested in the market, creating a persistent drag on overall fund performance. An accurate, real-time cash ladder that projects future cash flows is critical to minimising this buffer and maximising returns.
  • Scalability Constraints: Manual processes are fundamentally unscalable. As a firm grows its assets under management (AUM), launches new funds, or diversifies into more complex asset classes, the volume and complexity of transactions increase. In a manual environment, this growth requires a proportional, if not exponential, increase in operational headcount. This creates a significant bottleneck that can stifle a firm’s growth ambitions, as the operational infrastructure becomes incapable of supporting new business without a prohibitive increase in costs.
  • Employee Burnout and Turnover: The daily grind of high-pressure, repetitive, and error-prone manual tasks is a primary driver of employee burnout and dissatisfaction in operations teams. This not only reduces productivity but also leads to higher employee turnover rates. The costs associated with recruiting, hiring, and training new staff are substantial and represent another significant, albeit indirect, financial drain on the organisation.

 

To provide a clear framework for financial leaders, the full scope of these expenses can be organised into a comprehensive cost analysis.

Cost Category

Description

Example Calculation / Metric

Direct Financial Costs

Explicit, easily measured expenses.

– Labour: (FTEs) x (Avg. Salary) x (% Time on Manual Tasks) – Overtime: Hours spent by ops team fixing SOD breaks. – Error Remediation: Cost to unwind and correct a trade error.

Indirect Financial Costs

Costs resulting from operational failures.

– Audit & Compliance: Additional fees from longer audit cycles; fines from reporting errors. – Capital Buffers: Cost of capital held to cover potential settlement fails or overdrafts due to inaccurate cash positions.

Strategic & Opportunity Costs

Value lost from inaction or delay.

– Delayed Decision Impact: (TargetPrice−ExecutedPrice) x Shares, due to waiting for data confirmation. – Cash Drag: (Excess Cash Buffer) x (Avg. Market Return), due to inability to invest cash confidently. – Growth Limitation: Revenue from new strategies/AUM that cannot be supported by current operational capacity.

The start-of-day process serves as a high-leverage point within an investment firm’s daily workflow. Its function is to establish the foundational data, positions, cash balances, and exposures, upon which all subsequent investment and risk management decisions for the day are based. A failure at this initial stage is not an isolated incident; its effects cascade and compound throughout the trading day. A single misstated cash balance, for example, does not remain a localised operational issue. It propagates through the system, contaminating every downstream process. 

A pre-trade compliance check might erroneously fail, blocking a time-sensitive and critical trade. A risk management system, fed incorrect data, will calculate flawed exposures, leading to inadequate or incorrect hedging decisions. Most critically, a portfolio manager, confronted with conflicting or unreliable data, will hesitate, delaying execution and incurring price slippage that directly impacts performance. Therefore, the true cost of a start-of-day error is not merely the time an analyst spends correcting it; it is the sum of the missed market opportunity, the suboptimal trade execution, the flawed risk calculation, and the potential for a catastrophic trade error that ripples through the entire front office. This demonstrates that an investment in ensuring absolute data integrity at the start of the day has a disproportionately high return, precisely because of its foundational leverage over the entire day’s profit and loss.

A Framework for Calculating the ROI of Automation

For Chief Financial Officers and Chief Operating Officers tasked with stewarding firm resources, any significant technology investment must be justified by a clear and defensible business case. The decision to automate core operational processes, such as the start-of-day workflow, is no exception. While the costs of inaction are substantial, a compelling argument for change requires a rigorous framework for calculating the return on investment (ROI). A holistic approach that moves beyond a simple payback calculation is necessary to capture the full value proposition of strategic automation.

The standard formula for ROI provides a useful starting point:

  • ROI=CostofInvestment(NetBenefits−CostofInvestment)​×100

 

However, for a strategic investment in operational technology, this formula must be populated with a comprehensive assessment of both the full lifecycle costs of the investment and the multi-layered benefits it delivers.

 

Step 1: Calculating the Total Cost of Ownership (TCO)

A common mistake in evaluating technology investments is to focus solely on the upfront purchase price. A more accurate assessment requires calculating the Total Cost of Ownership (TCO), which encompasses all direct and indirect costs associated with the solution over its entire lifecycle. For a modern investment management solution, the key components of TCO include;

  • Acquisition and Implementation Costs: This category includes the initial software licensing or subscription fees, hardware costs (if applicable), and professional services fees for implementation, configuration, systems integration, and data migration. It also covers the internal and external costs of initial user training.
  • Ongoing Operational Costs: These are the recurring expenses required to run and maintain the system. They include annual software maintenance and support fees, IT infrastructure costs (such as cloud hosting), market data feed licenses, and any fees associated with future software upgrades or new releases.
  • Indirect Costs: While harder to quantify, it is important to account for indirect costs such as the temporary dip in productivity as teams adapt to new workflows and systems during the implementation phase.

 

Step 2: Quantifying the Full Spectrum of Benefits

The heart of a compelling ROI analysis lies in a thorough and credible quantification of the benefits. These benefits should be categorised to address the distinct priorities of different stakeholders: hard cost savings for the analytically-minded CFO, and risk mitigation and strategic enablement for the forward-looking COO.

  • Direct Benefits (Cost Savings & Productivity Gains): These are the most tangible returns and form the foundation of the business case.
  • Reduced Labour Costs: Automation of manual tasks like data aggregation and reconciliation directly reduces the number of hours your team spends on these activities. This can be calculated by estimating the time saved and multiplying it by the fully-loaded cost of the employees involved. This often eliminates the need for overtime pay previously required to resolve SOD issues.
  • Increased Productivity and Throughput: Automation allows the existing team to handle a greater volume of transactions and manage more funds or complex strategies without a corresponding increase in headcount. This productivity gain can be quantified by measuring the increase in tasks processed or by the value of the higher-level analytical work the team can now perform.
  • Indirect Benefits (Risk Mitigation & Compliance): This category quantifies the value of preventing costly negative events, a crucial component often overlooked in simple ROI models.
  • Avoided Financial Losses: This represents the “negative alpha avoidance” value. By automating processes, the firm can dramatically reduce the frequency of human error. The financial benefit can be estimated by calculating the expected annual cost of errors (e.g., the average cost to correct a trade error multiplied by the historical annual frequency) that the new system will prevent.
  • Reduced Compliance and Audit Costs: Automated systems provide robust controls and clear, accessible audit trails. This efficiency translates into shorter, less expensive external audits and a lower risk of incurring regulatory fines for reporting errors or compliance breaches.
  • Reduced Capital Requirements: In the context of T+1, faster and more accurate trade processing reduces counterparty risk and the likelihood of settlement fails. This can lead to lower margin requirements from clearinghouses and prime brokers, freeing up firm capital for more productive uses.
  • Strategic Benefits (Future Value & Agility): While more challenging to assign a precise dollar value, these benefits are often the most important drivers of long-term competitive advantage.
  • Faster Time-to-Market: A scalable, automated operational core removes the bottlenecks that often delay the launch of new investment products and strategies. This agility allows the firm to respond more quickly to market opportunities and client demands.
  • Enhanced Investment Decision-Making: The primary strategic benefit of a streamlined SOD process is providing portfolio managers with timely, accurate, and trusted data. This enables them to make better, more confident decisions, leading directly to improved investment outcomes.
  • Scalability for Growth: Automation provides the foundation for sustainable growth. It allows the firm to increase its AUM and client base without being constrained by an operational infrastructure that cannot keep pace, ensuring that growth is profitable.

 

Introducing Risk-Adjusted ROI

For a more sophisticated analysis, particularly relevant in finance, the concept of a risk-adjusted ROI can be introduced. This framework acknowledges that a key benefit of many technology projects is the reduction of operational risk. Instead of relying on a single “most likely” outcome, a risk-adjusted model considers a range of potential outcomes and their probabilities. For example, it might assign a probability to the occurrence of a catastrophic, high-cost operational error and factor the value of its prevention into the overall return calculation, providing a more realistic and defensible picture of the investment’s true value.

Ultimately, the business case for operational automation transcends a simple cost-saving initiative; it should be framed as an investment in a “digital alpha” generator. This value is created through a powerful trifecta of outcomes. 

First, it reduces direct costs by enhancing efficiency and eliminating manual labour. Secondly, it eliminates hidden costs and prevents financial losses by building resilience and mitigating operational risk. Thirdly, and most strategically, it enables new revenue and growth by providing a scalable platform that removes the operational handbrakes on the business. A traditional ROI case that focuses only on replacing manual labour is compelling but incomplete. A more advanced case that includes the value of risk mitigation reframes the investment as a form of essential insurance, appealing to a CFO’s mandate to protect the firm’s capital. The most powerful and strategic business case, however, positions the technology as a direct enabler of the firm’s growth strategy. By creating a highly efficient and scalable operational core, the firm can onboard new clients, launch complex products, and enter new markets faster and more profitably than competitors who remain mired in manual processes. This holistic narrative, “we will spend less (efficiency), lose less (resilience), and be able to earn more (scalability)”, aligns the operational investment directly with the firm’s most critical top-line strategic goals.

From Risk Mitigation to Negative Alpha Avoidance

The concept of operational risk is well understood in financial services, representing the risk of loss resulting from inadequate or failed internal processes, people, and systems. However, its impact is often viewed through a defensive lens, as a cost to be minimised or a disaster to be avoided. A more powerful and strategically aligned perspective is to frame the consequences of operational failure in the language of the front office: as a direct and measurable drag on investment performance. This concept can be termed Negative Operational Alpha.

 

Defining Negative Operational Alpha

While traditional alpha measures the excess return generated by a portfolio manager’s skill, Negative Operational Alpha represents the value subtracted from a fund’s return due to operational failures. It is the quantifiable performance drag caused by errors, inefficiencies, and delays in the operational workflow. As one analysis notes, the basis points saved through efficiency gains can be easily erased by a single operational error, pushing stakeholders into “negative alpha territory”. This loss is entirely distinct from market risk (beta) or poor investment decisions; it is a self-inflicted wound that directly penalises investors.

 

Sources of Negative Operational Alpha

Negative Operational Alpha manifests in several distinct and costly forms, all of which stem from breakdowns in the operational value chain.

  • Trade Errors: This is the most direct and visible source. A mistaken buy order instead of a sell, an incorrect quantity, or a trade allocated to the wrong account results in a quantifiable financial loss that must be absorbed by either the fund or the management company. This loss is a direct reduction of the fund’s Net Asset Value (NAV) and, therefore, its reported performance.
  • Compliance Breaches: Regulatory fines and penalties resulting from failures in compliance monitoring or reporting are a direct withdrawal from the firm’s profit and loss statement. These are pure losses that reduce the capital available for investment and diminish overall firm profitability.
  • Performance Fee Miscalculations: The complexity of modern performance fee structures, which can include high-water marks, hurdles, and clawbacks, makes their calculation highly susceptible to error, especially when managed in spreadsheets. A miscalculation can lead to over- or under-billing clients, resulting in costly remediation efforts, financial restatements, and severe reputational damage that can impact future asset gathering.
  • Data Integrity Failures: As demonstrated by the start-of-day challenges, inaccurate data is a potent source of negative alpha. When a reporting platform miscalculates a key performance metric like an Internal Rate of Return (IRR) by as much as 230 basis points, it constitutes a material misrepresentation of performance that can lead to flawed decision-making by both managers and investors. The suboptimal execution and missed opportunities that stem from a lack of trust in data represent a real, albeit harder to track, form of performance degradation.

 

Shifting the Mindset: Operations as an Alpha Preservation Function

This reframing of operational failure as Negative Operational Alpha necessitates a fundamental shift in how the operations function is perceived within an investment firm. The primary role of a modern, technology-enabled operations department is not merely to process trades cheaply, but to preserve the alpha generated by the front office. It acts as a firewall, protecting the firm’s core value proposition, investment performance, from being eroded by internal friction and error.

From this perspective, investments in automation, robust data governance, and streamlined operational controls are not discretionary cost-saving initiatives. They are essential risk management tools for avoiding negative alpha. This aligns the goals of the operations team directly with the performance objectives of the entire firm, transforming the investment in operational excellence from an efficiency play into a critical defense mechanism for profitability.

By creating and tracking a key performance indicator (KPI) for “Negative Operational Alpha,” firms can make the financial impact of operational failures visible and tangible. Traditionally, operations teams are measured on efficiency metrics like cost-per-trade or straight-through processing (STP) rates, while the front office is measured on P&L and alpha generation. These incentive structures are often misaligned. An operational error causing a $50,000 loss might not significantly alter the cost-per-trade metric, but it directly damages the fund’s performance. By tracking all financial losses attributable to operational failures under a single, high-visibility KPI, the firm creates a shared language and a common goal. The COO’s mandate evolves from simply “reducing operational costs” to the more strategic objective of “minimising Negative Operational Alpha.” This new mandate aligns perfectly with the CIO’s goal of maximising total net alpha and the CFO’s objective of protecting the firm’s bottom line. 

This shared metric fosters a culture of cross-functional collaboration and shared accountability, breaking down the traditional and often counterproductive front-office versus back-office divide. Consequently, investing in automation becomes a shared strategic priority, as it provides a clear and demonstrable path to reducing a key negative performance driver.

Conclusion: Architecting a Future-Proof, High-Alpha Operating Model

 

The landscape of investment management is undergoing a structural transformation. In this new environment, where market alpha is scarce and competitive pressures are immense, operational excellence has transitioned from a back-office concern to a primary driver of competitive advantage. The concept of Operational Alpha, the value generated through superior processes, data integrity, and technology, is no longer a theoretical ideal but a measurable and critical component of a firm’s success.

The analysis has shown that inefficient and manual start-of-day processes are a significant source of hidden costs, operational risk, and what can be termed Negative Operational Alpha, a direct drag on investment performance. These workflows introduce delays that cause portfolio managers to miss crucial trading windows, create data errors that lead to costly trade corrections, and impose scalability limits that stifle growth. The industry-wide shift to a T+1 settlement cycle has amplified these risks, making the modernisation of operational infrastructure an urgent, non-negotiable priority.

However, this challenge also presents a profound opportunity. By embracing automation, firms can not only mitigate these risks but also unlock substantial value. A robust, data-driven framework for calculating the Return on Investment (ROI) demonstrates that the benefits of automation extend far beyond simple labour cost savings. The true value lies in a powerful combination of enhanced efficiency, comprehensive risk mitigation, and strategic growth enablement. As the case study of “Firm X” illustrates, the financial returns from such an investment can be transformative, directly contributing to the firm’s bottom line and its ability to serve clients effectively.

Achieving this state of operational excellence is not a one-time project but a continuous discipline that requires a cultural shift toward perpetual improvement. It demands a holistic commitment to the three foundational pillars of a high-alpha operating model:

  • Technology: A strategic investment in modern, integrated, and automated platforms is the cornerstone of a future-proof operating model. These systems eliminate manual work, ensure data integrity, and provide the scalability required to compete.
  • Data: Establishing a centralised, single source of truth is non-negotiable. Data must be treated as a strategic asset, accurate, timely, and accessible to all who need it, to power informed decision-making across the enterprise.
  • People and Culture: Technology alone is insufficient. Success requires empowering employees with the right tools and training, and, critically, fostering a culture of ownership, experimentation, and rapid feedback that breaks down silos and aligns the entire organisation around the common goal of delivering value.

 

The call to action for financial leaders is clear. Chief Operating Officers are urged to champion the strategic role of operations, repositioning their function as a vital engine for value creation and alpha preservation. Chief Financial Officers should look beyond the upfront cost of technology and evaluate these investments through the comprehensive, risk-adjusted ROI framework, recognising the immense and compounding cost of inaction.

The firms that will lead the next decade of investment management will be those that master not only the art of investing but also the science of operations. By transforming their operational efficiency into a compounding source of Operational Alpha, they will build a resilient, scalable, and profitable foundation for enduring success.

References

  1. Capturing ‘operational alpha’ within a multi-asset portfolio – Mercer, accessed on October 4, 2025, https://www.mercer.com/en-us/insights/yield-point/capturing-operational-alpha-within-a-multi-asset-portfolio/
  2. REINVENTING OPERATIONS IN ASSET MANAGEMENT | Accenture, accessed on October 4, 2025, https://www.accenture.com/content/dam/accenture/final/a-com-migration/r3-3/pdf/pdf-107/accenture-reinvent-operations-asset-management.pdf
  3. US EQUITY MARKET STRUCTURE: LESSONS FROM … – SEC.gov, accessed on October 4, 2025, https://www.sec.gov/comments/265-29/26529-52.pdf
  4. (PDF) The return on investment (ROI) of intelligent automation: Assessing value creation via AI-enhanced financial process transformation – ResearchGate, accessed on October 4, 2025, https://www.researchgate.net/publication/394436747_The_return_on_investment_ROI_of_intelligent_automation_Assessing_value_creation_via_AI-enhanced_financial_process_transformation
  5. More uncertainty, less lending: how US policy affects firm financing in Europe – European Central Bank, accessed on October 4, 2025, https://www.ecb.europa.eu/press/blog/date/2025/html/ecb.blog20251002~1b15d67f4c.en.html
  6. How to Calculate ROI to Justify a Project | HBS Online, accessed on October 4, 2025, https://online.hbs.edu/blog/post/how-to-calculate-roi-for-a-project
  7. Total Cost of Ownership: How It’s Calculated With Example – Investopedia, accessed on October 4, 2025, https://www.investopedia.com/terms/t/totalcostofownership.asp
  8. Update on T+1 Settlement Efforts in the U.S.: Canadian Outlook and Considerations – CIBC Mellon, accessed on October 4, 2025, https://www.cibcmellon.com/en/_locale-assets/pdf/our-thinking/2021/ot202110-update-t+1-settlement-efforts-u.s.pdf
  9. What Is Operational Excellence? – IBM, accessed on October 4, 2025, https://www.ibm.com/think/topics/operational-excellence