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Wholesale markets have a rich history of serving as vital hubs for financial institutions and corporations to engage in short-term borrowing, lending, and investment activities. These markets have been instrumental in facilitating the efficient flow of funds, enabling participants to meet their funding needs, manage liquidity, and optimize their balance sheets. However, the landscape of wholesale markets has undergone significant transformations in recent years, driven by various factors that have reshaped their operations. One of the primary drivers of change has been the regulatory reforms introduced after the global financial crisis of 2008-2009. These reforms were designed to enhance financial stability, reduce systemic risks, and improve transparency and oversight in the markets. Stricter capital requirements, improved risk management standards, and greater transparency have redefined the way wholesale markets operate. In parallel, rapid technological advancements and the rise of digitization have revolutionized financial markets, including wholesale markets. Digital platforms, electronic trading systems, and algorithmic trading have emerged as powerful tools that streamline processes, improve efficiency, and provide real-time access to market information. The automation of tasks previously performed manually has led to faster execution and settlement of transactions, reducing reliance on traditional intermediaries. Furthermore, major financial crises and market disruptions, such as the global financial crisis and subsequent events like the European sovereign debt crisis, have underscored vulnerabilities within wholesale markets. These events prompted market participants to reevaluate their risk management practices, liquidity provision strategies, and the overall resilience of market structures. The lessons learned from these crises have driven efforts to create more robust and resilient wholesale market frameworks. The impact of these shifting dynamics extends to money market products, which play a crucial role in short-term financing and liquidity management. The evolving wholesale market landscape has influenced the structure, participants, and trading mechanisms of these products. Non-bank financial institutions, including money market funds and hedge funds, have gained prominence as key players alongside traditional banks. Moreover, the rise of technology-driven firms and fintech companies has expanded the range of participants in wholesale markets, injecting innovation and competition. Trading mechanisms and liquidity provision have also been transformed by technological advancements. Electronic trading platforms and algorithmic trading have revolutionized the way money market products are bought and sold. These developments have enhanced transparency, efficiency, and access to liquidity, enabling market participants to execute trades more quickly and efficiently. This shift has improved price discovery and reduced transaction costs, benefitting both buyers and sellers. Additionally, the changing dynamics of wholesale markets have spurred the development of new money market products and platforms. Digital platforms and peer-to-peer lending have emerged as alternative channels for short-term borrowing and lending, providing additional avenues for market participants to access funds. The introduction of innovative securitization and structured money market products has expanded the range of investment opportunities, allowing investors to diversify their portfolios. In conclusion, wholesale markets have experienced a profound transformation in recent years. Regulatory changes, technological advancements, and market disruptions have redefined the way these markets operate, impacting money market products and their associated trading mechanisms. As market participants adapt to these evolving dynamics, understanding the implications and challenges arising from these changes is essential for navigating the wholesale market landscape effectively.

The financial landscape is comprised of various instruments and markets that facilitate the movement of capital and financing. Among these, money market products play a pivotal role in enabling short-term borrowing and lending in wholesale markets. In this article, we will explore the nature of money market products, the significance of short-term transactions in wholesale markets, and provide an overview of cash products and their characteristics. Definition of Money Market Products Money market products refer to a class of financial instruments that are characterized by their short-term maturity and high liquidity. These products are typically issued and traded by various entities, including governments, financial institutions, and corporations, to meet their short-term funding needs. Money market instruments include Treasury bills, commercial paper, certificates of deposit, repurchase agreements, and short-term bonds. The primary objective of money market products is to provide participants with a secure and efficient means of borrowing and lending funds over short periods, usually less than one year. These instruments offer a relatively low-risk investment option while ensuring liquidity and capital preservation for investors. Importance of Short-Term Borrowing and Lending in Wholesale Markets Wholesale markets serve as the backbone of the financial system, facilitating the flow of funds between institutions and providing essential liquidity management tools. In these markets, banks and other financial institutions engage in short-term borrowing and lending activities to meet their funding requirements and manage their day-to-day operations. Short-term borrowing and lending are crucial for banks as they allow them to maintain liquidity, bridge temporary funding gaps, and manage fluctuations in their balance sheets. By borrowing funds in the wholesale market, banks can access capital quickly to meet their immediate obligations. Conversely, lending in the wholesale market enables banks to invest their excess funds and earn a return while maintaining a short-term investment horizon. Overview of Cash Products and Their Characteristics Cash products form a significant portion of money market instruments. These products are characterized by their high liquidity and short-term nature. They are typically issued by highly creditworthy entities such as governments, financial institutions, and blue-chip corporations. Some common examples of cash products include Treasury bills, which are short-term debt obligations issued by governments to raise funds for their short-term financing needs. Commercial paper, on the other hand, represents unsecured promissory notes issued by corporations to meet their short-term funding requirements. Cash products are attractive to investors due to their relatively low risk and ease of buying and selling in the secondary market. They provide an opportunity to park surplus funds temporarily and earn interest income without tying up capital for an extended period. In conclusion, money market products serve as essential instruments in wholesale markets, enabling short-term borrowing and lending. Cash products, with their short maturities and high liquidity, play a vital role within the money market landscape. In the following sections, we will delve deeper into the dynamics of wholesale markets, the characteristics of cash products, and the evolving nature of these markets. The Participants in Wholesale Markets Wholesale markets attract a diverse range of participants who engage in short-term borrowing and lending activities. The main participants in these markets include: Banks: Banks are prominent players in wholesale markets, actively involved in both borrowing and lending. They utilize these markets to obtain funds for their daily operations, manage liquidity, and meet regulatory requirements. Banks also lend funds to other institutions and corporations seeking short-term financing. Institutional Investors: Various institutional investors, such as pension funds, mutual funds, and insurance companies, participate in wholesale markets. They seek short-term investment opportunities with relatively low risk to optimize their portfolio returns and ensure liquidity management. Corporations: Large corporations with significant financial resources also participate in wholesale markets. They may borrow funds to cover short-term cash flow gaps or invest their excess funds in short-term instruments to earn a return. Central Banks: Central banks play a crucial role in wholesale markets by providing liquidity to financial institutions during times of market stress or crisis. They conduct open market operations, such as repurchase agreements (repos), to influence short-term interest rates and stabilize the financial system. Mechanisms of Short-Term Borrowing and Lending in Wholesale Markets Short-term borrowing and lending in wholesale markets occur through various mechanisms, including: Interbank Market: Banks borrow and lend funds to each other in the interbank market. This market facilitates short-term transactions between banks to manage their liquidity needs, adjust their reserve requirements, and optimize their balance sheets. Money Market Instruments: Money market instruments, such as Treasury bills, commercial paper, and certificates of deposit, are bought and sold in wholesale markets. These instruments provide avenues for investors to lend funds to issuers in exchange for interest income over a short period. Repurchase Agreements (Repos): Repos involve the sale of securities by one party to another with an agreement to repurchase them at a later date. This mechanism allows institutions to borrow funds against collateral, such as government bonds, for short periods. Importance of Wholesale Markets for Banks and Their Liquidity Management Wholesale markets are of utmost importance to banks, serving as a vital source of short-term funding and liquidity management. Banks heavily rely on these markets to access funds quickly, ensuring they can meet their daily operational requirements, honor deposit withdrawals, and fulfill regulatory obligations. The ability to borrow funds in wholesale markets provides banks with the flexibility to manage their liquidity needs efficiently. During times of increased demand for cash, banks can access funds rapidly, reducing the risk of liquidity shortages. Conversely, during periods of excess liquidity, banks can lend funds in wholesale markets to earn income and optimize their balance sheets. Furthermore, wholesale markets enable banks to diversify their funding sources and reduce their reliance on specific types of deposits or long-term borrowing. This diversification improves their overall resilience and reduces funding risks.

Definition of Cash Products in the Context of Money Markets Cash products, within the realm of money markets, encompass a range of short-term financial instruments that provide investors with a secure and liquid avenue for parking their funds for relatively brief periods. These instruments are typically issued by creditworthy entities, such as governments, financial institutions, and corporations, to fulfil their short-term financing needs. Common Types of Cash Products There are several common types of cash products that are actively traded in the money markets, including: Treasury Bills: Treasury bills, or T-bills, are short-term debt securities issued by governments to raise funds for their immediate financing requirements. These highly liquid instruments have maturities ranging from a few days to one year, and they are generally considered to be risk-free as they are backed by the full faith and credit of the issuing government. Commercial Paper: Commercial paper refers to unsecured promissory notes issued by corporations to finance their short-term obligations. These instruments typically have maturities ranging from a few days to several months. Commercial paper is primarily issued by highly creditworthy corporations and is considered to be a relatively safe investment due to the credit quality of the issuers. Certificates of Deposit (CDs): Certificates of deposit are time deposits offered by banks and other financial institutions. They represent a fixed-term investment with a specified maturity date and a predetermined interest rate. CDs typically have maturities ranging from a few days to one year, and they provide a secure and predictable return on investment. Features and Characteristics of Cash Products Cash products exhibit specific features and characteristics that make them attractive to investors in the money markets. Some key features include: Maturity: Cash products have relatively short maturities, typically less than one year. This short-term nature allows investors to have quicker access to their funds and the opportunity to reinvest them in other opportunities. Risk Profile: Cash products are generally considered to have a low-risk profile. Their short-term nature and high-quality issuers, such as governments and financially stable corporations, contribute to their perceived safety. However, it is important to note that no investment is entirely risk-free, and investors should still assess the creditworthiness and associated risks before investing. Liquidity: Cash products are highly liquid, meaning they can be easily bought and sold in the secondary market. This liquidity provides investors with the flexibility to access their funds quickly, should the need arise. Yield: The yield on cash products tends to be lower compared to longer-term investments. This lower yield is primarily due to the short-term nature of these instruments and the lower risk associated with them. However, cash products still provide a reliable source of income for investors seeking stability and capital preservation. Benefits of the Evolving Wholesale Market Dynamics The evolving wholesale market dynamics have brought forth several benefits: Enhanced Efficiency: The integration of technology and digitization has improved the efficiency of wholesale markets. Faster execution, automated processes, and real-time access to market information have streamlined operations and reduced transaction costs. Increased Market Access: The shift in market dynamics has expanded market access, allowing a broader range of participants, including non-bank financial institutions and technology-driven firms, to engage in wholesale market activities. This increased participation has enhanced market liquidity and facilitated a more diverse range of funding and investment options. Improved Risk Management: Regulatory reforms implemented in response to financial crises have bolstered risk management practices in wholesale markets. Stricter capital requirements and enhanced transparency have contributed to a more resilient financial system. Potential Challenges and Risks Associated with the Changes While the evolving wholesale market dynamics bring benefits, they also pose challenges and risks: Technological Risks: Reliance on technology introduces the risk of operational disruptions, cybersecurity threats, and algorithmic trading errors. Market participants must continuously invest in robust technological infrastructure and risk management systems to mitigate these risks. Liquidity Risk: The changing dynamics of wholesale markets may impact the availability and stability of liquidity. Market participants need to ensure they have access to sufficient liquidity during periods of stress or market volatility. Market Fragmentation: The proliferation of new platforms and market participants may lead to market fragmentation, making achieving price transparency and liquidity more challenging across different venues. This fragmentation can potentially hinder efficient price discovery and increase transaction costs. Impact on Market Participants and Their Strategies The evolving wholesale market dynamics have influenced the strategies and operations of market participants: Banks: Banks have had to adapt to changes in market structure and competition. They may need to reassess their business models, adjust their liquidity management strategies, and explore new avenues for revenue generation. Non-Bank Financial Institutions: Non-bank financial institutions, such as money market funds, face evolving regulatory requirements and increased scrutiny. They must navigate changing market conditions while ensuring the preservation of capital and liquidity for their investors. Technology-Driven Firms: Fintech companies and technology-driven firms have capitalized on the evolving dynamics to offer innovative solutions and platforms in wholesale markets. They have the opportunity to disrupt traditional market practices and provide efficient services to market participants. Regulatory Considerations and the Role of Central Banks Regulatory considerations play a crucial role in shaping the evolving wholesale market dynamics. Regulatory reforms aim to promote stability, transparency, and fairness in wholesale markets. Regulators focus on enhancing risk management practices, reducing systemic risks, and fostering market integrity. Central banks play a significant role in wholesale markets, particularly in times of financial stress or crisis. They provide liquidity support, conduct open market operations, and implement monetary policy measures to stabilize markets and ensure the smooth functioning of the financial system. Additionally, central banks and regulators collaborate to oversee market participants, monitor systemic risks, and address any potential threats to financial stability. Summary Throughout this article, we have explored the world of money market products and the evolving dynamics of wholesale markets. Here's a recap of the key points discussed: Money market products are short-term financial instruments used for borrowing and lending in wholesale markets. They have a maturity of typically less than one year and offer features such as low risk, high liquidity, and lower yields compared to longer-term investments. Wholesale markets play a crucial role in facilitating short-term borrowing, lending, and investment activities for financial institutions and corporations. Traditionally, they operated through established networks and manual processes, but the landscape has transformed with regulatory changes, technological advancements, and market disruptions. The shift in wholesale market dynamics has led to changes in market structure, trading mechanisms, and the introduction of new products and platforms. Non-bank financial institutions and technology-driven firms have become more prominent participants, and electronic trading systems have enhanced efficiency and transparency. The evolving wholesale market dynamics bring benefits such as enhanced efficiency, increased market access, and improved risk management. However, challenges and risks, including technological risks and market fragmentation, must be carefully managed. Market participants, including banks, non-bank financial institutions, and technology-driven firms, have had to adapt their strategies and operations to navigate the changing landscape. Regulatory considerations and the role of central banks are essential in shaping wholesale market dynamics and ensuring stability, transparency, and fairness.

It seems that everyone is utilising artificial intelligence. However, the narrative often contrasts sharply when I engage in personal conversations. For instance, I recently learned about a bank investing millions in developing models. Yet, when talking with its employees, many claim they aren't using or even aware of such tools. It's astonishing to see such a sophisticated strategy that overlooks the basics. Large Language Models (LLMs) like GPT-4 are democratising AI, granting anyone the potential to enhance their tasks — the only requirement is the willingness to embrace it. My work habits have changed. I used to search online or ask colleagues for answers. Now, I often turn to Large Language Models (LLMs) like GPT-4. This change comes from trying them out and seeing their efficiency. They save time and often give better results. So, I wonder, why aren't more businesses using tools like this? Inertia: Human nature tends to favour the familiar. We often gravitate towards tried-and-tested methods, which can prevent us from fully understanding or appreciating the capabilities of new innovations. Time Constraints: Our existing commitments often demand immediate attention, leaving little energy or inclination to venture into the unfamiliar territory of new tools and technologies at the end of the day. Turf Wars: Technology adoption can often be perceived as an IT-centric endeavour. This perception can lead to extensive processes like planning, development, testing, roll-out, and sign-offs. Solutions that bypass these stages can introduce inter-departmental friction. Access Restrictions: Most workplaces have strict protocols around software installations and gaining permission often involves multiple layers of approval, making spontaneous adoption challenging. Security Concerns: Data is a valuable asset. The threat of breaches or leaks can deter companies from trying new tools. Unauthorized access can benefit competitors and prove costly and damaging to a company's reputation, especially for regulated entities. Misinformation Risks: LLMs, though powerful, can sometimes generate inaccurate or misleading narratives. Associating your name or brand with such content can have unintended consequences. Even with these challenges, it's important to note that progress has its ups and downs. With the right planning and approach, we can overcome these obstacles and pave the way for new ideas and growth. Many businesses already rely on third-party services for data management, so outsourcing is not a novel concept. There's an underlying principle of mutual interest at play. Service providers are just as invested in preserving the integrity of the data they manage. Any breach, misuse, or unauthorised copying of client data would jeopardise their own business model and reputation. Do LLMs occasionally generate incorrect, biased, or misleading outputs? Certainly. But this doesn't render them unreliable. We have safeguards in place, one of which is human oversight. Just as we review spreadsheet results for accuracy, we can and should scrutinise LLM outputs to ensure they align with our expectations. Blindly accepting any tool's output without questioning or validation can lead to issues down the road. It's disheartening to see many businesses overlooking the potential of AI. In doing so, they also disadvantage their employees. As AI becomes increasingly integrated into industries, professionals unfamiliar with its capabilities risk being outpaced by those who can use it. Deploying these models can yield immediate benefits, and the investment of time and effort is minimal. A structured approach, spearheaded by a board-empowered project sponsor, can streamline the integration process. Here's a simple plan: Sign Up: Register for GPT-4 or its enterprise version. Empower the Workforce: Distribute access among your employees and encourage them to experiment. Feedback Mechanism: Set up a centralised system where users can document their experiences, uses, and feedback. This will provide invaluable insights into the model's real-world applications. Training: Although intuitive, a brief demonstration on how to effectively prompt the model can save hours in the long run. Review & Adapt: After a few weeks, review the innovative applications your team has discovered. The financial incentives alone, such as the time saved in drafting documents, make this endeavour worthwhile. The proposition is straightforward and cost-effective. Why not take the leap? Getting Started with GPT-4 While GPT-4 doesn't come with a manual, here are some steps: Registration: Opt for the paid version, priced at $20/month for individual access Switch it on: Navigate to settings and grant yourself access Experiment: Use the prompt window to test various queries. Toggle Features: Explore different modes to familiarise yourself with the model's capabilities. From here, it's all about learning through experience.

In this article, I explore the capabilities of GPT-4 in assessing a firm's interest rate risk. My objective is to provide a practical perspective on how advanced models like this can be applied in real-world scenarios. The insights garnered along the way not only shed light on future trajectories but also highlight potential pitfalls to be wary of. The Starting Point I've put together a straightforward spreadsheet that illustrates the typical interest rate exposure a small bank or building society might encounter. This often takes the form of long-dated fixed-rate assets funded by shorter-dated floating-rate liabilities. In such a setup, profits materialize if interest rates fall, while losses ensue if they rise. Such exposure is typically managed using various techniques. One of the most basic is the "gap report," which relies on a basis point value or delta calculation. For this analysis, we'll keep things uncomplicated. I believe a simpler approach allows us to better comprehend how the model can assist us. The initial step involves asking GPT-4 to reformat the information, ensuring it's presented in a manner the model can easily interpret and engage with. GPT 4: Next, I prompt GPT-4 to perform a DV01 analysis. It's important to note that I provided no additional cues, purely because I wanted to gauge its intuitive understanding of my request. Impressively, GPT-4 not only grasped the essence of what I intended but also laid out a set of assumptions it would be operating under. GPT 4: I ask it to continue. GPT 4: GPT-4 not only presents the DV01 calculation but also articulates its results in plain English, bringing to light some potential limitations of such an analysis. However, an unexpected discrepancy arises. Based on my calculations, I anticipated a risk figure around £106,000, but GPT-4 returns an estimate of £138,000. This raises the question: is there an error in our methods? A closer examination reveals methodological differences. GPT-4 employs a textbook duration approach, whereas I utilize a more hands-on discount function method. This latter technique involves adjusting the yield curve by a basis point. To determine if this variance in methods is the root of our discrepancy, I prompt GPT-4 to emulate my approach and provide its results. GPT 4: Our results are now closely aligned, though slight differences persist. Without diving too deep, these variances likely stem from GPT-4's preferred approach versus mine. Admittedly, this can sound rather technical, but there's an essential lesson to be drawn from it. GPT-4 has adopted a methodology that, while not incorrect, might diverge from prevalent industry practices. It has behaved akin to a diligent academic. This highlights a need to watch out for such outputs: they're not wrong, just potentially misaligned with expectations. Once such disparities are identified, GPT-4 can be directed to adjust its response in the context of specific industry practices, which it does commendably. In a novice's hands, GPT-4's output might be accepted without scrutiny, but an experienced practitioner can ensure the proper alignment. This raises a contemplation: given such a straightforward example, could LLMs potentially replace the conventional software tools used for risk management? It's a question I aim to delve into in the future, but the winds of change seem to be stirring. Having conducted some technical analysis, let's pivot to an area where GPT-4 truly shines: writing. Could it draft a memo to my supervisor elucidating the risk we've discussed? Let's find out. GPT 4: As anticipated, the memo is succinct and zeroes in on the primary exposures, setting a clear agenda for subsequent actions. Impressively, this was achieved in a single attempt by GPT-4. The memo effectively highlights the main concerns, suggests potential resolutions, and points to further analyses that might be beneficial. To provide some perspective, the quality and depth of the memo are such that I would expect it from someone with several years of experience in the field. What are the implications? The ability to rapidly produce high-quality documents undoubtedly boosts productivity. This efficiency implies we can either accomplish more with our current workforce or, in the future, require fewer personnel for the tasks we handle today. Many treasurers and risk managers—no offence intended—aren't always the most adept at drafting reports. Many would welcome a tool that both expedites and elevates the writing process. In fact, with GPT-4's assistance, one might be tempted to entrust the role of a risk analyst to an eager new graduate. Yet, I'd advise caution, especially recalling our earlier hiccup with the calculation method. While GPT-4 can certainly elevate the capabilities of less experienced individuals, it doesn't replace the nuanced understanding and seasoned judgment that veterans bring. It's a balance that organizations must thoughtfully consider as they move forward. Considering the complexities, it might be beneficial for management to grasp the nuances between the two risk calculation methodologies. To this end, I decided to test GPT-4's adeptness in elucidating the distinctions between the two approaches. GPT 4: While the output from GPT-4 provides a solid foundation, it may not be immediately ready for distribution. Some fine-tuning would be necessary, but the overarching essence and direction are promising. With more precise prompts, the quality of the output could be enhanced swiftly. To further illustrate GPT-4's capabilities, I tasked it with crafting a high-level paper. This paper would explain the rationale for shifting from one risk methodology to another—a task treasurers and risk managers often confront. It becomes evident that GPT-4 offers an efficient and streamlined approach to draft content tailored for diverse audiences. GPT 4 It’s short and to the point but not entirely what I was expecting as it’s now brought in issues (like non-parallel shifts) that I wasn’t anticipating. But it was a one-shot request that could easily be refined by further prompting. Key Takeaways from the Exercise: Capability and Application: GPT-4 can craft risk reports reminiscent of real-world standards. While this exercise isn't exhaustive proof of handling a real-life balance sheet, there's a strong indication that, given the right input and guidance, GPT-4 can produce results comparable to traditional systems. Understanding Computer-Generated Output: Accepting computer-generated information unquestioningly can lead to pitfalls. GPT-4's edge lies not just in generating numerical data but in framing it within coherent, natural language narratives. This offers remarkable benefits but requires users to approach with experience and discernment, ensuring they don't fall into potential traps. GPT-4's Writing Prowess: As touched upon in prior sections, GPT-4 excels in articulation. It can swiftly render intricate explanations in chosen formats. This proficiency, especially in saving time and elevating report quality, presents a compelling case for adopting the enterprise version of the model. Considering the time saved and the enhanced clarity in reports, the cost of GPT-4 can be quickly justified. Future Outlook: GPT-4 and LLMs are still in their nascent stages, with the latter being accessible for under a year. Yet, the transformative potential of these models is evident. They herald a shift in risk evaluation and reporting. Compared to the rigid algorithms of traditional systems, LLMs' ability to process and query data in natural language, especially given banks' vast unstructured data, signals an imminent paradigm shift. This evolution might unfold more rapidly than anticipated, perhaps even outpacing the transformative journey of the internet.

In today's rapidly evolving technological landscape, large language models (LLMs) stand as a testament to the strides we've made in artificial intelligence. However, harnessing their full potential in the professional realm remains a challenge. While tech companies excel in creating these advanced models, their grasp of the nuances of specific business sectors often falls short. To truly capitalize on the impending AI revolution, it's imperative to discern how LLMs can be integrated effectively within our workplaces to enhance both the quantity and quality of output. As insiders in our respective industries, we possess unique insights that place us in an advantageous position to experiment with AI applications. Rather than waiting for the perfect solution, it's often more rewarding to dive in headfirst, exploring first-hand the benefits and limitations of these models. In this article, I aim to shed light on one specific use case: managing basis risk. For those unfamiliar, basis risk, in its most basic sense, refers to the discrepancy when one pays and receives interest rates based on differing reference indices. It's a multifaceted risk, but for the sake of clarity, I've distilled its essence. Delving into how an LLM, specifically GPT-4, can aid in analyzing this risk has yielded some enlightening findings. Facing Initial Hurdles My exploration began with a straightforward approach: uploading a spreadsheet for the model to interpret. However, this method immediately ran into some snags. Surprisingly, GPT-4 struggled to comprehend the data I provided. This was unexpected, especially given the rave reviews I'd encountered regarding its data analysis capabilities. Switching to the default mode—bypassing the advanced settings—yielded better results. GPT-4 seemed more adept at evaluating the data in this configuration. Interestingly, I observed that spreadsheets, especially those with statistical data, don't mesh well with GPT's advanced data analysis. The model often requires significant prompting and guidance to navigate them effectively. Undeterred, I ventured further by using the advanced data analysis feature. I prompted GPT-4 to convert the spreadsheet into a format it found best to use and thereby facilitate a better conversation about the findings. The outcome? A vast improvement, which I'll delve into in the subsequent sections. GPT4: Diving Deeper into Risk Analysis I set the model on a task to gauge the risk in terms of delta value. Specifically, I wanted to understand the implications if the interest basis shifted by a mere 0.01%. GPT-4 accurately pinpointed the net basis risks, producing a potential profit and loss figure that could arise from such a change. Beyond just the numbers, the model went on to elucidate the constraints and limitations inherent to its analysis, providing a better understanding. GPT4: I then posed another challenge: a scenario analysis. I proposed a situation where the base rate decreased by 0.5%, while Libor increased by 0.25%. Without missing a beat, GPT-4 crunched the numbers, laying out the potential repercussions on profitability. Just as before, it highlighted certain caveats to consider in the analysis. GPT4: However, beyond the raw numbers and scenarios, understanding the 'why' is equally crucial. I asked the model to decipher the reasons behind such disparities in interest rates. Our exchange offered insights into the economic dynamics that might sway a balance sheet, rounding out a holistic picture. GPT4: Exploring Hedging Strategies with GPT-4 Our conversation progressed to potential strategies for hedging the identified risk. While GPT-4 offered suggestions, they leaned more towards the generic, lacking the specificity I had hoped for. It felt as if the model was, metaphorically, hedging its own bets, casting a wide net rather than zeroing in on a direct solution. Maybe I should have probed more incisively, pressing for a more exact answer. It's an avenue I plan to explore more deeply in future discussions on hedging techniques. GPT4: One common approach to managing basis risk is adjusting the margin on financial products. For instance, one might consider reducing savings rates while raising borrowing rates. However, this strategy is far from straightforward, carrying its own set of advantages and pitfalls. When I prompted GPT-4 to unpack these, it responded, touching on several pivotal concerns. GPT4: To cap off our session, I asked the model to draft a memo for the board. The objective was to succinctly capture the risks we had dissected and propose potential measures to mitigate those exposures. While GPT-4's initial draft was competent, it would undoubtedly benefit from some fine-tuning. Here’s the memo. GPT4: Key Takeaways using GPT-4 in this context From this deeper dive into the capabilities of GPT-4, several insights emerged: Embracing the Future: Any organization yet to experiment with GPT, even in a sandbox environment, is potentially missing out. The trajectory of GPT's impact remains somewhat nebulous, but its utility in swiftly identifying, analyzing, and elucidating risks is undeniable. By rapidly generating documents and explanations that would conventionally drain resources, GPT offers a glimpse into the future of business operations. Balancing Expectations with Reality: Banking on AI to have all the solutions right out of the box will lead to disappointment. The quality of GPT's output can oscillate - sometimes it's stellar, other times less so. It underscores the importance of leveraging your own expertise to sift through the results. Adeptness in Economic Analysis: GPT-4 consistently shines in its ability to dissect economic scenarios and their potential ripple effects on businesses. This isn't surprising, given its training on a wealth of textual data. Versatile Documentation: GPT's proficiency isn't limited to drafting memos for board members. It can adeptly tailor content to resonate with diverse audiences, presenting a promising avenue for customizing internal communications. In essence, while GPT-4 heralds a new frontier in AI-driven solutions learning how to apply it will be the big challenge going forward.

AI is coming for knowledge work, the problem is that we don't know how it will affect us and the businesses we work in. It has the potential to rapidly alter well-paid white-collar jobs, and that's something we have little choice over. However, we can experiment with the technology and learn new ways to do things. The speed at which these changes are happening means that they will affect strategic decisions, and boards will need to respond accordingly. One of the major issues is the disconnection between those who understand AI and those who make strategic decisions. This has the potential to lead to some very unfortunate consequences, not just for the business but for society. Any rapid adoption of AI that leads to higher unemployment will be socially disruptive, but that's not all. Many employees find fulfilment in their work, and if you automate the creative and interesting aspects of what they do, you risk adversely affecting morale, reducing engagement, and creating a weaker culture. But this doesn't mean ignoring what's going on. AI can increase productivity and creativity, and if it can be successfully applied to your business, market competitors will threaten your long-term viability if you don't utilise it. I know I struggle with AI because it has seemingly come from nowhere, and it has just been let loose on the public, largely as a result of having a natural language interface (just like ChatGPT). I don't think I'm alone in discovering that it's a whole lot better at doing things than expected, and as every day passes, I am more impressed. If we assume that we are all starting from a low base, how do you build up the mental muscle that allows you to understand enough to make sensible strategic decisions? Part of the answer is surely experimentation. If you try ChatGPT you will find it's straightforward, and you can learn some practical skills just by varying the prompt. Taking this a step further, you could build a safe space where you could collectively work with colleagues to improve executive AI fluency. This is not the same as coding; it's about developing a mental mindset that gives you insight into what AI can do for your business. Practical suggestions could include taking a document and repurposing it, asking it to explain some data trends, and building a new advertising campaign but don’t use proprietary data until you are clear about where it goes. It doesn't take a lot of imagination to see that just about every part of a board's information deck could, in the future, be AI-generated and nuanced for the reader, who would have the ability to summarise and drill down as required. But that's only the start. In the industry I'm familiar with, financial services, there's a huge amount of proprietary data held across a multitude of systems. That data is frequently in documents, contracts, and spreadsheets, making it very hard to access. There's also a deep history of legacy data that resides somewhere, and all of this is held together with tape and string. Now, with AI this data becomes extremely powerful. It can help you price and manage risk like it's never been done before. Underwriting credit and insurance risk immediately springs to mind. There's also a separate business case that takes data and sells access to it through an interface like a chatbot. This may sound fanciful, but it's something that's already being done by firms like Bloomberg. These two business models won't suit everyone, but there is a third way: to pick the low-hanging fruit and implement AI as and when it becomes commercially available from third parties and embedded in systems and applications that you already use. Examples include using chatbots for customers and implementing fraud detection systems to identify irregular patterns of behaviour. How you go about this depends on whether you can make smaller incremental changes leading to increased efficiency or if you are pushed into much larger, rapid changes to respond to competitors. There are also limitations to what can be achieved. In a regulated industry, you need to be aware of what data you can use and how you can use it. If you get this wrong, there are serious financial and reputational risks; therefore, banking cannot be as entrepreneurial as many lightly regulated businesses. Furthermore, regardless of the path you take, the results are only as good as the information used in the model, and we know that a lot of banking data has been subject to manual intervention which embeds errors. It also seems evident that if decision-makers can't explain how a model is trained, how it works, and what its limitations are, the regulator is not going to be happy. This is particularly important when considering GDPR and the principles of treating customers fairly, as well as the application of complex risk management techniques. There's no doubt that enterprising employees are already using AI in their personal workspaces, and for better or worse, it will leak into your business. Providing them with guidance on what is acceptable is a good place to start. Overall, AI experimentation and understanding are essential in navigating the changing landscape of knowledge work. By embracing the potential of AI, businesses can increase productivity, creativity, and strategic decision-making. However, it is crucial to consider the potential consequences, such as job displacement and employee morale, and find ways to mitigate them. With proper experimentation, collaboration, and a thoughtful approach, AI can become a powerful tool to drive growth and success in the business world.

After a bit of practice, I decided to put it to work on something practical and asked it to generate a series of reverse stress scenarios suitable for a building society. The results were good, but there was nothing particularly new. I then asked it to be more adventurous, and one of the things it came back with was the business impact of ChatGPT - you can see the result below. What surprised me was the ability for it to be creative and quickly produce something that's useful. If I wanted to, I could drill down into every aspect of what it has written. What's clear is that AI is coming for knowledge work, and the change may be relatively fast. It's hard not to think that things will look very different within five years, so this case study is something that decision-makers need to consider. Case Study: Building Society X - Reverse Stress Test on Automation Impact: ChatGPT and Workforce Displacement Background: Building Society X, a reputable building society with a focus on providing personalized financial services, recognizes the potential impact of AI and automation on its workforce. The top team, consisting of the CRO, CEO, and Chairman, acknowledges the need to conduct a reverse stress test to assess the society's vulnerability to job displacement caused by the implementation of ChatGPT, an advanced AI-powered chatbot. The society aims to navigate this transition while ensuring the well-being of its employees and maintaining a high level of customer service. Objective: The objective of this reverse stress test is to evaluate Building Society X's vulnerability to job displacement resulting from the introduction of ChatGPT. The test aims to identify potential risks arising from automation, assess the impact on the workforce, and develop strategies to mitigate the potential adverse effects on employees and customer service. Scenario: The reverse stress test focuses on a hypothetical scenario where Building Society X implements ChatGPT to enhance customer service efficiency and streamline operations. ChatGPT is an AI-powered chatbot capable of handling customer inquiries, providing financial advice, and performing various transactional tasks. The scenario assumes a significant adoption of ChatGPT, resulting in the potential displacement of a substantial number of employees across customer service, administrative, and operational roles. Process: Scenario Development: The top team, in collaboration with relevant stakeholders, develops the specific parameters of the scenario, considering the potential scope and scale of ChatGPT's implementation. They examine industry trends, technological advancements, and customer preferences to ensure the scenario reflects a plausible future state for the building society. Risk Identification: The team conducts a comprehensive assessment to identify potential risks associated with workforce displacement. They consider the impact on employees whose roles may become redundant or significantly altered due to automation. They also assess the potential effects on employee morale, customer satisfaction, and the society's reputation as a customer-centric organization. Impact Analysis: The team evaluates the potential impact of job displacement on Building Society X's workforce, including aspects such as employee retention, skills gaps, and workforce morale. They assess the potential consequences on employee engagement, productivity, and the ability to deliver personalized financial services. Additionally, they analyze the potential impact on customer satisfaction and loyalty, considering the changing dynamics of human-AI interactions. Workforce Transition Strategies: The team explores various strategies to mitigate the adverse effects of workforce displacement and ensure a smooth transition. They identify opportunities for reskilling and upskilling affected employees to prepare them for new roles that leverage their existing expertise while incorporating technology. Additionally, they consider initiatives to enhance employee engagement, such as fostering a culture of continuous learning, promoting collaboration, and providing support for career development. Customer Experience Optimization: Recognizing the importance of maintaining exceptional customer service, the team devises strategies to optimize the customer experience in a hybrid human-AI environment. They evaluate how ChatGPT can enhance service efficiency, provide seamless customer interactions, and ensure that customers receive personalized assistance when needed. The team explores ways to balance the advantages of automation with the value of human interaction, emphasizing the society's commitment to customer-centricity. Change Management and Communication: The team formulates a comprehensive change management plan to effectively communicate the adoption of ChatGPT and address potential employee concerns. They establish transparent channels of communication to keep employees informed about the automation implementation, the rationale behind it, and the support available for them during the transition. The plan emphasizes open dialogue, employee feedback mechanisms, and proactive measures to mitigate any negative impact on the workforce. Conclusion: By conducting a reverse stress test on the impact of ChatGPT and workforce displacement, Building Society X's top team gains valuable insights into potential risks and vulnerabilities.

Hedging mortgages with swaps is a common practice for financial institutions looking to manage interest rate risks associated with their mortgage portfolios. When hedging mortgages, two primary strategies emerge: cash flow hedging and basis point value (BPV) hedging. These strategies are influenced by the accounting method employed, with accrual accounting favouring cash flow hedging and mark-to-market accounting favouring basis point value hedging. It is important to understand how these approaches impact the hedge ratios of a mortgage book. Cash Flow Hedging and Accrual Accounting: Cash flow hedging involves aligning the cash flows from the mortgage portfolio with the cash flows on the swap thereby hedging interest rate risk as fixed rate interest is converted to variable rate. Accrual accounting, which focuses on recognizing revenue and expenses when they are earned or incurred, is typically used to account for cash flow hedges. Under accrual accounting, the objective is to hedge the future cash flows of the mortgage portfolio, ensuring that the income generated from the portfolio remains relatively stable despite fluctuations in interest rates. This approach allows financial institutions to match the interest income received from the mortgage portfolio with the interest payments made on the swap contract. The hedge ratio for cash flow hedging is determined based on the projected future cash flows of the mortgage portfolio and the swap contract. It aims to offset changes in interest rates by ensuring that the interest income and interest expense remain closely aligned. This approach provides a level of predictability and stability in cash flow generation, thereby reducing the impact of interest rate movements on the financial institution's earnings. Basis Point Value (BPV) Hedging and Mark-to-Market Accounting: Basis point value hedging focuses on managing the market value of the mortgage portfolio relative to changes in interest rates. This approach is often employed when mark-to-market accounting is used, which involves valuing assets and liabilities based on their current market prices. With basis point value hedging, the hedge ratio is determined based on the BPV of the mortgage portfolio and the swap contract. The BPV measures the sensitivity of the mortgage portfolio's value to changes in interest rates. By actively adjusting the hedge ratio, financial institutions aim to offset the impact of interest rate fluctuations on the market value of the mortgage portfolio. Mark-to-market accounting provides a real-time view of the market value of the mortgage portfolio and the swap contract. This approach enables financial institutions to assess the effectiveness of their hedge positions and make adjustments as necessary. By managing the risk through basis point value hedging, financial institutions can protect the market value of their mortgage portfolios and mitigate potential losses arising from interest rate movements. The choice between cash flow hedging and basis point value hedging for hedging mortgages with swaps depends on the accounting method used and the specific objectives of the financial institution. Accrual accounting favours cash flow hedging, which emphasizes stable cash flow generation, while mark-to-market accounting favours basis point value hedging, which focuses on managing market value and potential losses. Financial institutions must carefully evaluate their risk management objectives, regulatory requirements, and accounting practices when determining the most suitable hedging strategy for their mortgage portfolios. By understanding the nuances and implications of cash flow hedging and basis point value hedging, institutions can effectively manage interest rate risks and optimize their overall portfolio performance. Perspective The first time I encountered a variation on this risk was in the early 1990s, while working at a bank, I faced an unusual situation while hedging fixed-rate bonds with swaps. The challenge emerged because we were hedging bonds issued by Brazil and Mexico, which traded on a higher yield curve due to their credit risk. The bank's system recommended a hedge ratio of $10m bond to $7m swap, deviating from the conventional 1:1 ratio that aligned cash flows on an equal basis. This unexpected result gave two different but potentially correct hedge ratios depending on the purpose of the hedge. A 1:1 ratio was appropriate for a buy and hold, accrual investment and a 1:0.7 ratio was suited to a traded mark-to-market situation. It should be noted that this latter dynamic hedge would need constant rebalancing as a result of changing interest rates. At first sight, this may appear a million miles from mortgage hedging but the challenges encountered in hedging these bonds bore similarities to those faced in mortgage hedging, requiring financial professionals to manage interest rate risks and balance cash flows or market values effectively. This is brought home by a more recent encounter with mortgage hedging. In this situation, the mortgages had uncertain maturities, although they were predominantly long-dated. The bank, under regulatory pressure, was required to mark-to-market (MTM) the mortgages using a yield curve that reflected the relevant credit risk. Similarly, the swaps were also marked-to-market. Despite employing cash flow hedging with a 1:1 basis for the swaps to hedge the mortgages, significant profit and loss fluctuations occurred. As interest rates fell, the basis point values of the swaps increased at a faster pace than the mortgages due to differences in convexity. Since the bank paid a fixed rate on the swaps, it experienced increasing losses as interest rates declined. This situation presented a dilemma: should the bank shift from managing the risk through cash flow hedging to basis point value hedging? This dilemma consumed considerable management time as they grappled with finding the optimal solution. Ultimately, a compromise was reached, acknowledging the imperfect nature of the situation. The chosen hedge ratio fell between the two extreme scenarios. This compromise recognized that hedging is not a black and white issue. Real-world factors such as regulation, accounting practices, capital requirements, and management perceptions all play significant roles in shaping the approach to hedging. By understanding the nuances and trade-offs involved, financial professionals can navigate the complexities of hedging in a more informed manner. Balancing the objectives of risk mitigation, accounting requirements, and capital optimization is key to achieving successful hedging outcomes in the real world.

If you run your own money this may save you ten years of work, (honestly). My investment journey started over thirty years ago. I didn’t think too much about it, returns were high, my money went into “funds” and who worried about costs? It was easy to do some simple maths, the message couldn’t be clearer, if you wanted to keep your money, costs had to be low. At the time that meant paying at least 1% per annum plus hidden trading commissions. Herein was a problem over an investment lifetime even “modest” fees of 1% lead you to hand half of the investment return whilst still taking all the risk. It’s the simple maths of compounding and it’s like going to a restaurant, paying for a three-course meal and just getting the starter and desert. I started to read about indexing, something that had got going a lot earlier on the other side of the Atlantic. The pioneer, the late John (Jack) C. Bogle, had set up the mutual fund group Vanguard in 1974. My first introduction to his writing was “Don’t Count On it!” Now, over the years I’ve read quite a lot about investment. I really like Buffett’s style, Munger is pithy, Fisher asks the questions and Graham, well he’s the guru. But, to be honest, I’m just not as smart as they are. This is where Bogle differs, his strategy can be followed by anyone. Put 40%-60% in low-cost index trackers, the rest in low-cost bond funds and leave it well alone. It’s a case of the less you do the more you get. It’s even the strategy Buffett has followed for his wife’s trust. This year I picked up a copy of Bogle’s book “The Little Book of Common Sense Investing”, it’s an investment classic and I recommend reading it. In it he sets out the case for low-cost index investing in easy to read and insightful manner. There’s a lifetime of work here but some things really hit home, and I’d like to share them with you: Long run GDP growth is everything: Cumulative long run performance of the stock market is governed by GDP growth; one follows the other. Stock returns: Stock returns have two sources, investment return (dividend yield and earnings) and speculative returns, (the price of those earnings as expressed as the price/earnings multiplier). Investor returns: Returns earned by all investors must equal returns earned by the market. However, returns received by all investors equal returns earned by the market less fees and expenses. Outperformance: All evidence shows that over the long run market outperformance is random. So, although some win over short periods it is not something that can be done consistently. For this reason, by using low-cost index funds, your chance of beating the performance of a fund manager is 90% over 10 years and 98% over 50 years. We are all indexers: If you imagine a pie of all investors’ money and cut out the slice that is index tracking the remaining investors, as a whole, have to be indexers too. But individually they hold different positions and in the short run some win, and some lose. Collectively this group incurs significant trading costs that act as a drag on their returns. Market prediction: Short run predictions are guesses, but dividend yield, earnings and P/E can be used to provide a predictive look at what returns may be in the next ten years, the conclusion is that we are looking at substantially lower returns (3%) on a balanced portfolio over the next decade. After inflation of 2% and investment costs of 1.5% that’s a flat or negative real return only avoided by getting rock bottom costs. Behavioural bias: This plays against the investor. They tend to buy funds that have gone up and sell those that have gone down. This buy high, sell low, strategy acts as a drag on returns leading to a reduction in the order of 1.5% per annum. Keep it simple: Between 1926-2016 total market annual return was 9.8%, for the S&P 500 it was 10%. The S&P 500 index is about 85% of the value of all US stocks and being capital weighted doesn’t need rebalancing. The winning strategy is to own the index at minimum cost and let compounding work in your favour. Exchange traded funds: Bogle’s resistance to ETFs is clarified, he did not like the way they encourage trading but held for long periods broad range low-cost ETFs achieve much the same as an index fund. Asset allocation: Investment performance is dictated by asset allocation; Bogle considers the merits of 50/50 working up to 80/20 stock/bond portfolios. In essence it depends on the risk you can afford to take and what you feel comfortable with. What’s particularly thought-provoking is the common sense view that lower investment costs mean that you can take significantly less risk to achieve the same level of return. It’s not often less leads to more, and what I particularly like is that Bogle is an amusing writer, full of anecdote but above all backs up his case with irrefutable analysis where you can clearly see that he’s on your side. Don’t take my word for it, read the book and in the years to come you won’t regret it, after all he is setting out a strategy, where you will have an investment portfolio, that is worth at least a third more, or as I put it earlier, the option to retire early. I’m sure you too will see John Bogle as one of the greats in the investment world.

What's first strategy or risk appetite? A regular feature in the Investors Chronicle is a column called “Portfolio Clinic”, it evaluates a reader’s investments, appetite for risk and their retirement plans. Often the reader gets a shock - their assumptions about risk and return do not match and they are forced to adjust their plans. In this way investment strategy and risk appetite are inextricably linked. Businesses too have strategies that are closely connected with the risk they are prepared to assume. Firms that need to operate with tight safety, (nuclear power, refining, aircraft manufacturing), have strategies that recognise this as failure is costly - Tokyo Electric, BP, Boeing. In entrepreneurial sectors (technology, pharmacology, alternative energy) the strategy recognises greater variance in returns and the risk appetite accommodates. On this scale I’m sure we would agree that building societies are “conservative”. But does risk appetite drive strategy or vice versa? (A thought-provoking question for any NED). On the "edge" The strategy surely identifies the “edge” your business has and how best to exploit it.  It is only after defining your strategy to benefit from your perceived advantage that you can really see what it means in terms of risk. If you start with the risk appetite and attempt to fit the strategy around it, you are likely to miss out on identifying and exploiting the franchise your business has to offer. To be clear, I’m not suggesting an aggressive lending strategy that overrides risk but by putting the strategy before risk and using iteration to make sure that both work together you end up with a better outcome. It’s about recognising the idiosyncrasies of your business and where possible exploiting them within a risk appetite you are comfortable with. Notwithstanding I recognise that risk appetite in some Societies does drive the strategy and this leads to a further engaging question. How do you determine what your risk appetite is? You could respond with reference to solvency, profitability, franchise, resources, skill, expertise and experience – all sensible starting points. However what matters is the process. Does top down direction work with bottom up insight, to agree an appropriate risk limit? You can now see that by linking member value and the need to operate for extended periods under difficult conditions NEDs can explain how the risk limit(s) supports the strategic objective(s) of the society. What do I see? I’m not privy to what happens in all the sector’s forty plus Board rooms however I know Boards don’t find this easy. Published documents show that some Societies could do more to link strategy and risk together. Furthermore, I’m sure you can find instances in your own firm where it’s hard to determine the role strategy and risk take in driving what you do. When this happens, a common underlying factor appears to be regulation or the fear of falling foul it. As a consequence, sometimes we are guilty of trying to gauge the regulator’s appetite for risk (a bit like spoof). Whilst this may give NEDs some personal comfort it’s not what the regulator intends and what’s more it isn’t benefiting members per se. In Portfolio Clinic, it’s akin to holding only Gilts – great in the short run but ultimately a lot riskier than a balanced portfolio mainly because the return will be sub-optimal. It may seem unfashionable but making money is still important. Summary Strategy and risk appetite are closely linked. My view is that to get the most out of your business strategy comes first; There is an iterative process that connects strategy to your risk appetite and in this way a sensible balance is the outcome; I acknowledge that some Societies may put risk appetite as the driving force – if this is you, where does the risk appetite come from, is it the Regulator? NEDs may feel personally comfortable keeping risk to a minimum, but they should ask whether this is in member’s interests.

Capital isn't gold bars Capital adequacy is right up there in terms of importance and quite rightly too as it’s a measure of the ability to absorb losses. What’s a lot harder to nail down is the way we manage the interest rate risk on capital and that is what this article addresses. The capital or reserves arise from the net value of the firm’s assets and liabilities. In theory it’s what would be available for distribution if we liquidated the business. In the normal course of things capital isn’t identifiable as gold bars sitting in the safe or as an investment in Gilts it’s just used in what we do. But on paper we can isolate it and when we do, we appear to have two businesses, not one. The first is borrowing and lending, the second is asset management - that’s about getting a return on the owner’s wealth. The two are normally linked together by calculating the return on capital. By way of example, a balance sheet of £100m, of which £10m is capital, with a net profit of £1m has a 10% return on capital. Very good in the current environment but should this return fall beneath the rate on safe investments you are subtracting from the owner’s wealth not adding to it. Capital therefore belongs to the shareholders or members and although not specifically identifiable it is deployed in the business. It should provide a return and that return should exceed what is obtainable from passive investment - otherwise why not close the business and hand back the money? Interest rate risk In managing our first activity, borrowing and lending, we limit the mismatch between fixed and variable interest rates in the balance sheet. Without appropriate hedging, changes in interest rates would affect valuations and earnings. One of the main tools we use to monitor this risk is an interest rate gap report. In this, both assets and liabilities are placed into time buckets dependent on when the interest rate is repriced. So, a three-month deposit goes into the three-month bucket, a five-year gilt into the five-year bucket and a five-year variable rate mortgage in the three-month bucket, (because it’s capable of being quickly repriced) and so forth. The gap report shows how much risk we take when the yield curve moves and from here, we appreciate the gains and losses we may face if and when interest rates change. When the risk is considered too large interest rate swaps are used to manage things. If you take a close look at the gap you will see the capital position and it’s my bet that it’s treated differently and doesn’t have a specific time bucket allocated to it. In effect it’s treated as an overnight risk. Is this the right thing to do? Let’s consider the implications. The capital could be placed in one of three time buckets: Overnight 30 years, (representing a long way into the future) Anywhere in between The overnight position would lead to no apparent interest rate risk and wouldn’t need hedging. The 30-year position would lead to a much greater risk and would need to be hedged. Since the capital is a liability the hedge would require a receive fixed swap of a similar maturity. Does this mean that overnight is the correct place? As I discussed earlier the balance sheet can be thought of as two businesses and by excluding the reserves, the day-to-day interest rate risk is all about that arising from borrowing and lending not that from the capital itself. This has implications for earnings. By putting the capital in the one-day bucket it earns the overnight interest rate and by putting it out longer you get the 30-year yield.The decision on what to do must therefore depend on: The interest rate risk appetite on the capital itself, (how far do you want to invest it). Your expertise and judgement on the level of interest rates and the shape of the yield curve. How your balance sheet responds to higher and lower interest rates. It should also be noted that the default position of placing capital in an overnight bucket may appear risk free but is nevertheless at one end of the spectrum. At the time of writing receiving at 0.54% on a 30-year GBP swap doesn’t feel a great place to be in hedging the interest rate risk on capital (but then I remember rates in double digits, and this must affect my opinion). Consideration also needs to be made to the effect of any further falls in rates and their potential to compress margins further. Balanced approach It may therefore be worth evaluating a more balanced approach, for example, spreading equal quantities of capital across some of the shorter dated time buckets, say 20%, 1-5 years. If interest rates increase you are in effect investing capital at rates beneath the market rate and if rates fall further, you are investing capital above the market rate.  This could be beneficial if negative rates lead to further margin compression. With both scenarios the investment horizon is sufficiently short to see a fifth of it rolling off each year. What I find surprising, is that, presumably most firms see their business as long term but invest their capital as if it’s going to disappear tomorrow. I suspect this is largely due to the fact that this is a difficult topic to “nail-down” and by doing nothing you appear not to lose money, well not unless rates go down further. Therefore, it’s something that should be on the Board agenda. Summary Capital is the net value of assets and liabilities - the owner’s funds; It’s normally invisible and is used in the business; For banks and building societies when you separate the capital you have two businesses – borrowing/lending and capital investment; We limit the interest rate risk on our balance sheet; Where is the capital in the interest rate gap? It could be overnight, 30 years or in between; Where should it be? What are the implications in terms of hedging and earnings? How does this fit in with your earnings profile and risk appetite? It’s something the Board should have an opinion on.