The parallels to 1999 are unmistakable. Then, the Internet promised to change the world – and it did – but not before vaporizing trillions of dollars of investor capital. The exuberance of that period was not built on ignorance but on conviction: everyone knew the Internet would matter; they simply misjudged how much, how soon, and at what cost. Today’s AI boom carries the same blend of insight and excess. The technology is real and the applications are profound, but the economics remain unproven. Faith in AI’s inevitability has replaced analysis of its profitability.

AI-related spending has become one of the principal drivers of U.S. economic resilience. According to multiple estimates, it added roughly one percentage point to U.S. GDP growth in the first half of 2025.¹ In the second quarter alone, the ten largest U.S. technology firms – Alphabet, Meta, Amazon, Microsoft, Tesla, Apple, Nvidia, Oracle, Broadcom, and IBM – spent at an annualised rate of US$426 billion on capital investment, up 73 percent from a year earlier.² Economy-wide technology spending as a share of GDP has now surpassed the peak seen during the late 1990s.

That surge in investment has powered not only GDP but also stock markets. Since the launch of ChatGPT, the combined market value of these ten companies has increased by nearly US$10 trillion, equivalent to roughly one-third of annual U.S. GDP. Historically, the early stages of repricing around transformative technologies are rational; capital chases genuine innovation. But enthusiasm has a way of turning to excess. Whether the current stampede will prove wise depends on the economic return on investment, a question the data have yet to answer.

The near-term math is sobering. Total revenue for the “big ten” reached about US$2.4 trillion (seasonally adjusted annual rate) in the second quarter, up 15 percent from a year earlier.³ Revenue growth, however, remains well below the pace of investment, lifting the group’s capex-to-revenue ratio from 10 percent in early 2022 to roughly 18 percent today.⁴ Most profits still derive from established franchises—advertising for Meta, iPhone sales for Apple—rather than from AI itself. In other words, the AI story is driving valuations while legacy businesses are funding the experiment.

History also suggests that genuine productivity benefits from new technologies take longer to materialise than investors expect. While there are promising micro-level efficiencies, U.S. labour productivity has not yet broken decisively above its multi-decade trend. Despite the fanfare, the macro concern across advanced economies remains sluggish productivity growth. A recent MIT study found that 95 percent of organisations experimenting with generative AI are earning zero return on their investments.⁵

The problem is not intent but arithmetic. The AI buildout is exceptionally capital-intensive and depreciates at an extraordinary pace. Unlike traditional infrastructure such a poles, wires, and transmission lines that last decades, the useful life of GPUs and related processing units is measured in years, not decades. Each generation of chips is rendered obsolete by the next within three to four years, compressing the window for earning an adequate return. The physical data centres and power connections will endure; the computational core will not. As a result, the hurdle rate for profitability is far higher than most models imply. It is increasingly difficult to see how current market enthusiasm can be reconciled with the investment’s short economic life.

The AI boom’s initial financing has come largely from equity, venture capital, and retained earnings. Big Tech’s profitability and deep cash reserves gave it an extraordinary ability to self-fund early stages of the revolution. But as investment needs swell, particularly for data centres, semiconductor fabrication, and power generation, companies are turning decisively toward credit markets. Debt, not equity, will fund the next leg of AI’s expansion.

The scope has already shifted. According to Bank of America, AI-linked technology firms issued more than US$75 billion in U.S. investment-grade debt during September and October 2025 alone, more than double the sector’s average annual issuance of US$32 billion over the past decade.⁶ The total included US$30 billion from Meta and US$18 billion from Oracle, plus new borrowing from Alphabet and others. Barclays now identifies AI-related issuance as the key determinant of U.S. investment-grade supply in 2026,⁷ while J.P. Morgan estimates that AI-linked companies account for 14 percent of its investment-grade index, surpassing U.S. banks as the largest sector exposure.⁸

These are not speculative borrowers in the traditional sense; they are profitable, well-capitalized enterprises with legitimate business cases. Yet the sheer scale and complexity of their financing structures are giving investors pause. Meta’s US$27 billion off-balance-sheet financing with Blue Owl Capital – the largest private-credit deal ever recorded – keeps debt off Meta’s books but transfers it into opaque vehicles owned by yield-hungry investors. Such structures may provide flexibility, but they also disperse risk into corners of the market that are illiquid and difficult to price. It is this migration of leverage from transparent to opaque balance sheets that prompted the Bank of England to warn of “pockets of vulnerability” within the global financial system.⁹

The deeper the industry digs into infrastructure, the more circular its financing becomes. OpenAI owns a warrant to buy up to a 10 percent stake in AMD. Nvidia has invested roughly US$100 billion in OpenAI. Microsoft, OpenAI’s largest shareholder, is also one of Nvidia’s biggest customers, accounting for nearly 20 percent of its revenue, and a major client of CoreWeave, another Nvidia-backed venture. Oracle, for its part, is financing OpenAI’s US$300 billion data-centre buildout, which OpenAI will pay for using capital from investors who rely on Nvidia’s chips.

It is an ecosystem in which suppliers finance customers, competitors invest in one another, and equity stakes blur the distinction between partnership and exposure. The arrangement may be efficient in theory, but in practice it resembles the reflexive financing that defined the late-1990s telecom boom, when equipment makers lent to carriers who borrowed to buy more equipment. When funding tightened, the illusion of demand evaporated. The same pattern could repeat: when the flow of capital slows, valuations and balance sheets will adjust together.

The credit footprint of AI now extends well beyond the investment-grade market. High-yield issuance tied to AI has surged, with TeraWulf, a bitcoin miner turned data-centre operator, raising US$3.2 billion in BB-rated bonds, and CoreWeave issuing US$2 billion in high-yield debt earlier this year. These deals are small relative to the broader market, but they mark the familiar progression of optimism down the credit spectrum.

Private credit is also becoming a significant source of financing. UBS estimates that private-credit loans to AI-related borrowers nearly doubled over the twelve months through early 2025.¹⁰ Morgan Stanley projects that non-bank lenders could supply over half of the US$1.5 trillion required for the global data-centre buildout through 2028 (Figure 1).

Securitization is re-emerging as well. Digital-infrastructure asset-backed securities (ABS) – bonds backed by long-term rent payments from data-centre tenants – have expanded eightfold in five years to about US$80 billion. Bank of America expects that figure to reach US$115 billion by the end of 2026, with roughly two-thirds of issuance tied directly to data-centre construction.¹¹ These instruments are standard financing tools, but their proliferation in a short span and their dependence on illiquid assets echo the layering that preceded the global financial crisis.

Figure 1: Morgan Stanley (MS) estimates for how the global data centre buildout will be financed (October 2025)

In this way, the logic of the cycle is self-reinforcing. Companies that spend the most on infrastructure drive the narrative that justifies further investment by others. As in past technological manias, optimism migrates from valuation to financing. The danger is not that the technology fails, but that the return profile cannot keep pace with the capital committed to it. The wisdom of this stampede will ultimately be judged by whether the economic return on investment matches its financial cost.

None of this diminishes AI’s transformative potential. Like the Internet before it, the technology will ultimately reshape how economies function and how capital is deployed. But revolutions of this scale follow a familiar pattern: optimism, overinvestment, correction, and consolidation. The productivity gains outlast the financial losses, but the investors who finance the early excess seldom own the eventual winners. Railroads in the 19th century, electricity in the early 20th, and fiber-optic networks at the turn of this century all followed this trajectory. AI will be no exception.

For credit markets, the implications are clear. As capex rises faster than cash flow, companies will lean more heavily on debt. Investment-grade issuance will expand further, private credit will deepen its exposure, and securitization will spread risk into less transparent corners of the system. The temptation for lenders will be to chase yield by funding complexity. The wiser course will be to underwrite what is real (i.e., cash-flowing, collateralised, and verifiable) rather than what is fashionable.

From a lending standpoint, AI’s credit dimension is still young but growing fast. For now, the borrowers are large, liquid, and well-rated. But as the ecosystem broadens, the risk will migrate to smaller operators and second-tier suppliers, where disclosure is weaker and covenants thinner. That is where underwriting discipline will matter most. The current environment rewards patience. We prefer to lend into dislocation, not euphoria. And to finance the infrastructure that survives the shake-out, not the speculation that precedes it.

For the moment, AI investment continues to underpin growth, earnings, and sentiment. But beneath the surface, leverage is building, complexity is rising, and transparency is declining – the same combination that has marked every great market inflection. The credit cycle is always quieter than the equity cycle until the very end. When the adjustment comes, it will not disprove the promise of AI; it will simply reprice the capital that made it possible.

[1] Macrobond, September 2025
[2] Macquarie Economics, September 24, 2025
[3] Bloomberg
[4] Macquarie Economics, September 24, 2025
[5] https://nanda.media.mit.edu/
[6] BofA Global Research, October 15, 2025
[7] Barclays Research, October 2, 2025
[8] “At $1.2 Trillion, More High-Grade Debt Now Tied to AI Than Banks,” October 7, 2025, Bloomberg
[9] “Bank of England warns of growing risk that AI bubble could burst,” October 8, 2025, The Guardian
[10] “Private Credit-Powered AI Boom at Risk of Overheating, UBS Says,” August 18, 2025, Bloomberg
[11] BofA Global Research, October 15, 2025

The post Echoes Of 1999: Equity Euphoria, Credit Consequences (Q3-25) appeared first on Third Eye Capital.

Consider Canada’s largest supermarket chains, home-grown Loblaw and U.S. retail giant Walmart. They emerged from the pandemic with record earnings, fortified balance sheets, and even greater negotiating leverage over their supply chains. Their size allows them to dictate pricing, shelf placement, and promotional calendars to co-packers and smaller food producers. These dominant players can lock in multi-year retail pricing with their customers and pass most cost increases upstream, while simultaneously imposing “vendor programs” that reduce the supplier’s take even further: early-payment discounts, slotting fees, promotional chargebacks, and penalties for missing on-time-in-full delivery targets.

For a smaller producer, these terms are not optional; refusing them risks losing the contract entirely. The result is a self-reinforcing dynamic: the retailer preserves its own gross margins and cash conversion cycle by pushing working capital and volatility onto the supplier. The supplier’s margin erosion reduces its ability to invest in automation, product innovation, or marketing, further cementing its dependence on the buyer. Over time, this dependence makes it even harder to negotiate better terms, ensuring the imbalance perpetuates itself.

It is not simply that the big players negotiate harder. They set the price to the end customer, and then cascade the economics backwards, telling suppliers to make it work. In concentrated markets like Canada’s, where a handful of companies can make or break an industry, these terms are not just aggressive, they are de facto standard.

In the supply agreements and vendor guides that govern these relationships, there is no visible malice, only a quiet but relentless transfer of margin. A co-packer for a national grocery chain will find its gross price whittled away by promotional chargebacks and service-level penalties, even as it is told to hold extra stock to ensure on-time delivery. A precision manufacturer in aerospace will invest in new machining capacity at a customer’s request, only to watch volumes lag and annual “cost-down” clauses eat away at unit prices. An energy services contractor may win a coveted “preferred vendor” slot with a resource major, then discover that it is expected to maintain crews and equipment on call, unpaid, until the next job. The common element is a mismatch of leverage. The customer can diversify or reshore or simply move to the next bidder; the supplier has fewer options and less time.

In good years, these pressures are masked by growth, cheap credit, or both. A thin-margin supplier can refinance or roll over its bank line, paying today’s bills with tomorrow’s sales. But when inflation runs through materials and freight, when interest rates reset upward on floating debt, when borrowing bases shrink because a single large customer now accounts for too much of receivables, the mathematics change. The erosion of a few hundred basis points of margin becomes a liquidity crisis. Debt service that was easily covered at six percent profit margins cannot be met at one percent. The default is not a shock; it is the predictable end state of a system in which the stronger party captures the upside and shifts the downside.

In Canada, the problem is sharper than it is in the U.S. Our corporate landscape is more concentrated, and our banking sector more unified in its approach to risk. When a borrower breaches a covenant, it is not just one lender pulling back; the whole sector tends to tighten in unison. Asset-based lending availability is re-margined with new reserves, cutting liquidity at the worst moment. For middle-market companies that have already stretched their vendors and drawn down their lines to meet customer demands, the withdrawal of bank support can be terminal. They arrive in formal restructuring proceedings – NOIs and CCAAs – later than they should, having exhausted their cash to keep supplying a customer that cannot or will not adjust the terms.

We have seen it play out in every sector we touch. In one case, a Western Canadian industrial supplier saw its largest OEM customer cut prices annually under a long-term agreement, levy new quality penalties, and insist on expensive retooling without guaranteeing volume. Margins went from healthy to barely positive in a year; the senior lender, wary of concentration and foreign receivables, slashed the borrowing base. In another, a national food distributor’s revenue grew in step with a major retailer’s promotions, but cash drained away through deductions and extended terms. The facility that funded its inputs was suddenly reduced because too much of its receivables sat with one debtor. In both cases, the companies were not failing because they had lost their markets or their competence. They were failing because the contractual architecture of their business relationships left them unable to capture enough value to pay their bills.

The most dangerous shocks in this environment are those that give the stronger party cover to hold the line or push harder: tariffs, for example. Large public companies often manage to turn trade disruptions to their advantage, re-engineering supply chains, pushing price increases downstream, and using the disruption to consolidate share. Their smaller suppliers, lacking the same strategic options, see costs rise and orders fluctuate without any compensating change in terms. A ten percent tariff on key inputs can be absorbed, in theory; but, in practice, for a supplier on thin margins with variable-rate debt, it becomes a debt service problem within months.

When the squeeze comes, traditional credit often makes things worse. Loan agreements are designed to protect lenders from loss, not to give borrowers time and flexibility to adapt. Covenants are based on trailing numbers; borrowing bases are sensitive to reserves for slow-moving stock or concentrated receivables; cross-defaults can shut off new money overnight. Customers with their own working-capital targets to hit may stretch payables further or impose new demands on suppliers, knowing the bank, not they, will be left holding the risk. The result is a narrowing corridor in which management can operate, and a rising probability that the situation will tip into formal default.

It is in these corridors that we operate. The businesses we step into are almost always in the grip of this corporate class system – caught between a demanding customer and an inflexible lender, with margins too thin to carry the load. Solving the problem means more than injecting capital. It means renegotiating the terms that caused the problem, diversifying the customer base even at the cost of short-term revenue, and changing operations so that cash is generated by every unit of output, not just booked as throughput. It often means using court-supervised processes not as a threat but as a way to organize competing interests, freeze unhelpful behaviour, and push through changes that would be impossible in bilateral talks.

These are granular, unglamorous fixes: inserting indexation clauses into supply contracts; capping aggregate penalties and chargebacks; securing take-or-pay minimums in exchange for investment; reclaiming early-payment discounts in price; aligning production schedules with actual, profitable demand. They are not negotiated in a single meeting; they are won over months, with credible alternatives in hand and the discipline to walk away from unprofitable volume. They are paired with changes on the floor and in the yard – shorter production runs to reduce rework, stricter WIP control, maintenance to improve uptime, freight planning to end premium shipments.

None of it works without capital that is anchored in the assets and structured to survive the drama that comes with change. Fancy layered financings collapse when a single customer dispute consumes the cash budget. We favour simple, senior positions underwritten to the value of what can be sold or collected, with equity earned through milestones like contract resets or customer diversification. Sometimes we acquire the existing debt to control the process; sometimes we provide super-priority facilities to fund the fix. In every case, the capital is there to buy time for operational and contractual change, not to subsidize structural unprofitability.

The corporate class system is not a passing condition. It will keep reallocating margin to those with brand, channel, and balance-sheet power, and it will keep pushing volatility onto those without it. In Canada’s concentrated markets, the effects are magnified; in a world of higher rates, they are accelerating. For most lenders and investors, this is a reason to pull back from the middle market. For us, it is a signal of where the next complex situations will arise, and a reminder that the defaults, the distress, and the inevitable drama of these relationships are not anomalies, they are features of the system itself. The companies caught in it can survive and even prosper again, but only if someone is willing to change not just their capital structure, but the rules of the game they have been forced to play.

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In our world, each investment is a unique situation, shaped by a highly specific set of circumstances that defy algorithmic generalization. The analysis is neither formulaic nor mechanical. It demands something else entirely: discretion informed by context, a capacity to synthesize the idiosyncratic, and judgment honed through the repetition of non-repeatable events. The very nature of special situations investing means we are engaging where others have stepped away: with companies in transition, in distress, or at an inflection point where conventional financing is no longer accessible. There is no dataset large enough to encode how a founder’s or management team’s motivations will evolve when facing a cash crunch, or how a key supplier’s trust will influence a turnaround. Nor can AI models reliably predict the behavioral dynamics of teams under duress, or the strategic thinking of regulatory authorities, creditors, and stakeholders in moments of dislocation.

The process of assessing credit risk in these situations is deeply interpretive. It is grounded in a nuanced understanding of capital structure, asset value, and downside protection – but equally in an appreciation of context: What are the incentives of the owners? Is the management team capable of adapting under stress? Is the path to recovery defensible not just contractually, but practically? We often encounter incomplete information, contested narratives, and legal ambiguity. These are not obstacles to be sidestepped but rather the terrain. Our methodology is not about extrapolating from clean data but about evaluating under uncertainty, diagnosing root causes of business stress, and identifying what is salvageable and what is not.

Recent commentary from venture capitalist Marc Andreessen captures this distinction well¹. He notes that the most valuable skills in his business are “psychological” – understanding how people behave under pressure, anticipating their reactions, and guiding them through ambiguity. He describes venture investing not merely as capital allocation, but as psychological navigation. The same holds true in our domain. What separates a successful special situations investor is not access to a better model, but a more accurate reading of motives, incentives, and real-world constraints.

It would be misguided to suggest that AI cannot augment this work at the margins. We already employ technology to support many of the mechanical aspects of due diligence – for example, in processing large document sets, modeling downside cases, and analyzing comparative trends. But these are tools in service of a fundamentally human exercise: constructing a view of reality from fragmented, often conflicting information, and deciding what action, if any, has merit. The essence of our strategy remains interpretive and interventionist, not predictive, because the investment decisions we face are not reducible to spreadsheet logic or sentiment analysis. Consider, for example, the restructuring of a complex capital stack in a jurisdiction where creditor protections are uncertain, asset values are contested, and management alignment is fragile. Or the assessment of a family-owned enterprise undergoing succession amidst balance sheet distress and strategic dislocation. These are decisions where capital flows not on the basis of data completeness, but on trust, control levers, and an ability to navigate what is left unsaid.

Some have asked whether AI’s ascendancy in direct lending suggests a similar trajectory for the broader credit market. We see it differently. In fact, the expanding use of AI in conventional credit may well increase the value of human discretion in complex investing. As more capital flows toward standardized risk, guided by AI-enhanced underwriting tools, the opportunity set in our segment only widens. What we offer is not speed or low-cost execution, but a willingness to engage in the difficult, the mispriced, and the misunderstood. That willingness is underpinned by specialization more than scale and by the kind of insight that comes only from having worked through past cycles, restructurings, and recoveries, often where information was limited and pressure was acute.

We see this dynamic already playing out in the divergence between sponsored and non-sponsored lending. In sponsored transactions, loan structures are increasingly pro forma, reliant on sponsor-calibrated EBITDA adjustments and covenant-lite packages. These deals lend themselves to AI underwriting, because the inputs are normalized and the outputs – primarily spread and loss assumptions – are modeled from a wide base of historical data. In contrast, in the non-sponsored, special situations market where we operate, the capital structure is often bespoke, the documentation negotiated not off a template but a strategy, and the exit dependent on active intervention.

We do not believe AI will replace what we do. Not because we resist change, but because the work itself is, by its nature, resistant to commodification. Our value lies in discernment, in negotiation, in the orchestration of outcomes that would not occur without deliberate human engagement. These are not skills that can be learned from data alone. They are learned through experience.

Importantly, the real risk in complex credit is not modelable risk, but unpriced fragility: shifting collateral realities, hidden intercreditor disputes, or counterparty behavior under stress. It is worth recalling that in the lead-up to the 2008 financial crisis, it was the structured products – AAA rated, algorithmically priced – that misfired most catastrophically. The lesson was not that models were useless, but that they were blind to emergent behavior. In the same way, AI may replicate credit ratings and improve underwriting throughput, but it cannot substitute for the practitioner’s judgment in assessing unstated risks, opaque governance structures, or adversarial restructurings.

This is not just theoretical. Recent default episodes, such as the Serta Simmons capital structure controversy or the Envision Healthcare uptiering dispute, reveal a market increasingly characterized by litigation risk, document arbitrage, and first-mover advantage among lenders. These scenarios require investors to think not only about cash flows, but about process, control, and recovery paths. As Harvard Law Professor Jared Ellias and Duke Law Professor Elisabeth de Fontenay recently chronicled in their article about the meteoric rise of private credit, credit investing today is as much legal strategy as it is financial analysis.² This is precisely the space we inhabit.

In a financial world increasingly shaped by automation, we believe our strategy represents a durable and necessary counterpoint: one grounded in the conviction that complexity still requires judgment, and that outcomes still depend on the quality of decisions made by people with the experience to navigate what others overlook. As parts of the credit market become increasingly commoditized, we remain focused on opportunities that cannot be packaged, traded, or automated. The value we create is not found in higher leverage or broader distribution. It is found in being able to identify and resolve complexity that others cannot or will not engage. That is not something artificial intelligence is equipped to do. It is something only experience can.

[1] https://fortune.com/article/mark-andreessen-venture-capitalism-ai-automation-a16z/
[2] Ellias, Jared A. and Elisabeth de Fontenay. The Credit Markets Go Dark. The Yale Law Journal. January 2025.

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Yet history tells us that markets have an uncanny ability to make fools of those who believe the past no longer applies. From the rise of the automobile and telephone in the 1920s to the internet revolution of the 1990s, every period of disruptive innovation has fueled the same euphoria, leading investors to abandon arithmetic in favor of fantasy. Arthur C. Clarke famously observed that “any sufficiently advanced technology is indistinguishable from magic.” The problem is that investors, time and again, mistake this perceived magic for a license to ignore economic fundamentals.

Despite all the transformative technological breakthroughs of the past two decades – smartphones, e-commerce, cloud computing, automation, and now AI – both U.S. GDP growth and S&P 500 revenue growth have actually slowed, not accelerated. Since 2000, these measures have averaged just 4.5% annually, lower than in the preceding half-century.¹ One would think that with all the world-changing innovations since 2000, growth would be faster, not slower. It has not.

The lesson? The impact of innovation on markets follows a familiar pattern: early profits surge, valuations reach extremes, competition intensifies, margins compress, and the long-term benefits accrue mostly to consumers, not to speculative investors chasing unsustainable narratives.

Stock market gains have come disproportionately from a handful of mega-cap technology companies, just as past bubbles have been led by their own anointed winners. Investors convince themselves that this time, the winners are different – immune to the laws of competition and destined to outgrow the economy indefinitely. But history suggests otherwise. Every speculative peak has been defined by a cohort of “must-own” companies that were expected to dominate forever. Yet over time, market leadership is constantly disrupted. Consider:

• In 1929, the dominant companies were industrial titans tied to automobiles, oil, and consumer goods.
• In the 1960s and 1970s, it was electronics, aerospace, and the Nifty Fifty, with IBM, Kodak, and Xerox at the forefront.
• In 1999, it was internet pioneers like Cisco, Intel, and Microsoft, commanding valuations that assumed decades of uninterrupted dominance.

Each of these groups was seen as invincible at the time. Yet as the speculative peaks unwound, reality set in: profit margins normalized, growth expectations were revised downward, and stock prices suffered, sometimes for decades.

Today’s AI obsession is following the same script. Companies like Nvidia, Broadcom, Super Micro Computer, and Palantir have been propelled to astronomical valuations based on the assumption that AI will drive perpetual earnings growth. But just as with past cycles, competition will intensify, margins will contract, and much of AI’s economic benefit will accrue as consumer surplus rather than as permanent excess profits for investors.

A common argument supporting today’s elevated stock valuations is that corporate profit margins are structurally higher due to technology and globalization.² While these factors have played a role, they are not the primary drivers of margin expansion over the past three decades. The most significant driver has been declining interest rates. Since 1990, corporate EBIT margins (earnings before interest and taxes) have barely increased. The key reason net profit margins have expanded is that companies have enjoyed steadily lower interest expenses thanks to falling borrowing costs. In 2020-2021, many companies locked in historically low rates, further extending the illusion of permanently high margins.

Now, with interest rates back at more normalized levels, this tailwind is disappearing. Companies that relied on cheap debt to drive earnings growth will face rising borrowing costs, compressing margins and exposing just how unsustainable much of the past decade’s profit expansion really was.

Investors make the repeated mistake of basing valuations on excitement rather than arithmetic. When a company or sector is growing rapidly, it is tempting to project that growth forward indefinitely. But even in industries experiencing massive technological shifts, growth follows a trajectory that is not a straight line:

• In the early 1900s, the rise of the automobile revolutionized transportation, yet the auto industry soon became fiercely competitive, with margins eroding over time.
• The 1960s and 1970s saw the explosion of electronics and computing, yet even dominant players like IBM saw prolonged periods of underperformance as competition caught up.
• The 1990s internet boom saw enormous enthusiasm around online commerce and software, but investors who bought at peak valuations had to wait nearly 15 years just to break even.

Investors today, enthralled by the promises of AI, risk making the same mistake. Yes, AI will be transformative. But the assumption that AI will deliver indefinite profit expansion for a select few companies is an illusion. Capitalism ensures that competition eventually narrows the gap between innovation and economic reality.

The psychology of speculative bubbles follows a predictable sequence:

1. A real, transformative innovation emerges.
2. Investors extrapolate early successes into an unsustainable growth trajectory.
3. Capital floods into the sector, driving valuations far beyond reasonable levels.
4. Margin pressures, competition, or macroeconomic forces begin to erode profitability.
5. The unwinding begins, leaving latecomers with steep losses.

This pattern has played out in every major speculative cycle – from the railroads of the 1800s to the dot-com crash to the SPAC frenzies of recent years. It is unfolding again in today’s AI-driven stock boom.

Speculation thrives on narrative, not numbers. When investors begin justifying extreme valuations with phrases like “new era,” “paradigm shift,” or “this time is different,” history warns us to be skeptical. Yes, technology is evolving rapidly. Yes, AI will reshape industries. Yes, some of today’s dominant companies will continue to succeed. But that does not mean profit margins, revenue growth, and market leadership will defy gravity indefinitely.

Ultimately, long-term wealth is built not by chasing momentum, but by respecting arithmetic, recognizing cycles, and maintaining discipline. The history of markets is clear: those who buy into speculative frenzies based on the belief that they are witnessing magic will, in the end, find themselves in the disappearing act.

[1] John Hussman, November 2024
[2] “AI stocks aren’t in a bubble”, Goldman Sachs, September 18, 2024

The post Now You See It (Q4-24) appeared first on Third Eye Capital.

Stale valuations, or delayed adjustments to asset prices, are an inherent characteristic of private credit due to the market’s illiquidity. Unlike publicly traded debt instruments, private credit loans do not benefit from regular price discovery, making it difficult to gauge their true market value. This creates several systemic risks. For instance, in evergreen or open-end funds, where investors can request redemptions at periodic intervals, stale valuations can create a first-mover advantage for better informed investors. Those with insights into a fund’s overvalued assets may choose to redeem their investments early, leaving remaining investors to bear the brunt of eventual markdowns. This dynamic increases the risk of “runs” on evergreen private credit funds, which are popular with retail high-net-worth investors, potentially destabilizing the segment. Such a risk is minimized in closed-end funds, the preferred vehicle for institutional investors, since limited partners do not have the option to redeem at will. Any attempt to exit would require a secondary market transaction, which inherently involves price negotiation, limiting the ability to exploit stale valuations.

The combination of illiquidity and infrequent valuation updates makes it challenging for external stakeholders to assess potential losses in a timely manner. This lack of transparency can undermine trust in private credit markets, particularly during periods of stress when accurate information is most critical. In such moments, market conditions shift rapidly, borrower creditworthiness deteriorates, and valuations need to reflect these realities to provide a true picture of portfolio risks. However, the lack of real-time price discovery and limited transparency leaves investors reliant on fund managers’ reporting, which is often delayed or incomplete. This lag can obscure risks, leading to poor decision-making and an inability to act swiftly to protect capital.

This challenge is further exacerbated by the incentives fund managers have to maintain high valuations, especially during fundraising periods. Strong historical returns are one of the most persuasive marketing devices managers can leverage to attract new commitments, and valuations play a critical role in shaping the narrative of a fund’s performance. By keeping valuations elevated, managers can enhance reported metrics, projecting a sense of stability and profitability that appeals to prospective investors. For institutional investors wary of the inherent opacity in private credit, such performance metrics can be reassuring, even if they are not entirely reflective of underlying realities.

The reliance on historically higher returns to market funds also aligns with the financial incentives of fund managers. Most evergreen private credit funds structure management fees as a percentage of the net asset value (“NAV”) of the fund. This creates a direct economic benefit for managers who maintain elevated valuations, as higher NAV translates to higher fees. In cases where valuations are closely tied to perceived fund size, delaying markdowns or impairments becomes not just a defensive strategy but a financial one.

This approach, however, creates a dangerous misalignment between the interests of fund managers and those of investors. Inflated valuations, while advantageous in the short term for raising capital and maintaining fees, can mask underlying risks. When economic conditions worsen or borrower performance declines, these risks can become suddenly visible, leading to a rapid deterioration in trust. Investors who relied on optimistic valuations may feel misled, particularly if losses materialize without sufficient warning. This erosion of trust damages not only the specific fund in question but the broader credibility of private credit as an asset class.

During periods of financial stress, the impact of delayed or inflated valuations becomes particularly acute. Accurate valuations are critical for investors to evaluate portfolio health, rebalance exposure, and make informed decisions about future commitments. If valuations are consistently slow to reflect impairments, investors may begin to suspect that managers are prioritizing their own interests over those of the fund’s stakeholders. This perception can lead to reduced confidence in the market and, in the case of open-end funds, redemption pressures that exacerbate liquidity challenges.

When high valuations attract new commitments, managers may be incentivized to continue delaying impairments, creating a cycle where reported performance diverges increasingly from actual portfolio health. Eventually, the delayed recognition of losses can no longer be sustained, leading to sudden markdowns that damage investor confidence and destabilize funds.

This dynamic highlights the need for private credit managers to adopt more transparent and consistent valuation practices. While accounting standards such as GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards) provide general guidance, they stop short of prescribing specific techniques for asset valuation, leaving managers with substantial discretion in how valuations are determined.

Business Development Companies (BDCs) in the U.S. offer a unique window into otherwise opaque private credit valuation practices due to their granular reporting requirements such as position-by-position accounting fair value marks. BDCs are specific investment funds created in the United States to encourage the flow of capital, primarily in the form of secured loans, to smaller companies.

A benchmarking analysis by the International Monetary Fund (IMF), which focuses on promoting global financial stability, assessed BDC valuation practices.¹ The study revealed that private credit prices tend to be less responsive to credit shocks compared to high-yield or leveraged loans, despite the higher risk profile of BDC loan portfolios. This reduced sensitivity in valuation adjustments is offset by a significant discount applied to the market prices of BDC shares (relative to their NAV). Notably, the discount tends to widen during stress periods, driven by the general market repricing of credit risk, as reflected by traded proxies for loans such as the LSTA US Leveraged Loan Index.

Evidence suggests that adjustments to private credit loan valuations are not only smaller but also slower than those observed in public markets. Such deviations often persist for several quarters, during which time share prices and net asset values gradually converge. This lag in valuation adjustments highlights the inherent challenges of assessing private credit assets, particularly in volatile market conditions. As private credit attracts more retail investors, concerns about valuation have grown. Retail participants often lack the sophistication and access to information that institutional investors possess, making them more vulnerable to the risks posed by inaccurate or inconsistent valuations. This has led to increased regulatory scrutiny and calls for enhanced disclosure and governance practices.

The regulatory framework for private credit funds has historically been light, focusing on policy documentation, governance standards, and investor disclosures, without prescribing specific valuation methodologies. This leniency reflects the assumption that institutional investors, such as insurance companies and pension funds, possess the sophistication and resources to independently assess valuation practices and make informed decisions. However, these same investors often prioritize portfolio stability and may lack the incentive to rigorously challenge fund managers’ valuations, even in cases where discretion leads to significant discrepancies in asset pricing across funds.

In response to growing concerns about valuation risks, regulators have begun intensifying oversight. The U.S. Securities and Exchange Commission (SEC), the UK Financial Conduct Authority (FCA), and the European Securities and Markets Authority (ESMA) have all taken steps to tighten governance and enhance transparency in private fund valuations. Measures include stricter requirements for independent audits, more frequent supervisory inspections, and greater scrutiny of valuation practices. Regulators are also emphasizing the importance of timely and accurate loss recognition, particularly for semiliquid funds or those approaching the end of their lock-up periods. Where valuation risks are deemed high, policymakers are considering mandates for independent external audits and more rigorous internal governance frameworks to improve valuation reliability and investor confidence.

The increasing trend of retail participation in private credit has raised concerns about herd behavior during stress periods, where redemption pressures could destabilize the market. To mitigate these risks, securities regulators are recommending measures such as longer settlement periods, closed-end fund structures, and the adoption of liquidity management tools. Under the Alternative Investment Fund Managers Directive II (AIFMD II) in Europe, which went into force in April 2024, funds that substantially engage in the granting of loans must adopt a closed-end structure. Clear and comprehensive 10 International Monetary Fund, Global Financial Stability Report: The Last Mile: Financial Vulnerabilities and Risks. April 2024 disclosures on redemption limitations and potential risks are also being emphasized by regulators worldwide to ensure retail investors are adequately informed.

These developments underscore a broader shift toward enhanced regulatory scrutiny and governance practices in private credit. As the market continues to grow and evolve, aligning valuation and liquidity practices with heightened transparency standards will be essential to maintaining trust and resilience in the asset class.

[1] International Monetary Fund, Global Financial Stability Report: The Last Mile: Financial Vulnerabilities and Risks. April 2024

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This asset-centric model is appealing for its simplicity – after all, it is easier to quantify collateral than character. Character, in the context of credit analysis, refers to the integrity, reputation, and trustworthiness of the borrower – in this case, the management team. It is an assessment of whether the borrower has the ethical standards and commitment to meet their obligations. Character is subjective and often becomes a secondary consideration in asset-based lending. In fact, many of the structural features of asset-based loans, such as lockboxes, blocked account agreements, and customer notifications, are designed to make management irrelevant. However, in our experience, character should hold equal if not more weight in the determination of making a secured loan. We have learned, sometimes the hard way that, no matter how promising a business model may appear or how attractive an asset might seem, the quality of management can determine the success or failure of an investment.

Asset values are not static, but they are generally more stable and less prone to volatility than earnings or cash flows. This relative stability is another reason why asset-based lenders often consider management quality to be of secondary importance. Unlike earnings, which can fluctuate with various market or industry conditions, and can be subject to aggressive accounting practices, assets have an intrinsic value that is less susceptible to short-term market fluctuations or management manipulation. This perceived reliability makes it tempting for lenders to focus primarily on the value of the collateral, with the assumption that even if management falters, the assets will hold their value and can be liquidated to recover the loan.

Management character is the cornerstone of how a business is run. Even the most valuable assets can quickly become worthless if mismanaged. A management team that lacks integrity, transparency, or the ability to make sound decisions can erode asset value rapidly, leaving lenders with far less than they anticipated. Poor management can lead to the misuse of assets, neglect of maintenance, or engagement in risky business practices that diminish the collateral’s worth over time. While collateral value is central to asset-based lending, the interaction between collateral and character is what ultimately determines the success of the loan. A strong collateral base can mitigate some risks, but it cannot substitute for poor management. Conversely, a management team of high character and competence can sometimes compensate for weaker collateral, using their skills to enhance asset value over time.

The synergy between these elements is where true security lies. A prudent lender recognizes that while assets provide a buffer, it is the character and quality of the management team that determines whether those assets will retain or increase their value. Evaluating management’s track record, their response to past challenges, and their overall business acumen is as important as assessing the current market value of the collateral.

The character of the management team is a non-negotiable criterion in our investment process. We believe that you cannot make a good deal with bad people. If we begin to question the integrity or character of management, our course of action is clear: we either exit the investment or replace the management. This principle is rooted in the reality that character flaws in leadership inevitably lead to deeper problems down the line – problems that can erode value, destroy trust, and ultimately lead to the failure of the business. We have observed time and again that leaders who lack integrity are more likely to engage in practices that are harmful to the business and its stakeholders. These practices can range from financial mismanagement and fraud to unethical business dealings and poor corporate governance. Such behaviors create an environment of mistrust, where stakeholders, including employees, customers, or lenders, become wary and disengaged. This erosion of trust can be fatal, especially in situations where a company is undergoing change, challenge, or complexity – the TEC “Cs” of credit!

Assets that are well-managed, well-maintained, and strategically deployed tend to appreciate or at least retain their value. Conversely, poorly managed assets can rapidly depreciate, become obsolete, or be misused in a way that diminishes their market value. Poor management, driven by short-sightedness or desperation, can effectively burn the very assets that were intended to secure the loan, leaving little to no value upon which to recover. Moreover, liquidation is not a straightforward process. The assumption that assets can be easily liquidated to recover the loan overlooks the complexities and costs associated with this process. In a distressed scenario, the market value of assets can be significantly lower than their intrinsic value due to the need for a quick sale, lack of demand, or the condition of the assets at the time of liquidation. Additionally, the legal and administrative costs of liquidation can further erode the recoverable value. If the assets are integral to the business’s operations, their value may be deeply intertwined with the company as a going concern, meaning that their maximum value can only be realized if the business continues to operate effectively – a task that requires strong management.

Furthermore, the broader impact of poor management extends beyond just asset value. Bad management often leads to deteriorating relationships with customers, suppliers, and employees, all of which can further harm the business and, by extension, the value of the assets. For instance, customer contracts may be lost, supplier terms may become less favourable, and key employees may leave the company. These are intangible losses that can have a tangible impact on the overall value of the business.

Reflecting on our firm’s history, it is evident that the majority of our defaults can be traced back to one common factor: bad management. Whether it was due to poor decision-making, lack of foresight, or unethical practices, bad management has been the root cause of many of our challenges. In several instances, we entered into deals with companies that, on the surface, appeared to be solid investments. The assets were valuable, the market opportunity was clear, and the potential for growth was significant. However, what we underestimated, and did not fully anticipate was the impact that poor management would have on these businesses and more importantly our collateral.

In one particular case, we invested in an established oilfield maintenance, transportation, and surveillance company that used helicopters and fixed wing aircraft to service rural and remote energy infrastructure for blue-chip oil and gas companies. The company was a leader in its industry that provided essential services but due to an overleveraged balance sheet had to delay critical aircraft capex. Our financing was used to restructure existing debt and provide growth capital. The financial projections were promising, strong asset values were verified through third-party appraisals, and the initial due diligence did not raise any red flags. However, as time went on, it became clear that the CEO and his management team were misallocating resources and starving critical parts of the business such as pilot training and aircraft maintenance. Management was not forthcoming about spending decisions, was resistant to feedback, and overly optimistic about their ability to execute their strategy. Despite our efforts to provide guidance and support, the management team’s poor decision-making and lack of transparency ultimately led to the company’s downfall. In their desperation, they began to cut more corners and mismanage resources, effectively “burning the ships” – the very assets that should have secured the investment – leaving us with far less than anticipated.

This experience reinforced a critical lesson for us: no amount of financial engineering can compensate for bad management. A company’s success is inextricably linked to the quality of its leadership. Strong management teams are proactive, transparent, and willing to confront challenges head-on. They are open to feedback, willing to admit mistakes, and committed to finding solutions. In contrast, weak management teams are reactive, opaque, and often in denial about the reality of their situation. They are more concerned with preserving their own image than with addressing the issues at hand, and this inevitably leads to failure.

One of the key metrics we use to evaluate the quality of management is their level of transparency. We have a “no surprises” rule at our firm – a principle that we believe is fundamental to any successful business relationship. We implore management to communicate openly and honestly with us and all other stakeholders, especially when it comes to bad news. We understand that even the best leaders and the most well-run companies can encounter difficulties. Challenges are a part of business, and mistakes are a part of being human. What matters is how those challenges and mistakes are handled.

When management is transparent about the challenges they face, it allows us to work together to find solutions. It enables us to offer our expertise and support in navigating through tough times. However, when management chooses to hide or mask bad news, it not only undermines our ability to help but also signals a deeper issue: a lack of integrity and accountability. This is unacceptable to us, and we will not tolerate it.

Transparency is not just about being honest when things go wrong; it’s about creating a culture of openness where information flows freely, and all stakeholders are kept informed. It’s about ensuring that everyone is on the same page and that there are no hidden agendas or surprises. When transparency is absent, it creates an environment of uncertainty and mistrust, which can be just as damaging as the issues being concealed.

Management quality, reflected in the character of the leadership team, plays a pivotal role in safeguarding and enhancing the value of the collateral. A loan secured by valuable assets can still be a risky proposition if placed in the hands of poor management. Conversely, strong management with a commitment to transparency and ethical conduct can transform even modest assets into valuable, sustainable security. As such, the evaluation of character should be a core component of the underwriting process, ensuring that both collateral and leadership are aligned to protect and maximize the value of our investments.

PRIVATE DEBT MARKET AND OUTLOOK

Annual loan default rates have historically mirrored the trend in corporate bankruptcy filings. The number of U.S. Chapter 11 fillings in the first half of 2024 are 39% higher than the same period in 2023, and 46% higher over the number of first half of the year filings in the ten-year period 2015-2024.2

As we have indicated in previous letters,
the number of business insolvencies in Canada has also surged at record rates. Canadian corporate bankruptcies are up a whopping 70% in the first half of 2024 from a year ago. Yet, current empirical data and survey information about the credit risk of small and middle market companies report unusually low default rates, below the historical ten-year average. So what explains this surprising anomaly?

Professor of Finance at the NYU Stern School of Business, Dr. Edward Altman, recently penned a note aimed at dispassionately assessing where we are today in the credit cycle. His research since the 1970s has looked at the U.S. credit cycle’s associations with economic expansions, slow-downs, recessions, and depressions – essentially, the business cycle. According to Altman, the credit cycle has distinct stages characterized by varying levels of interest rates, default rates, recovery rates, liquidity, and required returns for investors:

• Benign Stage: Characterized by low interest rates, below-average default rates, high recovery rates, and ample liquidity. Investors require lower returns due to favorable credit conditions.

• Average Stage: Marked by moderate interest rates, default rates, and recovery rates that align with historical averages. Liquidity is balanced, and required returns reflect standard risk and reward.

• Stressed Stage: This stage sees increasing interest rates, higher-than-average default rates, declining recovery rates, and reduced liquidity. Investors demand above-average returns due to rising risk perceptions.

• Credit Crisis Stage: Defined by very high interest rates, extreme default rates, low recovery rates, and a severe lack of liquidity. Investors require significantly higher returns as risk becomes severe, with yield spreads much above average.

Based on this spectrum of credit market activity, we appear to be in the “average” stage of the credit cycle, which is contrary to where we expected to be today and certainly disconnected from the business insolvency data. Altman agrees and has his doubts about whether default rates will continue to stay at low levels. We have previously written about the unprecedented levels of liquidity support and intervention by governments and central banks in the financial markets over the past few years, which provided a critical lifeline to many businesses, enabling them to manage their debt obligations despite challenging economic conditions. At the same time, banks were obliged to forbear from enforcement and provided relief through indefinite waivers – activity which we termed “forced compassion.” We have also highlighted the recent rise in liability management exercises (LMEs), or the negotiation of debt restructurings, that prevent an immediate default and can extend debt maturities, further contributing to the low default rates observed.

The increase in corporate bankruptcies has been heavily concentrated in certain sectors that were more vulnerable to economic disruptions, such as retail, hospitality, and energy. However, many small and middle market companies, especially those in essential or resilient sectors, have managed to maintain stable cash flows and meet their debt obligations. According to the Golub Capital Altman Index, U.S. middle market companies experienced positive year-over-year growth in sales and profits throughout the second half of 2023 and into the second quarter of 2024. This sectoral disparity has contributed to the lower overall default rates. But the presence of large, secured liabilities at bankrupt borrowers begs a different explanation.

We believe the anomaly in the relationship between loans defaults and corporate insolvencies is primarily due to the delayed impact of broader economic conditions. The effects of economic downturns and rising interest rates on default rates often lag behind the initial triggers, such as increased bankruptcies. In our opinion, the full impact of current economic conditions has yet to be felt in terms of defaults, which could manifest more clearly in the coming quarters. Recent bank earnings reports in Canada reminded investors that business loan losses can be highly unpredictable and idiosyncratic. Some of the larger losses at Bank of Montreal, for example, were to commercial loans made during the pandemic – over three years ago!

Annual default rate trends have been a leading indicator of recessions in the U.S. over the past forty years (but understandably less so in 2020, during the short-lived pandemic recession). Economic uncertainty persists on both sides of the border, but with the Bank of Canada well into its easing cycle and expectations mounting for the Federal Reserve to begin cutting rates in September, the “higher-for-longer” thesis is beginning to fade. The critical question now is not just the extent of these anticipated rate cuts, but more importantly, their timing and impact on
credit conditions, which, while not dire, are undeniably deteriorating. As we move forward, the focus will be on how effectively these rate reductions can stabilize and eventually improve the credit environment and keep loan defaults low.

The outlook for the credit cycle is anything but “average.”

Yours very truly,

Arif N. Bhalwani
President & CEO

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The rise in financing costs over the past two years combined with slowing economic growth have posed severe challenges for many businesses. Since 2022, the debt-servicing costs for publicly listed businesses headquartered in Canada have surged dramatically, following a period of decline in the preceding years (see Chart 1).

Chart 1: Interest expenses as a share of earnings, 4-quarter moving average
Source: Financial Stability Report 2024, Bank of Canada.
However, this data captures just 0.25% of the approximately 1.3 million active businesses with employees in Canada, and therefore understates servicing costs since most publicly-listed businesses primarily finance through fixed-rate bonds rather than floating-rate bank loans. In addition, about two-thirds of these bonds currently have a remaining maturity of five years or more, meaning for many listed firms, financing costs will not increase for some time. Although interest costs as a share of earnings remain below pre-pandemic levels, they are poised to escalate in the coming years as existing debt is refinanced at higher interest rates.

The financial strain on businesses is intensifying especially for smaller firms. The number of businesses in Canada filing for insolvency, which had been unusually low during the pandemic, has now surpassed pre-pandemic levels by a large margin. Business insolvencies in Canada surged by 87.2% in the first quarter of 2024 compared to the same period last year, marking the sharpest increase in the 37-year history of records from the Office of the Superintendent of Bankruptcy. This unprecedented rise underscores the profound economic challenges that Canadian businesses are currently facing. Over two thousand (2,003) businesses filed for insolvency in the first quarter of 2024, marking the highest volume since the recession in 2008.

The surge in insolvencies over the past 12 months has generally been: (i) concentrated among small businesses, (ii) broad-based across industries, and (iii) driven by bankruptcy filings rather than insolvency proposals. A rebound in business insolvencies from pandemic lows was to be expected. Some businesses may have been sustained throughout the pandemic by government support programs and the forced compassion of banks. Successive pandemic lockdowns also disrupted the insolvency filing process, particularly the use of bankruptcy courts, leading to a backlog of cases.

Notably, small and medium-sized businesses face unique challenges today, often lacking the resilience and access to capital that larger companies possess, leaving them with more limited options for restructuring. Moreover, some business owners may opt to cease operations altogether rather than pursue formal insolvency proceedings. These business failures are not included in the official insolvency statistics.

Filings for large debtor restructurings under the Companies’ Creditors Arrangement Act (CCAA) jumped over 40% in the first quarter of 2024 compared to the same period last year. For ten years until the pandemic in 2020, there were an average of 34 CCAA filings per year. In 2020, 60 CCAA proceedings were commenced, a record-high consequence of the pandemic. For the twelve months ending March 31, 2024, there were an astounding 72 CCAA filings, with 24 of them in the first quarter of 2024 alone. Given that the average length of a CCAA proceeding as measured from the date of the initial order to the date of the monitor’s discharge is 620 days (or slightly more than one year and eight months), the actual outcome of the latest proceedings is still unknown. The best CCAA outcome is when a debtor company uses the breathing space provided by the proceedings to restore solvency without needing to reorganize or liquidate. No plan is presented to creditors, no assets are sold, and the debtor company exits as a going concern business. Unfortunately, less than 1% of all CCAA proceedings result in such an ideal outcome. The total liabilities represented by debtors in CCAA filings during the first quarter of 2024 were $4.2 billion, $2 billion more than the previous quarter, and two-thirds of which constituted secured credit.

The rising number of insolvencies and CCAA filings highlights the growing strain on businesses and underscores the precariousness of the current economic environment. Given these conditions, significant losses should be expected for creditors of these debtors. the tendency by lenders to employ strategies to buy time and restructure quietly and informally is a historical precedent that goes back to the Babylonian Financiers. As businesses and their lenders engage in these quiet negotiations and restructuring efforts, the real scale of financial distress may not be immediately apparent, potentially leading to more pronounced financial repercussions down the line. In its latest Financial Stability Report, the Bank of Canada acknowledges that it is closely watching the rise of business insolvencies but believes that it does not currently represent a significant source of concern for the credit performance of banks.

Credit performance at Canada’s “Big 6” banks remains strong, with capital and liquidity buffers continuing to exceed regulatory minimums. Business lending comprises about 40% of the total lending by Canadian banks, with loans to small and medium-sized businesses (“SMBs”) making up a relatively small share. According to the Bank of Canada, loans to SMBs, categorized as loans of $5 million or less, account for only 8.5% of total business loans. The liabilities of insolvent businesses (as declared at the time of filing) totaled about $11.4 billion in 2023. Even under the extreme assumption that all of these liabilities were owed in the form of bank loans, this would represent only about 0.7% of all outstanding bank loans to incorporated businesses.

Banks have increased loan-loss provisions on their business loans, serving as an early line of defence to absorb credit losses. Allowances for loan losses at the Big 6, expressed as a percentage of loan balances, were 20% higher in the first fiscal quarter of 2024 (three months ending January 31, 2024) than before the pandemic. Banks also continue to maintain ample buffers to meet unexpected liquidity needs and absorb unexpected losses. As of the first fiscal quarter of 2024, the average common equity Tier 1 capital ratio of large banks was 13.4%, about 2 percentage points higher than just before the pandemic and the highest regulatory capital ratio they have ever achieved, largely due to the phase-in of mandated debt stability buffers.

Canadian banks appear to be weathering a softening economy brought on by higher interest rates, as evidenced by better-than-expected fee-based earnings from capital markets and wealth management offsetting weak credit performance. Given the lagging nature of credit, provisions for credit losses (“PCLs”) are expected to move higher in 2024 relative to reported targets. PCLs for Stage 3 loans (i.e., non-performing, impaired loans) have risen faster than expected, which is not surprising given the rise in business insolvencies and CCAA filings. Overall loan write-offs have spiked and are above pandemic peaks. All these factors portend weaker credit growth over the near term and the Big 6 anticipate continuing to build reserves on Stage 1 and Stage 2 loans (i.e., performing loans) through the year.

Non-bank lenders, including private debt funds, benefit from reduced bank credit availability through greater lending opportunities and higher spreads. However, these lenders also account for the largest proportion of CCAA filings. In Q1-2024, 61% of CCAA filings with affected secured creditors involved a private debt fund, half of which were non-Canadian. The filings with the largest liabilities usually involved a U.S.-based private debt fund. Non-bank lenders are more inclined to resolve defaults out-of-court since recoveries are higher and reputational risks are lower; thus, the higher proportion of private debt funds involved in expensive and potentially prolonged CCAA proceedings likely means greater losses for these lenders.

As the impact of higher interest rates ripples through credit markets, a larger number of firms will experience significant credit stress, whether due to individual circumstances or broader credit conditions. This suggests a greater need for managers with business turnaround skills and restructuring experience and a more permanent role for special situations investments in private debt portfolios.

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Indeed, private credit opportunities are on the rise, since companies are struggling to obtain financing through traditional methods, says Arif Bhalwani, chief executive officer and managing director at Third Eye Capital.

“A lot of these banks are just inclined to seek liquidation as the most straightforward way to mitigate the risk and that’s where we see a significant gap and a major compelling opportunity. For pensions, there’s an opportunity . . . where they can earn a financial return but they can know that their money is being used to instigate some kind of change to businesses that really account for the [gross domestic product] of Canada.”

It’s an established norm now for pension plan sponsors to take private credit seriously as an asset class due to the benefits it brings to a portfolio, he adds, citing its diversification benefits and its positive performance when other asset classes like bonds are struggling.

Institutional investors are increasingly turning to strategies that can be trusted to mitigate risk amid a difficult and uncertain landscape primarily with the role of rising interest rates, says Bhalwani, noting rates are adding a downward pressure on the valuations of all pension fund investments.

“We’re seeing pensions become a lot more discerning in the emphasis around tangible distributions and cash-based outcomes.”

While private debt has largely been dismissed, that thought process is now changing, he says. He points to the U.S. investment market as an example where private debt has a more significant and widespread use. “There’s this cultural perception in Canada that leads to bank financing being the standard route.”

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Third Eye Capital (“TEC” or the “Company”), Canada’s leading provider of asset-based financing solutions to companies that are underserved or overlooked by traditional sources of capital, is pleased to announce the successful exit of its debt and equity holdings in Cricket Energy Holdings Inc. (“Cricket”). This follows the strategic majority sale of Cricket’s subsidiary, Provident Energy Management Inc. (“Provident”), to funds managed by Northleaf Capital Partners. Financial details and terms of the transaction were not disclosed.

In December 2020, TEC provided approximately $150 Million to back the management buyout of Cricket. Following the investment, Cricket re-aligned its business to focus on becoming the leading provider of sub-metering and building automation services to multi-residential buildings. TEC’s investment was structured as a form of asset-based financing, including senior secured term, revolver, and bridge loan facilities, that enabled Cricket to roll interest charges into the principal, delaying repayment until the sale of assets in the future. This allowed Cricket to channel its cash flows back into bolstering growth in its core businesses.

“Third Eye has been an exceptional partner and supported us through our transition as we strengthened our position in the new construction, multi-residential sub-metering market,” said Marco Pisterzi, COO/CFO at Cricket and Provident. “We are grateful for their investment and strategic guidance as we executed our plans.”

“Provident is an impressive energy management platform that has grown significantly in recent years,” said Arif Bhalwani, CEO at Third Eye Capital. “It has been a pleasure to work with the owners and senior leadership team and we wish them continued success in the years ahead.”

The successful exit not only surpassed TEC’s return and timing expectations but also led to the repayment of certain loans made by the Company to support the working capital of OZZ Electric Inc., Canada’s largest multi-residential electrical contractor, and a long-term strategic partner of Cricket and Provident.

“We are thrilled that we’ve been able to successfully execute on our plans and provide a great outcome to Provident and its customers, employees, and stakeholders,” added Steven Muzzo, founder and CEO of both Provident and OZZ. “Third Eye really understands our business and appreciates the synergistic power of OZZ’s deep industry relationships and 1,200 dedicated employees across Canada and the U.S. We look forward to working with them again in the future as we spin-out new sustainable businesses in the electrical energy space.”

About Third Eye Capital

Third Eye Capital is one of Canada’s largest and most experienced private debt firms, specializing in providing capital solutions to companies that are underserved or overlooked by traditional sources of financing, primarily banks. The firm’s strategic approach to asset-based financing empowers businesses to thrive and accelerate growth in their core operations. From navigating through periods of extreme distress to embracing moments of extraordinary change, Third Eye Capital excels at executing opportunities that other lenders simply cannot or will not pursue.

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Third Eye Capital Corporation (“Third Eye Capital”), Canada’s leading provider of asset-based financing solutions to companies that are underserved or overlooked by traditional sources of capital, confirmed today that it has substantially exited its investment in Pieridae Energy Limited. (TSX: PEA). Through Erikson National Energy (“Erikson”), a special purpose entity, Third Eye Capital made a net loan of $200 Million to a subsidiary of Pieridae Energy in October 2019, which was repaid primarily through financing from Prudential Private Capital and Voya Investment Management. Through to today, Erikson has received over $370 Million in cash payments comprising principal, interest, and fees. Erickson will retain a $20 Million short-term, senior secured loan at Pieridae Energy,, and currently owns an approximately 15% stake in the common stock of Pieridae Energy.

Mark Horrox, Principal at Third Eye Capital commented: “We backed Pieridae Energy to complete a transformational acquisition at a time when capital allocation to Canadian oil and natural gas businesses was scarce. Our analysis proved correct and Pieridae has successfully positioned itself as an environmentally conscious market leader in Alberta’s Foothills region, delivering substantial benefits to our investors. We are excited to continue our support for Pieridae’s growth journey.”

Arif Bhalwani, CEO at Third Eye Capital highlighted his firm’s investment approach: “This is another example of how Third Eye Capital can unlock value for asset-rich companies, even when their assets are out of favour or too complex for mainstream lenders. At a time when uncertainty reigns, we can provide critical financing and strategic guidance to help companies solve problems.”

About Third Eye Capital

Founded in 2005, Third Eye Capital is Canada’s leading provider of asset-based financing solutions to companies that are underserved or overlooked by traditional sources of capital. With a proven track record of nearly $5 billion in lending commitments across more than 125 portfolio investments, Third Eye Capital is one of the most experienced private debt firms in Canada. From navigating through periods of extreme distress to embracing moments of extraordinary change, Third Eye Capital excels at executing opportunities that other lenders simply cannot or will not pursue. Third Eye Capital actively engages with a company’s key assets to bridge the gap between their perceived value and actual worth, and provides access to capital and expertise that enables portfolio companies to flourish.

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