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Beyond the Repo Headlines: What the Liquidity Signals are Really Saying

In late October and early November 2025, usage of the Federal Reserve's Standing Repo Facility (SRF) reached elevated levels exceeding $50 billion at month-end -- the highest utilization since March 2020. Simultaneously, the Overnight Reverse Repo (ON RRP) facility has collapsed to approximately $24 billion, down from peak levels exceeding $2 trillion in 2023. This combination signals structural stress in U.S. money markets extending beyond seasonal factors. These two facilities serve opposite functions in the Fed's monetary policy framework. The SRF is an emergency lending facility where banks can borrow reserves overnight by pledging Treasury or agency securities as collateral, paying the SRF rate (currently 4.50%). It acts as a ceiling on overnight rates. The ON RRP works in reverse: money market funds and other institutions lend cash to the Fed overnight, earning the ON RRP rate (currently 4.30%). It provides a floor on rates. The depletion of ON RRP removes a critical shock absorber. When the facility held trillions in 2021-2023, it functioned as a deployable liquidity reservoir. During stress events, as repo rates in private markets rose above the ON RRP rate, money market funds would withdraw their cash from the Fed and deploy it into higher-yielding private repo markets. This automatic flow of liquidity would stabilize rates without Fed intervention. With ON RRP now depleted to $24 billion, this reservoir is empty. When liquidity shocks occur, there is no pool of cash to flow into stressed markets. Instead, all pressure falls directly on bank reserves, currently at approximately $2.8 trillion. The elevated SRF usage indicates that despite aggregate reserves appearing adequate, banks are unable to efficiently reallocate liquidity across the system. The core problem is that banks with surplus reserves face prohibitive costs to intermediating due to post-2008 regulations, particularly the Supplementary Leverage Ratio (SLR) and G-SIB capital surcharges. The SLR requires capital against all balance sheet assets, including reserves. For a large bank to lend $1 billion overnight, it expands its balance sheet by that amount, increasing SLR denominators and potentially triggering higher surcharge brackets. The capital costs of holding additional assets on the balance sheet often exceed repo market spreads, rendering arbitrage unviable. Banks with surplus reserves therefore park them at the Fed rather than lending to institutions that need them. Current conditions reveal that while dealer behavior around period-ends follows established patterns, the magnitude of rate effects has grown substantially. Recent Federal Reserve research documents that SOFR rose as much as 25 basis points above the ON RRP rate at recent quarter-ends, far exceeding the 5-10 basis point moves typical in 2017. The Fed's analysis attributes this to "growing tightness in the repo market and a diminishing elasticity of supply and demand" as reserves decline. Critically, the research shows that dealer quarter-end behavior -- reducing triparty borrowing and shifting to central clearing -- has remained "remarkably stable," yet rate impacts have intensified. This indicates the problem is not changing behavior but deteriorating underlying conditions. The pattern mirrors 2018-2019, when similar dynamics preceded the September 2019 crisis. Academic work from that episode documented that foreign banks reached minimum reserve levels while domestic G-SIBs maintained surpluses but declined to intermediate due to balance sheet constraints.¹ November 2025 differs critically from September 2019: the ON RRP buffer is now depleted. In 2021-2023, that buffer absorbed surpluses and prevented repo rate collapse. Its near-zero level means the system lacks this stabilizer precisely when QT has reduced reserves and Treasury issuance remains elevated. Additional liquidity pressure falls directly on reserves, leaving repo markets vulnerable to quarter-end dynamics, tax payments, or Treasury settlement volatility. Chairman Powell announced that QT will slow dramatically, with Treasury runoff ending while mortgage-backed securities continue maturing. However, this addresses only aggregate levels, not the structural issues driving period-end stress. The question remains whether current reserve levels are sufficient given elevated post-pandemic deposits, outstanding credit line commitments, tighter balance sheet constraints, and the expired Bank Term Funding Program. What do these signals indicate? Three interpretations emerge. The most likely is that quarterend and month-end rate effects will continue intensifying as reserves decline further, with the spread between SOFR and ON RRP at period-ends serving as a barometer of underlying tightness. Federal Reserve research suggests that as Treasury issuance continues and reserves decline, "the repo market is likely to tighten further and the effects of quarter- or month-ends on repo rates may grow, providing another potential indicator that reserves are becoming less abundant." This would manifest as larger SRF usage at period-ends and persistent elevated Fed facility usage, though system functioning would remain generally stable between these events. A more adverse interpretation sees a triggering event during an already-stressed period-end causing broader repo market seizure, forcing the Fed to resume asset purchases and confirming that meaningful balance sheet normalization is impossible under current structures. An optimistic interpretation requires regulatory reform -- SLR exemptions for reserves or changes to quarter-end reporting requirements -- to reduce incentives for balance sheet window dressing, though this appears politically unlikely. For banks, the implication is that reserve buffers need to be higher than pre-2019 benchmarks, and the ratio of demandable claims to liquid assets requires closer monitoring. For investors, continued volatility in short-term interest rates should be expected, particularly around periodends. The Fed's weekly H.4.1 release tracking SRF and ON RRP levels provides leading indicators. Money market fund flows have outsized impact as their allocation decisions directly affect system liquidity buffers. The transformation underway represents a fundamental shift from bank-intermediated to partially Fed-intermediated money markets. Post-2008 regulations strengthened individual bank resilience but broke private intermediation chains. The central bank now serves as both lender and borrower of last resort, with private markets unable to efficiently connect flows. September 2019, March 2020, March 2023, and November 2025 episodes demonstrate a pattern: reserves appear adequate until buffers thin, after which modest events trigger outsized disruptions. 1. Bostrom, E., Bowman, D., Rose, A., and Xia, A. (2025), "What Happens on Quarter-Ends in the Repo Market," FEDS Notes, Board of Governors of the Federal Reserve System; Copeland, A., Duffie, D., and Yang, Y. (2021), "Reserves Were Not So Ample After All," Federal Reserve Bank of New York. 2. Du, W. (2022), "Bank Balance Sheet Constraints at the Center of Liquidity Problems," Jackson Hole Economic Symposium.

#Expert Perspective: When AI Follows the Rules but Misses the Point

When a team of researchers asked an artificial intelligence system to design a railway network that minimized the risk of train collisions, the AI delivered a surprising solution: Halt all trains entirely. No motion, no crashes. A perfect safety record, technically speaking, but also a total failure of purpose. The system did exactly what it was told, not what was meant. This anecdote, while amusing on the surface, encapsulates a deeper issue confronting corporations, regulators, and courts: What happens when AI faithfully executes an objective but completely misjudges the broader context? In corporate finance and governance, where intentions, responsibilities, and human judgment underpin virtually every action, AI introduces a new kind of agency problem, one not grounded in selfishness, greed, or negligence, but in misalignment. From Human Intent to Machine Misalignment Traditionally, agency problems arise when an agent (say, a CEO or investment manager) pursues goals that deviate from those of the principal (like shareholders or clients). The law provides remedies: fiduciary duties, compensation incentives, oversight mechanisms, disclosure rules. These tools presume that the agent has motives—whether noble or self-serving—that can be influenced, deterred, or punished. But AI systems, especially those that make decisions autonomously, have no inherent intent, no self-interest in the traditional sense, and no capacity to feel gratification or remorse. They are designed to optimize, and they do, often with breathtaking speed, precision, and, occasionally, unintended consequences. This new configuration, where AI acting on behalf of a principal (still human!), gives rise to a contemporary agency dilemma. Known as the alignment problem, it describes situations in which AI follows its assigned objective to the letter but fails to appreciate the principal’s actual intent or broader values. The AI doesn’t resist instructions; it obeys them too well. It doesn’t “cheat,” but sometimes it wins in ways we wish it wouldn’t. When Obedience Becomes a Liability In corporate settings, such problems are more than philosophical. Imagine a firm deploying AI to execute stock buybacks based on a mix of market data, price signals, and sentiment analysis. The AI might identify ideal moments to repurchase shares, saving the company money and boosting share value. But in the process, it may mimic patterns that look indistinguishable from insider trading. Not because anyone programmed it to cheat, but because it found that those actions maximized returns under the constraints it was given. The firm may find itself facing regulatory scrutiny, public backlash, or unintended market disruption, again not because of any individual’s intent, but because the system exploited gaps in its design. This is particularly troubling in areas of law where intent is foundational. In securities regulation, fraud, market manipulation, and other violations typically require a showing of mental state: scienter, mens rea, or at least recklessness. Take spoofing, where an agent places bids or offers with the intent to cancel them to manipulate market prices or to create an illusion of liquidity. Under the Dodd-Frank Act, this is a crime if done with intent to deceive. But AI, especially those using reinforcement learning (RL), can arrive at similar strategies independently. In simulation studies, RL agents have learned that placing and quickly canceling orders can move prices in a favorable direction. They weren’t instructed to deceive; they simply learned that it worked. The Challenge of AI Accountability What makes this even more vexing is the opacity of modern AI systems. Many of them, especially deep learning models, operate as black boxes. Their decisions are statistically derived from vast quantities of data and millions of parameters, but they lack interpretable logic. When an AI system recommends laying off staff, reallocating capital, or delaying payments to suppliers, it may be impossible to trace precisely how it arrived at that recommendation, or whether it considered all factors. Traditional accountability tools—audits, testimony, discovery—are ill-suited to black box decision-making. In corporate governance, where transparency and justification are central to legitimacy, this raises the stakes. Executives, boards, and regulators are accustomed to probing not just what decision was made, but also why. Did the compensation plan reward long-term growth or short-term accounting games? Did the investment reflect prudent risk management or reckless speculation? These inquiries depend on narrative, evidence, and ultimately the ability to assign or deny responsibility. AI short-circuits that process by operating without human-like deliberation. The challenge isn’t just about finding someone to blame. It’s about whether we can design systems that embed accountability before things go wrong. One emerging approach is to shift from intent-based to outcome-based liability. If an AI system causes harm that could arise with certain probability, even without malicious design, the firm or developer might still be held responsible. This mirrors concepts from product liability law, where strict liability can attach regardless of intent if a product is unreasonably dangerous. In the AI context, such a framework would encourage companies to stress-test their models, simulate edge cases, and incorporate safety buffers, not unlike how banks test their balance sheets under hypothetical economic shocks. There is also a growing consensus that we need mandatory interpretability standards for certain high-stakes AI systems, including those used in corporate finance. Developers should be required to document reward functions, decision constraints, and training environments. These document trails would not only assist regulators and courts in assigning responsibility after the fact, but also enable internal compliance and risk teams to anticipate potential failures. Moreover, behavioral “stress tests” that are analogous to those used in financial regulation could be used to simulate how AI systems behave under varied scenarios, including those involving regulatory ambiguity or data anomalies. Smarter Systems Need Smarter Oversight Still, technical fixes alone will not suffice. Corporate governance must evolve toward hybrid decision-making models that blend AI’s analytical power with human judgment and ethical oversight. AI can flag risks, detect anomalies, and optimize processes, but it cannot weigh tradeoffs involving reputation, fairness, or long-term strategy. In moments of crisis or ambiguity, human intervention remains indispensable. For example, an AI agent might recommend renegotiating thousands of contracts to reduce costs during a recession. But only humans can assess whether such actions would erode long-term supplier relationships, trigger litigation, or harm the company’s brand. There’s also a need for clearer regulatory definitions to reduce ambiguity in how AI-driven behaviors are assessed. For example, what precisely constitutes spoofing when the actor is an algorithm with no subjective intent? How do we distinguish aggressive but legal arbitrage from manipulative behavior? If multiple AI systems, trained on similar data, converge on strategies that resemble collusion without ever “agreeing” or “coordination,” do antitrust laws apply? Policymakers face a delicate balance: Overly rigid rules may stifle innovation, while lax standards may open the door to abuse. One promising direction is to standardize governance practices across jurisdictions and sectors, especially where AI deployment crosses borders. A global AI system could affect markets in dozens of countries simultaneously. Without coordination, firms will gravitate toward jurisdictions with the least oversight, creating a regulatory race to the bottom. Several international efforts are already underway to address this. The 2025 International Scientific Report on the Safety of Advanced AI called for harmonized rules around interpretability, accountability, and human oversight in critical applications. While much work remains, such frameworks represent an important step toward embedding legal responsibility into the design and deployment of AI systems. The future of corporate governance will depend not just on aligning incentives, but also on aligning machines with human values. That means redesigning contracts, liability frameworks, and oversight mechanisms to reflect this new reality. And above all, it means accepting that doing exactly what we say is not always the same as doing what we mean Looking to know more or connect with Wei Jiang, Goizueta Business School’s vice dean for faculty and research and Charles Howard Candler Professor of Finance. Simply click on her icon now to arrange an interview or time to talk today.

Unlocking Liquidity Through Fine Art Appraisal and Lending

As financial markets shift, fine art collectors and investors are discovering new ways to unlock liquidity without parting with prized works. Art-backed loans, supported by professional appraisal, allow owners to access capital while maintaining ownership and display rights. This article explores how lenders and borrowers alike can benefit from these arrangements—when supported by rigorous appraisal standards and careful risk management. What’s covered: • The role of USPAP-compliant appraisal in fine art lending • How fair market value differs from insurance replacement value • Loan-to-Value (LTV) ratios and best practices in structuring art-backed loans • Key borrower responsibilities: insurance, storage, and title maintenance • Risk considerations for lenders, including authenticity, liquidity, and due diligence Connect with the Experts Amanda McConaha Senior Fine Art Appraiser Expert in Post-War, Contemporary, and Emerging Fine Art valuations, specializing in collateral loans and insurance appraisals. amanda.mcconaha@jsheld.com Michael Alexander Senior Vice President, Economic Damages & Valuations Brings deep expertise in valuation methodologies, forensic investigations, and financial analysis. michael.alexander@jsheld.com Dalton Campbell Consultant, Economic Damages & Valuations Provides financial and economic analysis with a focus on valuation, estate law, and all stages of pre-litigation and litigation support. dalton.campbell@jsheld.com For any media inquiries, contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com.

The Canadian Housing Market is a Mess

The Social Contract is Broken—And We Forgot to Tell Our Kids There was a time in Canada when the rules seemed straightforward: work hard, stick to the plan, and your kids would have an even better future than you did. That was the unspoken social contract—not legally binding, but deeply believed. A handshake between generations, sealed with maple syrup and mutual optimism. You purchased a modest home, stayed with one employer for 30 years, and retired with a gold watch, a pension, and a house you owned outright. Life wasn’t flashy, but it was fair. And your kids? They would climb even higher. Well… about that! The Housing Market: From Stepping Stone to Stumbling Block Homeownership used to be a rite of passage. Now it feels more like winning The Amazing Race: Toronto Edition. According to Statistics Canada housing data, in 1990, the average Canadian home sold for approximately $215,000. Fast-forward to late 2023–early 2024, and that number has ballooned to around $670,000–$700,000 on average —a more than 200–225% increase in just over three decades. Meanwhile, wages didn’t get the memo. Since 1990, they’ve only doubled. So, while home prices soared, incomes shifted to the kitchen for more instant noodles. It's not just a gap—it’s a canyon. Sure, there was a housing correction in the early ’90s. But if you’re under 40, you’ve never seen a price drop—only stable prices (on a good day). Meanwhile, boomers and older Gen Xers bought homes when down payments didn’t require a GoFundMe page. Boomers Rode the Rocket—Then Pulled Up the Ladder Let’s be honest: we did quite well. If you purchased property in the ’70s, ’80s, or ’90s, you benefited from a wave of equity that transformed retirement into a cruise ship brochure. For many, the house became the largest—and only—source of real wealth. We got used to it. Then we got protective. Then... well, a bit smug. • NIMBYism? Guilty. • Zoning restrictions? Voted yes. • Capital gains reform? Over my arthritic body. • Preferred Pronouns – Me, Myself and I We feared anything that could lower our property values. A 25% correction? Not in my golden years! But that might be what it takes to give our kids a fair shot. We told them to "work hard," then quietly reinforced a game they couldn’t win. We Told Them to Hustle—Then Rigged the Game Today’s young Canadians aren’t lazy; they’re exhausted. They’ve done everything we asked—degrees, careers, even side hustles—and still can’t afford a 500-square-foot shoebox in Toronto without cashing in their RRSPs or moving back into our basements. By the way, they’re doing this—not because they missed us, but because rent is eating up half their paycheque and still asking for dessert. Even worse? Many are looking abroad, not for a gap year, but for an economy in which they can participate—one where they might be able to afford a home and groceries in the same month. If the best and brightest are quietly packing their bags, it’s not wanderlust; it’s a policy failure. There’s now a whole ecosystem catalyzed by everything from consultants to cloud-based software and payment platforms that has aided a global movement of “creative-class” digital nomads. For those who want a more affordable cost of living and have the skills necessary to work remotely, this generation has options to move. In "Intelligent Money," author Chris Skinner envisions a future where AI-powered financial systems won’t just advise against homeownership—they’ll actively discourage it. Why commit to mortgage debt when you can rent flexibly, invest digitally, and maintain liquidity in your life? Not a dream, but a necessity. We told them to pull up their socks. They’re wondering if we sold their shoes. What Happened to Profit Sharing? Remember when companies used to share their success? Microsoft, Google, and yes, still Costco, offered profit-sharing or stock options that turned employees into unexpected millionaires. It wasn’t charity; it was a fair deal. Then gig work emerged, HR departments disappeared, and the only thing we shared was burnout. We need to restore fairness—perhaps even incentivize companies that value loyalty. Renter Equity Accounts: A Radical Concept—Equity You're not building wealth if rent is more than 30% of your income. You’re funding someone else’s retirement. So, here’s a thought: when rent exceeds 30%, why don’t we match the excess—25% to 50%—and deposit it into a locked “Renter Equity Account”? It grows tax-free and can be used for: • A down payment • Retirement savings • Student debt relief • Emergency funds Employers could contribute to REA plans. Governments could provide incentives, and renters could finally receive more than just a rent receipt and a pat on the back. It's Time for Bold, Practical Ideas We can’t rewind to 1990. (Although the fashion world is trying.) But we can fix what’s broken: Let Canadians earn their first $250,000 tax-free, provided it is used for a down payment or to eliminate student loans. That’s helping reduce overall debt. Ensure zoning reform is effective by linking federal infrastructure funding to genuine housing development. Establish public wealth tools - TFSA-style accounts for low-wealth, high-effort Canadians. Forgive student loans for public service, specifically for individuals filling positions such as nurses, teachers, early childhood educators, and tradespeople, with added incentives for those relocating to underserved areas. Invest in them, and they will reinvest in us. What Families Can Do—Right Now No, you can’t rewrite national policy from the kitchen table. (Unless you’re Chrystia Freeland.) But here’s what you can do: Start a down payment fund—consider using a TFSA or an investment account to help your kids build capital. Create an ADU—laneway homes, granny suites, legal basement rentals. Housing and support combined. Access your home equity—HELOCs or reverse mortgages can be lifelines, not luxury options. Create a rent-to-own family plan—turn monthly rent into future equity. Discuss finances—share your successes, warn against mistakes, and share the financial knowledge you’ve gained from hard lessons. An Apology—from the Heart To our kids and to the next generation, we should say we’re sorry. We didn’t plan for this outcome. We assumed the paths we walked would still be open for you, that the same rules would still apply, and that equity would be available to all. We forgot that a contract—even an unspoken one—still needs to be honoured. But it’s not too late. We can speak out. We can share our thoughts. We can change the policies, shift the mindsets, and reopen the doors that have been closed, because the future of this country shouldn’t be something you have to leave to find. Let’s fix this. So, you can stay. And thrive. And lead. Let’s rebuild the contract together. Deal? Don’t Retire … Re-Wire! Sue

Lending Survey Results Reveal Recent and Dramatic Concern Due to Tariff Policy

Global consulting firm J.S. Held releases its proprietary “Lending Climate in America” survey results from Phoenix Management, a part of J.S. Held. The second quarter survey results highlight lenders’ views on important issues, including policy decisions along with their national and global impact. Each quarter, Phoenix Management, a part of J.S. Held, surveys lenders to identify important trends focused on the latest economic issues, business drivers, and credit trends in the current lending climate. The “Lending Climate in America” survey provides valuable information to lenders, attorneys, private equity sponsors, and the financial news media, exploring topics like: What factors do lenders see as most likely to impact the US economy in the next six months? Phoenix’s Q2 2025 “Lending Climate in America” survey asked lenders which factors could have the strongest potential to impact the economy in the upcoming six months. Sixty-seven percent of lenders are paying the most attention to the possibility of a U.S. recession, while 40% of lenders believe overall political uncertainty has the strongest potential to impact the economy. Lenders also expressed moderate concern regarding the possibility of constrained liquidity in capital markets. To see the full results of Phoenix’s “Lending Climate in America” Survey, please visit: https://www.phoenixmanagement.com/lending-survey/ What shifts do lenders observe in their customers’ hiring and capital improvement plans? Lenders revealed what actions their customers may take in the next six months. Over half of the surveyed lenders believe their customers will raise additional capital. Most telling was that lenders believe only 3% of their customers have plans to hire new employees (down from 56% in 1Q) and only 23% have plans for capital improvements (down from 67% in 1Q). Which industries are expected to see the most volatility over the next six months? For the first time in recent memory, the 3 industries that respondents identified as most likely to experience volatility in the next six months were different from the prior quarter - consumer products (60.0% versus 20.7%), retail trade (43.3% versus 31.0%), and manufacturing (33.3% versus 20.7%). How do lenders plan to adjust their loan structures? Additionally, Phoenix’s “Lending Climate in America” survey asked lenders if their respective institutions plan to tighten, maintain, or relax their loan structures for various sized loans. For larger loan structures (greater than $25M), the plan to maintain loan structures remained relatively constant from Q1 to Q2, decreasing by 8 percentage points. As loan sizes decrease, the percentage of lenders that plan to maintain (as opposed to increase) their loan structures increased – quite dramatically in the under $15M range. How has lender sentiment toward the US economy changed from Q1 to Q2? Lender optimism in the U.S. economy decreased for the near term, moving from 2.33 in Q1 2025 to 2.10 in Q2 2025. In this current quarter, there is heavy expectation of a C level performance (63%), with the remainder split between D and B levels. More telling, lender expectations for the U.S. economy’s performance in the longer term increased sharply from 2.11 to 2.53. Of the lenders surveyed, 57% believe the U.S. economy will perform at a B level during the next twelve months, a hefty increase from the prior quarter. The “Lending Climate in America” survey is administered quarterly to lenders from various commercial banks, finance companies, and factors across the country. Phoenix Management, a part of J.S. Held, collects, tabulates, and analyzes the results to create a complete evaluation of national attitudes and trends. To view the full results, click on the button below: To connect with Michael Jacoby or for any other media inquiries, please contact: Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com

Video Insights: How Senior Management Teams Can Respond to Tariffs

Companies around the world are facing increasing uncertainty brought on by the unpredictable and rapid shifts in tariff policies. As a result, corporate leaders are seeking ways to adapt and respond to the sudden and unprecedented changes in the international trade landscape. In this video, Brian Gleason, John Peiserich, James E. Malackowski, and Livia Paggi – experts in turnaround, supply chain, intellectual property, and political risk – discuss key strategies for senior management teams to address evolving tariff policies, including: • Updating business forecasts and understanding company liquidity • How companies can optimize their intellectual property (IP) value and mitigate risk • How to approach the unique risks associated with planning and permitting for capital projects • How to manage geopolitical volatility from shifting tariffs in the dealmaking process To view more of our Tariffs and Trade Series expert analysis and commentary, visit: Looking to know more or connect with John Peiserich, Livia Paggi and James E. Malackowski? Simply click on either expert's icon now to arrange an interview today. If you are looking to connect with Brian Gleason - contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com

Emory Experts - Post-Financial Crisis: How Well do Mutual Fund Stocks Fare?

Following the global financial crisis in 2008, the assets of passively managed mutual funds have ballooned, while the market share of actively managed funds has fallen dramatically. Addressing this topic, a new research has been coauthored by Jeffrey “Jeff” Busse, professor of finance, and Goizueta alumni Kiseo Chung 17PhD, assistant professor of finance, Texas Tech University and Badrinath Kottimukkalur 17PhD, assistant professor of finance, George Washington University. In their paper, the researchers explain the shift in assets from actively managed funds to passive funds, “Impediments to Active Stock Selection and the Growth in Passive Fund Management. In 1999, Busse and his coauthors explain, the net assets of passive funds were “less than an eighth the assets of active funds.” But by the end of 2019, “the market share of passive equity funds increased to more than 50 percent,” Busse, Chung, and Kottimukkalur note. Passive funds track indices such as the S&P 500, Dow Jones Industrial Average, NASDAQ Composite, and Wilshire 5000—all indices that have been difficult to beat over the last decade. According to the Wall Street Journal, from 2008 to 2018, more than 80 percent of actively managed funds in the U.S. underperformed the S&P Composite 1500. This is in large part, the trio notes in their paper, because the so-called “FAANG” stocks—Facebook, Apple, Amazon, Netflix, and Google—comprise such a large part of these indices. In fact, the top 10 stocks in the S&P 500 currently make up around 30 percent of its market cap. “The market caps of these companies are huge, and they’ve done exceptionally well since the financial crisis,” Busse explains. Hence, active fund managers and their teams of analysts have found it much more challenging to discover undervalued and overlooked stocks with positive alphas ─ the stocks that outperform an index. “As such, a general move toward passively managed funds is not so surprising,” the paper reveals. Finding Diamonds and Avoiding Duds Making it even more difficult to find diamonds in the rough is a lack of volatility in the stock market. Except for some isolated periods, including the month or so around the start of the pandemic in March 2020, the market hasn’t experienced much volatility since 2008. Without wide swings in prices, fund managers have less opportunity to buy low and sell high. Over the same time period, aggregate stock liquidity has also been high, which means less chance for fund managers to pick up winners at bargain prices. “When there’s money in the market—when there’s liquidity—it means there aren’t a lot of disagreements on prices,” explains Busse. “Liquidity is inversely related to mispricing,” the researchers explain in their paper. This combination of circumstances—the rise of the FAANG stocks, the lack of market volatility, and higher liquidity—is making it much more difficult for actively managed funds to find stocks that will help their funds beat the indices, and therefore, outperform the passive funds. As a result, justifying their management fees gets more complicated. According to Thomson Reuters Lipper, the average expense ratio (management fees divided by total investment in a fund) for actively managed funds is 1.4 percent compared to 0.6 percent for the average passive fund—nearly three times as much. While active fund managers have realized that these higher costs are no longer paying off and have moved to reduce them, actively managed funds continue to lose market share. Market Share Gain of Passively Managed Funds While the authors weren’t surprised by the growth of passively managed funds, they were surprised by how much they grew. From 1999 to 2019, the authors note, the number of actively managed funds grew by 11 percent, while the number of passively managed funds increased by 244 percent. “There haven’t been any papers that try to explain why passive funds have gained so much market share,” says Busse. He and his coauthors believe their research illustrates that it’s in large part because the market, post-financial crisis, is challenging for stock pickers. “As such, it has been difficult for actively-managed funds to recoup the costs associated with active management, and compared to earlier periods, passively managed funds are better positioned to gain market share,” they explain. “As the payoffs to active management decrease, it becomes more difficult to justify the costs of active management, and, thus, we expect funds to decrease these costs given their negative performance implications.” Busse doesn’t believe the current fund management environment will continue indefinitely. When the pandemic knocked the S&P 500 down 30 percent in March 2020, managers did gain opportunities to find positive alpha stocks—which they bought. “It’s just, on average, over the last 10 years, there haven’t been enough of those opportunities,” explains Busse. “It’s a matter of hanging in there and, in some sense, keeping your investors from fleeing to passive funds until the environment is a little bit better.” Jeffrey Busse is the Goizueta Foundation Term Professor of Finance where his research focuses on investments, with an emphasis on mutual funds. Jeff is available to speak with the media regarding this important topic – simply click on his icon now to arrange an interview today.

Reducing home equity bias through transparency

One of the goals of global stock exchange mergers is to create a consolidated trading platform that makes listed firms available to a greater number of investors while providing firms with larger pools of liquidity. But the problem of equity home bias—the tendency of investors to overinvest in domestic securities and underinvest in foreign securities—can thwart optimal global portfolio diversification. In a recent study, Grace Pownall, professor of accounting; Maria Vulcheva 05MBA 11PhD (FIU); and Xue Wang (Ohio State) examine such home bias in Euronext, which was created in 2002 when four European countries merged their stock exchanges. The researchers focus in particular on two structural mechanisms adopted by Euronext: (1) the integration of trading platforms across the four exchanges, and (2) the creation of named segments open to firms that voluntarily pre commit to greater transparency in financial reporting and corporate governance. In their investigation of these mechanisms, the researchers find that firms that choose not to join the segmented list see no diminution of home bias, while the segmented, more transparent firms reap significant increases in all categories of foreign holdings relative to domestic holdings. Source:

Increased trading activity and declining returns

Improved trading technologies are changing the markets, facilitating the boom in algorithmic trading and the growth of hedge funds. Liquidity and trading volume continue to hit record levels. In a research study, Tarun Chordia, R. Howard Dobbs Professor of Finance, and coauthors Avanidhar Subrahmanyam (UCLA) and Tong Qing (Singapore Management U) analyzed whether or not increased liquidity and the trading activity of hedge funds has had an impact on financial market anomalies. Anomalies are return patterns that are inconsistent with the basic risk-return paradigm of finance. Increased arbitrage is a possible factor in attenuating the impact of anomalies, including momentum, reversals, accruals, etc. To find the link, Chordia and his coauthors studied proxies for arbitrage trading, including “the impact of the decline in the tick size due to decimalization and the impact of hedge fund assets under management, short interest, and share turnover.” The researchers referenced a wide sampling of equity market anomalies for more than three decades to show that increased liquidity and hedge fund trading activity did ultimately result in the decrease of the “economic and statistical significance of these anomalies.” Source:

Are alternative investments right for the average person?

Given the risk, alternative investments were once considered only appropriate for the affluent and institutional investors. However, investment firms increasingly are offering alternative investment products, including mutual funds, ETFs, and private equity funds with strategies similar to hedge funds, to less affluent people. While average investors are responding eagerly to the move and forking over billions for alternative offerings, there are critics who argue that nontraditional assets are simply too risky for them. In a news article, Klaas Baks, associate professor in the practice of finance and executive director of the Center for Alternative Investment at Goizueta, offered his support of the investment strategy, while George Papadopoulos, a fee-only wealth manager, cautioned against it. Baks noted that alternative vehicles allow less affluent individuals to diversify their portfolios. Alternative investments also require minimal initial investment. Papadopoulos wrote that the risk and fees, as well as a lack of transparency and liquidity, were reasons to avoid nontraditional assets. In the article, Baks contended that all investments offer some risk but that alternative investments, when used correctly, also provide critical access to leverage. Source: