Hedge Fund Performance

◾ High volatility and low returns have weighed on risk-adjusted performance across US equity indices so far this year. The S&P 500’s 2% return year-to-date and volatility of 17 have yielded an annualized risk-adjusted return ratio of 0.1, well below the median annual reading since 1990 of 1.0. ◾ We define a stock’s prospective risk-adjusted return as the return to the stock’s consensus 12-month price target divided by its 6-month option-implied volatility. Currently, the median S&P 500 stock is expected to post an 11% return to its 12-month consensus price target with a 6-month implied volatility of 28, yielding a prospective risk-adjusted return of 0.4. ◾ Within the S&P 500, our High Sharpe Ratio basket (ticker: GSTHSHRP) contains companies with the highest prospective risk-adjusted returns relative to their sector peers. The basket’s median constituent has a prospective risk-adjusted return of 0.9. Our High Sharpe Ratio basket has posted a YTD return of 3%, outperforming both the cap-weighted S&P 500 (2%) and equal-weighted S&P 500 (1%). The basket contains 50 S&P 500 stocks and is sector-neutral and equal-weighted. ◾ We rebalance our High Sharpe Ratio basket in this report. Consensus price targets indicate that the median stock in the basket will generate more than two times the price return of the median S&P 500 stock (29% vs. 11%) with only slightly higher implied volatility (30 vs. 28). Stocks in the basket with the highest prospective risk-adjusted returns include LKQ, VTRS, and OMC.

Can you guess a fund's final performance based on interim quarterly reports?   Yes - according to a recent paper by Stanford and Chicago academics.   3 highlights - background, key findings, and applications for LPs:   ________________________   1️⃣ Background:   ‣ Interim fund NAVs are based on discretionary, "fair market value" valuations by GPs   ‣ Past studied have found that NAVs often deviate from fair value as they tend to: (i) be conservative on average (ii) get inflated around fundraising by low quality GPs (iii) held at cost when investments are underperforming   ‣ Can these patterns be exploited by LPs trying to predict final performance based on interim NAVs? ________________________   2️⃣ Key findings:   ‣ The paper builds three valuation metrics to predict future returns: (i) "interim multiple": fund TVPI at the time the forecast is made (ii) "past staleness": fraction of previous quarters with 0 changes in fair value (iii) "markdown frequency": fraction of previous quarters with negative changes in fair value   ‣ Overall, past staleness and markdown frequency predict future changes in multiples, but the results differ for buyouts vs venture capital   ‣ For buyouts: markdown frequency and past staleness (even in the first few years) predict negative future performance   ‣ For venture: higher interim multiples predict future negative returns, but past staleness and markdown frequency do not   ‣ "…the combination of interim multiple, past staleness, and past markdown frequency helps predict whether an investment will end up among the best or worst performing investments at exit. These predictions are informative as early as the first year of the investment."   ________________________   3️⃣ Applications for LPs:   ‣ Due diligence on primary commitments: analysing track record and projecting performance of currently active funds   ‣ Due diligence on secondary investments: modelling residual upside on funds   ‣ Portfolio monitoring: sense-checking valuations and projecting future performance ________________________   Source: "Interim Valuations, Predictability, and Outcomes in PE" - by Ege Ercan, Steven Kaplan, and Ilya Strebulaev - 2024   #privateEquity #ventureCapital #valuations

How do banks balance profitability with risk when lending? Imagine, You're a bank evaluating a loan application. The client looks promising rated as low risk with a solid repayment history. But what if small changes in assumptions, like the probability of default, could completely alter the picture? This is where understanding risk-adjusted return on capital (RAROC) becomes crucial. In a recent exercise, I explored how tweaking just one variable default probability can reshape decisions. For instance, Increasing the probability of default from 1% to 1.5% (a seemingly small change) resulted in a noticeable drop in the year-one RAROC. Why does this matter? It means the bank may see the client as less profitable and adjust terms or even reconsider the loan altogether. Here’s an example: -A $4M operating loan with a 75% utilization rate and a 60% loss given default. -At a 1% probability of default, the facility appears profitable. -But raise the probability to 1.5%, and profitability takes a hit impacting both the client and the bank's bottom line. Banks also face pressure to integrate factors like ESG risks into their models. If a client operates in industries with higher environmental or social risks, those risks could translate into a higher default probability. This adjustment not only impacts pricing but also highlights how much these factors influence lending decisions. Why should this matter to us as professionals? It’s a reminder that data-driven decisions don’t exist in isolation they directly impact real people and businesses. It’s not just about percentages and formulas; it’s about trust, opportunity, and financial well-being. From my perspective, This exercise was a valuable way to see how small assumptions can cascade into major outcomes. Whether we’re working in banking, sustainability, or beyond, it’s a call to examine the assumptions driving our choices. What do you think? How do you account for subtle risks in your decision-making process? Let’s discuss.

Many market participants still rely almost automatically on the Sharpe Ratio when assessing the relationship between return and risk. It is widely used — but not always the most appropriate metric. Especially for funds with strong positive monthly performance, the Sharpe Ratio can paint a distorted picture. Why? Because it captures all volatility — not only downside fluctuations, but also upside volatility. In other words: If a fund regularly delivers months of +5%, +10% or even +20%, returns increase significantly — but so does measured volatility. And in the Sharpe Ratio, even this positive volatility effectively gets “penalized.” For strategies and funds with pronounced upside volatility, the Sortino Ratio is often the more meaningful measure. It focuses only on harmful volatility, meaning returns that fall below a target or minimum acceptable return. That is the key distinction: ▪️ Sharpe Ratio = return per unit of total volatility ▪️ Sortino Ratio = return per unit of downside volatility For a fund such as Intalcon Alpha for Impact Global, which repeatedly shows very strong positive months, the Sortino Ratio is, in my view, the more appropriate metric when linking return and risk in a fair and economically sensible way. My point is simple: Well-performing funds should not be penalized for being volatile to the upside. If we want to understand the true quality of returns, we should not look at Sharpe Ratio alone. In particular, for asymmetric and opportunity-driven strategies, the Sortino Ratio deserves far more attention. More on this topic in our magazine: "Sharpe vs. Sortino – Choosing the Right Reward-Risk Metric for Your Strategies" - see link in comments. What do you think: which metric is more relevant when assessing risk-adjusted returns — Sharpe Ratio or Sortino Ratio? #stockmarket #mutualfunds #hedgefunds

For aspiring quants: Last year, I evaluated a strategy that "beat the S&P 500 by 20%." Impressive, right? Then I ran it through a simple CAPM risk model. Turns out, the strategy was just 2x leveraged beta with extra steps. It didn't beat the market - it just borrowed more money to buy the market. Worse: It was delivering only 60% of the return you'd expect for that level of risk. Here's the problem most backtests hide: They show you total return. They don't show you risk-adjusted return. A real example: UPRO (3x leveraged S&P 500 ETF) Edgar Alcántara and I analyzed leveraged ETFs using CAPM for our open-source course and found that:: ·      UPRO takes on 3.04x the volatility (β = 3.04) ·      But delivers only 2.04x the cumulative returns ·      That's 67% of what you'd expect for the risk taken The chart below shows the decomposition: The green line is what CAPM predicts UPRO should return. The blue line is what it actually returns. The gap? Inefficiencies from volatility drag, transaction costs, and behavioral factors. This isn't "alpha." It's inefficient beta. For those building careers in quant finance: Before you present any strategy, decompose it through a risk model: → How much return comes from market exposure? → How much from factor tilts? → How much from genuine alpha? The question isn't "did it outperform?" The question is "was it worth the risk?" Want to learn how to do this analysis? I've built a full tutorial on CAPM and risk-adjusted returns in my open-source Portfolio Management Course (developed with Edgar Alcántara): https://lnkd.in/ebsTPNQ7 Module 4.1-4.4 covers exactly this type of analysis with Python code. Read the full paper: "Leveraged Portfolios Risk Analysis" https://lnkd.in/eVFh-sme This analysis was conducted independently using publicly available data. All views are my own. What's your experience? Where have you seen backtests that looked great but didn't account for risk? #QuantFinance #RiskManagement #PortfolioManagement

📌 Beyond Returns: Using Risk Ratios & Information Ratio to Pick Smarter Mutual Funds When it comes to investing in mutual funds, most investors only focus on returns. But the real edge lies in understanding how consistently and efficiently those returns are delivered — and that’s where Risk Ratios and the Information Ratio (IR) come into play. 📊 🔍 What did I do? I shortlisted Equity Mutual Funds (as on 2nd June 2025) across categories based on these normal screening criteria: ✅ AUM > ₹1,000 Cr ✅ TER < 2% ✅ Fund age ≥ 3 years ✅ Positive Information Ratio – indicating consistent outperformance over the benchmark (Fund manager's Consistency) 💡 Key Risk Ratios Explained: 📈 Standard Deviation – Measures volatility. Higher = more fluctuations. 📉 Sharpe Ratio – How much return a fund gives per unit of risk. Higher = better risk-reward. ⚠️ Sortino Ratio – Like Sharpe, but considers only downside risk (bad volatility). 📊 Beta – Sensitivity to market movements. Beta > 1 = more volatile than the index. 🏁 Alpha – Measures excess return vs. the benchmark. Positive Alpha = skillful fund management. 📘 Information Ratio (IR) – Measures consistent alpha generation vs. benchmark volatility. Positive IR means the fund has delivered superior risk-adjusted returns. 🔗 R-Squared – Shows how closely the fund’s movements track the benchmark. Closer to 1 = more aligned. 💭 Key Insight: A fund with high past returns but poor risk metrics might not be sustainable. True investing success lies in risk-adjusted consistency, not just absolute numbers. If you're selecting funds based only on past returns, you might be missing the full picture. --- 🛡️ Disclaimer: This post is for educational purposes only and does not constitute investment advice or recommendation. Mutual fund investments are subject to market risks. Please read all scheme-related documents carefully and consult your financial advisor before investing. #MutualFundsIndia #FinancialLiteracy #InformationRatio #SmartInvesting #PersonalFinance #EquityFunds #WealthManagement

FDA Layoffs: What It Means for Medical Device Approvals and the Life Sciences Industry The recent wave of layoffs at the FDA—part of broader cuts within the Department of Health and Human Services—has sent shockwaves through the life sciences and medical device industry. With key staff reductions in medical device review teams, the implications for product approvals, regulatory timelines, and market access could be significant. What This Means for Medical Device Companies 🔴 Longer Approval Timelines – With fewer reviewers handling medical device applications, companies can expect delays in regulatory approvals. This could slow innovation and disrupt go-to-market strategies. ⚠️ Increased Regulatory Bottlenecks – A reduced workforce may lead to application backlogs, making it harder for companies to navigate FDA requirements efficiently. Startups and smaller firms without dedicated regulatory teams may face even greater challenges. 🛑 Potential Risks to Product Safety & Compliance – A smaller FDA team could mean less rigorous oversight, increasing the risk of compliance gaps. Companies must be proactive in ensuring their devices meet safety and efficacy standards to avoid delays or recalls. How Companies Can Prepare ✅ Plan for Longer Review Periods – Build extra time into regulatory and product launch timelines to account for possible delays. ✅ Strengthen Pre-Submission Efforts – Ensuring thorough and well-documented applications can help streamline the review process and reduce back-and-forth with regulators. ✅ Monitor Regulatory Changes – Stay updated on FDA policies, potential fast-track programs, and alternative certification pathways that may help mitigate delays. This shift in the regulatory landscape is a wake-up call for the industry. Companies that proactively adapt to these challenges will be in a stronger position to navigate the evolving approval process and bring life-saving innovations to market efficiently. What are your thoughts on the impact of these changes? Are you already seeing delays or disruptions? Let’s discuss in the comments.

The FAR rewrite is not a policy update. It is an operating model shift. The Revolutionary FAR Overhaul replaces "shall" with "may" across the entire regulation. That sounds administrative until you realize what it means: every contracting officer now has discretion where they used to have a checklist. For small contractors, this creates two simultaneous realities. Reality one: the companies that build relationships with contracting officers and understand how individual agencies interpret the new flexibility will win more work. The old playbook of checking compliance boxes and submitting the lowest price is losing its advantage. Reality two: the companies that wait for the formal rulemaking to finish before adapting will find that their competitors already positioned themselves during the deviation period. The agencies implementing class deviations right now are not waiting for the final rule. Neither should you. Three things to do this month: Read your target agency's class deviation notices. They tell you exactly how that agency is interpreting the new flexibility. Rethink your past performance strategy. The 2026 NDAA expands what counts as relevant experience, including commercial work. If you have been leaving commercial past performance off your proposals, stop. Get in front of your contracting officers before the evaluation criteria change. Oral presentations and phased down-selects are now explicitly authorized. If your capture strategy assumes a traditional written proposal evaluation, it may already be outdated. The companies that treat this as a compliance update will struggle. The companies that treat it as a positioning opportunity will lead. #GovCon #FederalContracting #AcquisitionReform

The landscape shifted last week when GSA announced its sweeping Refresh 30 initiative—and if you're on a Multiple Award Schedule contract, this isn't optional reading. Here's what changed: GSA is incorporating class deviations from the Revolutionary FAR Overhaul directly into contract vehicles like OASIS+, Polaris, and the Alliance series. We're discussing updated contract clauses: 5 new, 36 eliminated, and 53-56 replacements across the board. This marks the first major wave of reforms, which began with Executive Order 14275 in March 2024. What started as policy is now an operational reality. What contractors must do immediately: ✅ Visit the Refresh 30 website and establish an internal review process for all clause changes. You have a 90-day acceptance window—use it strategically. Map the operational impacts on your current contracts, then align your compliance programs with both FAR changes and GSA regulatory updates. ✅ Document everything. The broader context matters here. Treasury's cancellation of all 31 Booz Allen Hamilton contracts signals a zero-tolerance stance on compliance failures, even for resolved issues. GSA's "defend the spend" scrutiny of consulting firms continues to intensify. The RFI on value-added resellers—with responses due February 9—suggests GSA may implement markup caps unless VARs clearly demonstrate their value beyond repackaging. This isn't bureaucracy for its own sake. Agencies are being held accountable for demonstrating taxpayer value, and they're passing that accountability down through the supply chain. The contractors who document their compliance, justify their value proposition, and adapt quickly to regulatory changes will thrive. Those who treat this as administrative noise will find themselves increasingly marginalized. The Revolutionary FAR Overhaul promised streamlined acquisition. Refresh 30 delivers on that promise—but only for contractors who engage seriously with the changes. Review your contracts against the new clause structure. Understand what's been eliminated and why. Identify where your operations need adjustment. 💡 For federal acquisition professionals: This is exactly the type of regulatory modernization that requires both deep FAR knowledge and the ability to operationalize change across complex portfolios. As agencies implement these updates, acquisition teams need support in understanding the practical implications for their specific contract types and mission requirements. #FederalAcquisition #FAROverhaul #GSA #GovernmentContracting #ProcurementModernization

Most budget debates sound like this: Let’s put $100K into Channel X because last quarter ROI looked solid. Translation: You’re gambling on a single point estimate. I introduce confidence bands, an idea borrowed from finance, to make marketing spend a calculated risk, not roulette. How it works: 1️⃣ Model Return Distribution: ↳ Take the last 12 months of channel ROI. ↳ Build a simple 80 % confidence interval (CI). ↳ GA4 + BigQuery make this a two‑line SQL script. 2️⃣ Assign Risk Tiers: ↳ Channels with narrow CIs = predictable (low risk). ↳ Wide CIs = volatile (high risk). ↳ Create three tiers: Core. Growth. Experimental. 3️⃣ Allocate by Risk Appetite: ↳ Core gets stable funding. ↳ Growth receives incremental budget as long as ROI stays within band. ↳ Experimental gets capped spend, think venture bets with predefined exit rules. Result: Budgets adjust automatically to performance volatility, not politics. One e‑commerce client reallocated 15 % of ad spend from volatile display ads to a stable influencer program and saw a 26 % lift in blended ROAS, no additional dollars required. Executives love it because it turns marketing magic into disciplined portfolio management. Which risk tier currently eats most of your budget? A) Core (predictable) B) Growth (moderate risk) C) Experimental (high risk)