Hedge Fund Performance

KPI Vs BSC Vs KSF Performance management is essential for organizations aiming to achieve their strategic objectives and maintain competitive advantage. Three key concepts in this domain are Key Performance Indicators (KPIs), the Balanced Scorecard (BSC), and Key Success Factors (KSFs). Each serves a unique purpose in evaluating and guiding organizational performance, but they differ in scope, implementation, and focus. Understanding these differences is crucial for effectively utilizing these tools in strategic planning and performance management. Key Performance Indicators- KPIs are specific, quantifiable metrics used to evaluate the efficiency and effectiveness of various operations within an organization. They provide a focused view on particular areas such as sales revenue, customer retention rate, or employee productivity. KPIs are typically short to medium-term in nature and are often used in dashboards and performance reports to monitor progress against specific targets. Their primary advantage lies in their ability to provide clear, measurable insights that can drive immediate operational improvements. Balanced Scorecard-The BSC is a strategic planning and management system that offers a comprehensive view of organizational performance across four perspectives: Financial, Customer, Internal Processes, and Learning & Growth. It integrates both quantitative and qualitative measures, aligning business activities with the organization’s vision and strategy. The BSC encourages a balanced approach, ensuring that improvements in one area do not come at the expense of another. Its medium to long-term focus makes it a robust tool for strategic alignment and holistic performance management. Key Success Factors- KSFs are the critical areas that an organization must excel in to achieve its mission and objectives. These are typically broad, qualitative factors such as innovation capability, market position, or customer loyalty. KSFs help identify the most important areas of focus that are essential for long-term success. They are deeply integrated into strategic planning and operational processes, providing a foundation for setting priorities and guiding resource allocation. While KPIs, the BSC, and KSFs all play vital roles in performance management, they serve different purposes and offer unique benefits. KPIs provide focused, quantifiable insights into specific operational areas, making them ideal for short-term performance monitoring. The BSC offers a comprehensive framework that aligns organizational activities with strategic objectives, promoting balanced and long-term performance improvement. KSFs identify the critical areas necessary for achieving strategic success, guiding overall focus and resource allocation. Together, these tools can provide a robust framework for managing and enhancing organizational performance, ensuring both immediate operational efficiency and long-term strategic success.  #hr #performancemanagement #kpi #bsc #ksf

What's the closest historical parallel to today's market regime? To make sense of prevailing market conditions we are somehow wired to think in frameworks and historical parallels – and I believe it’s a useful exercise to do as long as you contextualize it to today. Today’s market regime resembles mid-2007: core sticky inflation was regularly printing at .2% MoM (.12% in the last 3 months today), the Fed was predictably on hold (getting there now) and the economy was cooling off below trend but not recessionary yet. Sounds familiar? The table below shows global macro returns Sharpe for macro asset classes during the March to September 2007 period and a couple of trends are very interesting. 1) Equities did well with tech leading cyclicals: soft landing data favored big cap tech over cyclicals; 2) Bonds just hovered around directionless, with only mildly positive total returns as the Fed kept rates at 5.25% despite inflation slowing down back to target; 3) But the most marked trend was a very weak USD: EUR, GBP and AUD did really well but the weak US Dollar particularly boosted EM FX and equity returns 4) Commodities had a strong run also helped by idiosyncratic production bottlenecks (e.g. Saudi oil production). Today's dominant market regime seems to be very similar to mid-2007, so contextualizing returns back then to today's setup might be a useful exercise. If you are an institutional investor, I cover macro and markets live for you through a Bloomberg chat service and much more. Send me a BBG ping if interested.

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

Sharpe, Treynor and Sortino Ratio! 3 Measures of Risk Adjusted Returns.. Let's decode in this post! SAVE this post for future reference SHARE this with your network to help them. First up, what is the need to look at Risk Adjusted Returns? When comparing portfolios of various Fund Managers, most people rely on Returns. But Returns cannot be looked at in isolation. Understanding the risk taken to generate those returns is also to be considered A fund manager could have taken excessive risk to generate returns. For example, high concentration in a single risky stock. If it does well, returns could be very high, but this could be purely due to luck. To standardize returns against risk, we use Risk Adjusted Returns Also, let us define Excess Return. Excess Return is given by Portfolio Returns minus Risk Free Rate. One can get Risk Free Returns without taking any risk. Thus, the fund manager is assumed to be taking risk only for the Excess Return. Let us now decode the metrics. ✅ Sharpe Ratio - This is given by Excess Return / Standard Deviation of Fund Returns - (Portfolio Return - Risk Free Rate)/ (Standard Deviation of Portfolio Returns) - Higher the better, as a higher number denotes higher excess return generated per unit of risk ✅ Treynor Ratio - This is given by Excess Return / Portfolio Beta - (Portfolio Return - Risk Free Rate)/ (Portfolio Beta) - Higher the better - Usually funds appearing higher on Sharpe Ratio will also rank higher on Treynor, unless they are not diversified properly ✅ Sortino Ratio - This is slightly complicated - Standard Deviation does not distinguish between upside and downside risk - In long only Investing, upside risk is not really a risk - So we come with a concept of Downside Standard Deviation - This is the standard deviation as calculated by using returns on down days - Now the Sortino Ratio is calculated as (Portfolio Return - Risk Free Rate)/ (Downside Standard Deviation) - Once again, higher the better - This tells you which portfolio is doing better on a risk adjusted basis when the returns are negative When investing in funds, it is important to consider these parameters! One of these numbers is also mentioned on all equity fund factsheets. Do you know which one? ------ Peeyush Chitlangia, CFA I help you decode complex financial concepts!

Most small businesses default to two forecasting methods: top-down or bottom-up. But they both share the same problem. The "why" behind performance isn't explained. These approaches are easy to model and are used all the time. But they can easily fail as companies grow larger and more driver based. (1) Top-down forecasting Many companies favor top-down because it's simple and aligned with strategic goals. But the biggest drawback is it's often completely disconnected from an operational reality. I use it for high-level financial forecasting and hardly ever for operational planning. • Leadership sets growth or margin targets • The P&L is segmented into business units • These targets cascade down the statements • Line-items are forecast on high-level assumptions (2) Bottom-up forecasting Bottom-up forecasting is based upon detailed inputs such as sales to customers, sales by SKU, hiring plans by individual versus job category or department, expense budgets, etc. The benefit of bottoms-up is it's detailed and grounded in operations. But it's usually time-consuming, fragmented, and hard to roll up consistently. • Individual contributors come up with their numbers • They share it with an accountant or financial analyst • The accounting/finance person puts it into a model • The model is updated constantly with new details (3) Driver-based forecasting Rather than come up with high-level assumptions that don't tie into operations, or granular detail that doesn't separate signal from noise, driver-based combines the best of both. In this example for a professional staffing company, we can tie future revenue to placements per recruiter, contract duration, markup percentage, bill rates, and recruiter headcount. This allows FP&A the ability to flex operating assumptions, test them, and quickly see what can be done on the ground to influence. Differences between the 3 methods matter: Top-down may set revenue at $50 million based upon an 8% growth rate. We can ask "how do we increase growth?" Bottoms-up may set revenue at $50 million based upon a monthly forecast of 200 customers. We can ask "what do we expect from each customer?" Driver-based planning may arrive at the same $50 million but ask "what operational levers can we press to truly move revenue and margin?" The result is forecasts that are faster, more explainable and easier to update. 💡 If you want to explore next-level modeling techniques, join live with 200+ people for Advanced FP&A: Financial Modeling with Dynamic Excel Session 2. https://lnkd.in/emi2xFdZ

◾ 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.

Past Performance Does Not Guarantee Future Results: (But It Usually Does). Despite the standard disclaimer that “past performance does not guarantee future results,” empirical data suggests that when evaluating private equity and private credit funds, past performance is indeed indicative of future results. Research by a leading investment consultant and a top-tier private markets data provider shows top-quartile managers tend to deliver strong performance in subsequent funds, especially when the observed manager has demonstrated such results in consecutive fund vintages. One study analyzing over 1,400 fund families found that top-quartile results are highly repeatable, particularly when they have demonstrated this track record over a six-year period that include 2 successive fund vintages. Another analysis of more than 1,700 funds confirmed that top-quartile managers consistently outperform their mean peers in subsequent vintages. This persistence isn’t coincidence. It reflects institutional advantages: experienced and highly motivated investment teams, repeatable investment processes, disciplined underwriting and structuring expertise, economic alignment, and proprietary deal sourcing networks. These strengths create structural edge, and that edge compounds generating alpha and absolute returns that consistently outperforms relevant benchmarks across market cycles. Sophisticated allocators and investment consultants evaluate performance holistically, assessing not just IRR, but also MOIC and DPI, which I call the tri-vector of investment performance. While a firm’s infrastructure, risk management, culture, are all important, the three dimensions of performance (IRR, MOIC, DPI) will always represent the cornerstone by which investment managers are measured. The Investment Advisors Act of 1940 requires that performance advertising by registered investment advisors (PE and PC operating in the U.S.) included relevant disclosure and disclaimers when marketing fund offerings, most sophisticated investors rely on historical performance for a reason. While there are no guarantees in life beyond death and taxes, when it comes to manager selection, track record matters. I believe that the strongest predictor of future outperformance is an alternative asset manager with all the requisite skills who has consistently done it before.

In Q1 2025, LTI (Ongoing Equity) Programs Had 4x the “Pay for Performance” Differentiation for Promoted Employees Vs. Salary Raises Companies generally reward top performers through three types of compensation programs: [A] Salary Raises [B] Long Term Incentives (LTI)–often ongoing equity grants [C] Short Term Incentives (STI)–often called a bonus program Today, let’s compare how much differentiation there is across the market for top performers between [A] and [B]. ________________ 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆: We recently took a look at Q1 2025 merit cycle data across 46k+ employees from Pave's dataset. 1st, our data science team grouped and analyzed employees across four groups:  • [1] Promoted  • [2] Above expectations (no promo)  • [3] Meets Expectations or equivalent (no promo)  • [4] Below Expectations (no promo) 2nd, our data science team looked at two dimensions across salary and ongoing equity grants  • [1] What % of employees received a compensation update?  • [2] For those who received, what was the size of the increase? Note that for equity, this was measured by the % increase in net equity value compensation vesting over the next 12 months 3rd, our data science team multiplied “participation” with “amount” to find the “𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲” as a method of measuring pay for performance. ________________ The Results: ✅ 𝗣𝗿𝗼𝗺𝗼𝘁𝗲𝗱  => Salary: +9.7% expected value increase => Ongoing Equity: +38.6% expected value increase ✅ 𝗔𝗯𝗼𝘃𝗲 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +4.5% => Ongoing Equity: +11.0% ✅ 𝗠𝗲𝗲𝘁𝘀 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗿 𝗘𝗾𝘂𝗶𝘃𝗮𝗹𝗲𝗻𝘁 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +3.1% => Ongoing Equity: +3.8% ✅ 𝗕𝗲𝗹𝗼𝘄 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗡𝗼 𝗣𝗿𝗼𝗺𝗼)  => Salary: +0.3% => Ongoing Equity: +0.0% expected value increase ________________ 𝗠𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1️⃣ 𝗣𝗿𝗼𝗺𝗼𝘁𝗲𝗱 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝗮 𝗺𝗲𝗱𝗶𝗮𝗻 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝟯𝟴.𝟲% “𝗲𝗾𝘂𝗶𝘁𝘆 𝗿𝗮𝗶𝘀𝗲” 𝘃𝘀 𝗮 𝟵.𝟳% 𝘀𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗶𝘀𝗲. This means that for promoted employees, the equity comp is ~4x as outsized from a pay for performance standpoint. 2️⃣ 𝗠𝗲𝗮𝗻𝘄𝗵𝗶𝗹𝗲, 𝘁𝗵𝗲 “𝗲𝗾𝘂𝗶𝘁𝘆 𝗿𝗮𝗶𝘀𝗲𝘀” (𝟯.𝟴%) 𝗮𝗿𝗲 𝗺𝘂𝗰𝗵 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘀𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗶𝘀𝗲𝘀 (𝟯.𝟭%) 𝗳𝗼𝗿 “𝗺𝗲𝗲𝘁 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀” 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀. This suggests that the real LTI/ongoing equity comp differentiation is happening for top performers (both those in the “promoted” and “above expectations (no promo)” buckets. ________________ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 & 𝗛𝗥 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: Analyze your company’s “expected value” salary and equity raise amounts. How do your outcomes compare to the Q1 2025 benchmarks from this post? And where + how should you consider tweaking your "recommendation logic” to guide your company towards more or less merit cycle differentiation for different cohorts of employees?

Not all 15% returns are created equal 🎭 Two hedge funds report the same annual return: +15%. But when you break it down, the story flips: 🔹 Fund A • +12% from broad market beta • +2% from crowded trades • +1% from idiosyncratic bets 🔹 Fund B • +3% from market beta • +2% from crowded trades • +10% from idiosyncratic alpha Both funds look identical at first glance. But if you’re an allocator seeking true alpha, you know where you’d place your bet. In today’s markets, alpha ≠ high returns. Alpha = unique, uncorrelated, skill-driven returns. If you aren’t measuring idio risk, you might just be rewarding beta dressed up as genius. Anyone else here diving deep into this space? Would love to swap notes. #HedgeFunds #PortfolioAnalytics #RiskManagement #AlternativeInvestments #Alpha

One of the most concerning developments is the growing divergence between professional and retail investors. Institutional investors have quietly reduced risk, shifting toward defensive sectors and fixed income, while retail traders continue chasing speculative trades. Sentiment surveys confirm this imbalance, showing extreme bullishness among small traders, especially in options markets. With these risks building under the surface, prudent investors should proactively protect their portfolios. No one can predict precisely when the market will correct, but the ingredients for a sharp downturn are clearly in place. Savvy investors should use this period of complacency to reduce risk exposure before the cycle turns. Here are six practical steps investors should consider: ▪️ Rebalancing portfolios to reduce overweight exposure to technology and speculative growth names. ▪️ Increasing cash allocations to provide flexibility during periods of volatility. ▪️ Rotating into more defensive sectors like healthcare, consumer staples, and utilities that tend to outperform during corrections. ▪️ Reducing exposure to leverage by avoiding margin debt and leveraged ETFs. ▪️ Using options prudently—not for gambling, but for protecting portfolios through longer-dated puts on broad market indexes. ▪️ Focusing on companies with strong balance sheets, stable earnings, and reasonable valuations. ▪️ The explosion of zero-day options trading is not a sign of a healthy market. It is a symptom of an unhealthy market increasingly driven by speculation rather than investment discipline. Retail traders have moved from investing to gambling, chasing fast profits while ignoring the mounting risks. Greed is rampant, leverage is extreme, and complacency is near record levels. Markets can remain irrational longer than expected, but history tells us these speculative periods always end in a painful correction. Bull markets do not die quietly; they end with euphoric retail excess followed by painful corrections. Investors who recognize the signs early will avoid the worst of the fallout and be positioned to capitalize when value opportunities return.