Risk-Adjusted Returns Analysis

◾ 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.

Multi-Asset Strategy: Case Study on Gold as a Reliable Ally Equity markets are inherently volatile, presenting a significant challenge for investment professionals. Since April 2022, the NIFTY Index has delivered an approximate return of 40%, with a compound annual growth rate (CAGR) of 12.4%. However, this performance came with a volatility of 13.3%, resulting in a risk-adjusted return of 0.9. The critical question for fund managers and researchers: Are you adequately compensated for the risks you undertake? By introducing Gold into the allocation mix—through a 25% allocation complemented by dynamic equity allocations—the performance narrative changes dramatically. This diversified approach would have outperformed the NIFTY Index in 20 out of 34 months. Key Outcomes for Reducing Volatility: 1) Absolute Returns: 63% — an excess of 23% over the NIFTY Index. 2) Risk-Adjusted Return: 1.7 — nearly double the NIFTY’s performance on a risk-adjusted basis This case study highlights how a data-driven asset allocation approach can significantly enhance returns while managing volatility, making it a valuable option for quantitative researchers and fund managers. For comparison, this strategy also outperformed the NSE 500 Index in 18 out of 34 months, demonstrating its robustness across different benchmarks. Gold isn’t just a haven—it’s a powerful ally in constructing resilient portfolios. This case study is free for quantitative researchers to explore the art and science of asset allocation. This evolves with the market and is updated in real time. Link: https://lnkd.in/dr-5zdeg Follow Quantace Research Disclaimer: This content is intended for educational purposes only and is designed for quantitative researchers and fund managers. Past performance is not indicative of future results, and this should not be considered investment advice. Quantace Research is a SEBI Registered RA INH000018258 and an expert in Quantitative Investing

Modern quantitative analysis methodologies used in portfolio management mainly fall into the following categories: • Predict-then-optimize: These methods first forecast asset prices or returns and then solve an optimization problem (e.g., mean-variance model) to determine the portfolio. While easy to implement, their performance heavily depends on accurate predictions, which are challenging due to market volatility. • RL (Reinforcement Learning) based methods: Instead of focusing on accurate price prediction, the RL approaches directly learn portfolio allocations by maximizing a reward function; e.g., cumulative return using PPO (Proximal Policy Optimization). However, they often inefficiently optimize from surrogate losses, as portfolio optimization differs from typical RL applications where rewards are more straightforwardly differentiable. • DL (Deep Learning) based approaches: These methods address RL limitations by directly optimizing financial objectives (eg, Sharpe ratio). Despite this advantage, they still face some limitations. First, the dynamic market and low signal-to-noise ratio in historical data hinder model generalization. Solutions like simple architectures or external data (e.g., financial news) either fail to capture essential features or rely on information that may be unavailable. Second, DL methods produce fixed portfolios that overlook varying investor risk preferences and lack fine-grained risk control. To address these shortcomings, the authors of [1] propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of 2 main phases: • return-related maximization • risk control In the return-related maximization phase, 2 auxiliary objectives; return rate prediction and return rate ranking, are introduced and combined with portfolio optimization to mitigate overfitting and improve the model's generalization to future markets. Subsequently, in the risk control phase, 2 methods; portfolio interpolation and portfolio improvement, are introduced to achieve fine-grained risk control and rapid adaptation to a user-specified risk level. For the portfolio interpolation method, the authors show that the adjusted portfolio’s return rate is at least as high as that of the minimum-variance optimization, provided the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method achieves higher return rates than portfolio interpolation while maintaining the same risk level. Extensive experiments on 3 real-world datasets: NAS100, DOW30 and Crypto10. The results, evaluated using metrics such as Annualized Percentage Rate (APR), Annualized Volatility (AVOL), Annualized Sharpe Ratio (ASR), MDD, demonstrate the superiority of MILLION compared to the baselines: MVM, DT, LR, RF, SVM, LSTM-PTO, LSTMHAM-PTO, FinRL-A2C, FinRL-PPO, LSTMHAM-S, LSTMHAM-C and LSTMHAM-M. Links to the preprint [1] is provided in the comments.

📌 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

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!

📈 Volatility-Managed Portfolios Hello #FinanceCommunity, This paper by Moreira and Muir on volatility-managed portfolios warrants your attention. The authors challenge prevailing assumptions about risk and return, finding that certain volatility-managed portfolios can offer higher risk-adjusted returns. This runs counter to long-held theories. 🔑 Key Takeaways: 1️⃣ Risk-Adjusted Returns: The paper introduces a strategy that scales monthly returns by the inverse of their previous month's realized variance. This simple yet effective approach can significantly improve alphas and Sharpe ratios. 2️⃣ Contrarian Approach: Interestingly, the strategy advises taking less risk during high-volatility periods, including recessions and financial crises. This is contrary to the popular belief that these are the times to take more risks. 3️⃣ Utility Gains: The strategy offers substantial utility gains for mean-variance investors, making it a robust and profitable approach. 4️⃣ Challenges to Existing Models: The findings pose a challenge to representative agent models and macro-finance models, suggesting that an investor’s willingness to take stock market risk must be higher in periods of high stock market volatility. 5️⃣ Robustness: The strategy is robust to realistic transaction costs and leverage constraints, making it practical for real-world implementation. If you're interested in asset pricing, risk management, or portfolio optimization, this paper is worth a read. It not only offers actionable insights but also opens up new lines of inquiry in the finance research community. #Finance #AssetPricing #RiskManagement #PortfolioOptimization

This is everything you need to know about the Sharpe ratio: In the complex world of investment management, the Sharpe Ratio is a critical measure, enabling fund managers to assess the risk-adjusted return of their portfolios. What is the Sharpe Ratio? Developed by Nobel laureate William F. Sharpe, the Sharpe Ratio evaluates investment performance compared to a risk-free asset, adjusting for risk. It’s calculated by subtracting the risk-free rate from the investment return and dividing the result by the investment’s standard deviation. Why It Matters * Risk-Adjusted Returns: Provides a clear view of returns in relation to risk. * Performance Comparison: Helps compare the efficiency of different investments. * Investor Confidence: Reassures investors about fund management efficiency. How Does It Work? * Excess Return: Return of the investment minus the risk-free return. * Standard Deviation: Measures investment volatility or risk. * Ratio Calculation: Excess return divided by standard deviation. Interpreting the Sharpe Ratio * Good Ratios: Above 1.0: Good risk-adjusted returns. * Above 2.0: Very good, with significantly outweighing returns. * Above 3.0: Excellent, superior returns with low risk. * Bad Ratios:Below 1.0: Returns not compensating for risk. * Around 0: Returns only equal to the risk-free rate. * Negative: Underperforming the risk-free rate, indicating losses. Context Matters Economic environment and asset class are crucial. High-interest periods make high Sharpe Ratios challenging. Different asset classes have varying baseline expectations. Conclusion The Sharpe Ratio is vital for fund managers, helping them gauge and compare investment efficiency by considering both returns and risks. It underscores the importance of achieving high returns with a keen eye on risk, aligning investment strategies with investor objectives and risk tolerance.

You may know what ROC is, but have you come across 𝐑𝐀𝐑𝐎𝐂 yet?   𝐑𝐢𝐬𝐤-𝐀𝐝𝐣𝐮𝐬𝐭𝐞𝐝 𝐑𝐞𝐭𝐮𝐫𝐧 𝐨𝐧 𝐂𝐚𝐩𝐢𝐭𝐚𝐥 (𝐑𝐀𝐑𝐎𝐂) is a financial ratio that helps companies assess the return they’re generating on capital while factoring in the risks they’re taking   It’s crucial for understanding whether the returns justify the risks involved and is a powerful tool for comparing investments with varying risk levels   ↪ 𝐓𝐡𝐞 𝐅𝐨𝐫𝐦𝐮𝐥𝐚:   𝐑𝐀𝐑𝐎𝐂 = (r − e − el + ifc) / c   Where: 𝐫 = Revenue 𝐞 = Expenses 𝐞𝐥 = Expected loss (average loss over a specified period) 𝐢𝐟𝐜 = Income from capital (capital charges × the risk-free rate) 𝐜 = Capital   𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫, 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐁 - invests ₹10 crore in a high-risk startup   ↪ 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬: Revenue (r) = ₹1.5 crore Expenses (e) = ₹0.3 crore Expected Loss (el) = ₹0.8 crore (25% potential loss) Income from Capital (ifc) = ₹0.4 crore Capital (c) = ₹10 crore   Now, let’s calculate the RAROC for Company B: RAROC = (1.5 − 0.3 − 0.8 + 0.4) / 10  𝐑𝐀𝐑𝐎𝐂 = 0.08 or 8%   ↪ 𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧? Company B’s investment yields a risk-adjusted return of 8%, which tells us that after accounting for all costs, losses, and capital charges, the return on this investment is modest but positive #return #capital LinkedIn

The Hidden Risk Premium of "High Return" Investments A tale of two investment strategies: Investor A: Pursued a complex adaptive reuse project projecting 22% IRR with a single exit strategy Investor B: Acquired a multi-tenant industrial property with 7.5% immediate cash flow, backed by an experienced operator with three viable exit paths Three years later: Investor A: Still awaiting returns, facing execution challenges and a narrowing exit window Investor B: Has collected $225,000 in distributions, seen 12% appreciation, and maintains multiple exit options What proformas don't capture: • Execution risk premium • Value of multiple exit strategies • Operator experience factor • Flexibility in changing market conditions The best investors don't chase returns—they focus on risk-adjusted returns through proven operators and deal structures with built-in contingency plans. #RiskAdjustedInvesting #CapitalAllocation #OperatorExperience #ExitStrategyPlanning