Analyzing Market Volatility

• S&P 500 annual return dispersion in 2024 rose to 70 pp, the highest level outside of recessions since 2007. High return dispersion reflects a favorable stock-picking environment. 6 of 11 sectors exhibited above-average return dispersion in 2024. • Return dispersion has been supported by low stock correlations and high single stock volatility. Realized average S&P 500 stock correlation registered 0.2 during the past 6 months, ranking in the 6th percentile relative to the past 20 years. Options-implied volatility and correlations suggest the environment of elevated return dispersion should persist. • Our analysis shows that the market has been more micro-driven than average since the start of 2023. During the last 6 months, 74% of the typical S&P 500 stock’s returns have been driven by company-specific rather than "macro" factors vs. an average of 58% over the past two decades. • We expect the current micro-driven environment will persist in 2025 for three reasons. First, GS economic forecasts point to a healthy growth environment this year. Second, continued AI development and adoption should create differentiation across stocks. Third, elevated policy uncertainty also suggests elevated dispersion. Debates over trade, tax, fiscal, and other policies represent potential catalysts for additional return dispersion. • Our forward-looking dispersion framework identifies S&P 500 sectors and stocks most likely to generate exhibit micro-driven volatility in coming months. Currently stock-pickers should focus efforts within the Consumer Discretionary, Info Tech, and Communication Services sectors. We highlight a list of 25 stocks across 7 sectors that represent idiosyncratic opportunities for alpha generation.

We’re only human Measures of economic policy uncertainty have eclipsed the pandemic. The largest increases are due to trade policy uncertainty and where the US will end up with regard to tariffs. Why do we care? A top 10 list 1. A “wait and see” mentality emerges. Large, hard to reverse spending decisions by firms and households are put on hold. That acts as a drag or tax on economic activity. 2. Business investment feels the bulk of the effects and contracts. 3. Credit conditions tighten, especially for those most exposed to tariffs, which further constrains investment. Even firms with plans to invest can be hobbled. 4. The banking system becomes less stable. Loan defaults pick up as the economy slows. Consumer delinquencies are already on the rise. 5. Unemployment rises as growth slips to levels that no longer enable the economy to absorb those entering the labor force. What is unknown is whether that weakness will cause a further slowdown in wage growth given the stagflationary effects tariffs. Workers tend to demand compensation for the escalation in the cost of living due to tariffs. 6. Consumer spending skips a beat. Job losses confirm fears and and trigger a larger blow to aggregate incomes and spending. 7. Financial market volatility soars and asset prices fall. People lose retirement savings and feel poorer, companies can't raise money by selling stock and loan losses accelerate. Confidence among consumers and busineses further falters. 8. Monetary policy becomes less effective as fear prevents firms and consumers from reacting to stimulus once it starts. 9. Contagion. Foreign firms and governments perceive the US as an unreliable and less predictable partner. Supply chains are reconfigured to reduce their dependence on US markets. 10. If left unchecked, sustained periods of uncertainty can trigger a breakdown of economic and political systems. Five things can help mitigate and derail bouts of uncertainty from becoming a vicious global cycle: 1. Strong institutions. They create confidence that rules won’t arbitrarily change, and work to counter the “wait and see” behaviors that curb growth. The judiciary plays a key role. 2. Clear communications by the Fed. That and a lack of political interference tempers uncertainty regarding the trajectory of inflation. 3. Automatic fiscal stabilizers, which provide immediate, predictable government response without political gridlock that can worsen a crisis. 4. Well capitalized banks, which prevent larger credit crunches from taking root. 5. International cooperation, which limits contagion. Bottom line Bouts of uncertainty trigger fight or flight reactions. That has resulted in a toxic mix of panic and paralysis. Expect whiplash, as the surge in activity ahead of tariffs borrows from growth later in the year. As for national security, that could be shored up with a targeted & strategic approach to industrial policy. Break bread not ties when possible. Be kind; pay it forward.

A drop in interest rates is likely to trigger an explosion of M&A activity among Private Equity firms in the current environment. This viewpoint is commonly shared by Consultants and Investors with whom I speak. However, is a prolonged period of lower rates actually likely in the foreseeable future? PE funds are desperate to transact, not only because they are sitting on record levels of undeployed capital from recent fundraises, but because many existing investments remain in underperforming portfolios longer than anticipated, awaiting favourable exit valuations. Bain & Company has warned of a "towering backlog" of companies that must be sold so that cash can be returned to investors such as pension funds, which have become overexposed to unlisted investments. This stagnation in capital recycling is critical, as it directly impacts PE firms' ability to generate operational fees. However, many overlook the fact that allowing interest rates to drop and thus enabling an explosion of Private Equity M&A is in itself highly inflationary. If the latent potential for deal activity within the market is fully realised, we will quickly face a scenario where interest rates need to be increased substantially. It seems we are heading towards a prolonged period of volatility, driven by gyrations in the interest rate environment. This volatility is unlikely to deter PE funds from transacting; however, the rules of the game will change. Margins will be squeezed to a far greater degree than before as competition for deals intensifies and the cost of transactions rises. More than ever, PE firms will need to focus on operational value creation within their portfolio companies to generate returns for investors. What's more, timing is going to be everything.

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.

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

📈 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

💭 AI is transforming finance—but is it truly reshaping the core of Quant Finance beyond just trading? While algorithmic trading gets most of the attention, AI is making a deeper impact in risk modeling, derivatives pricing, and portfolio optimization. 1️⃣ Sentiment Analysis for Market Forecasting (LLMs & NLP Models) 👉 Why it matters: Markets don’t move on fundamentals alone—investor sentiment drives volatility. AI-powered NLP can process news, earnings calls, analyst reports, and social media to detect sentiment shifts in real time, providing traders with early signals before price movements occur. 🛠 Real Models in Action: ✔ FinBERT (Hugging Face) – A finance-focused NLP model trained on earnings reports and financial news to extract sentiment insights. ✔ GPT-4 fine-tuned for finance – Used in hedge funds to generate sentiment-based trading signals and volatility forecasts. ✔ BloombergGPT – Specialised for market-related NLP tasks, enhancing automated financial analysis. 2️⃣ AI for Derivatives Pricing & Risk Management (Deep Learning & Stochastic Models) 👉 Why it matters: Traditional pricing methods rely on Monte Carlo simulations and PDE-based models, which can be computationally expensive and slow. AI accelerates pricing and hedging strategies by learning risk-neutral representations and improving predictive accuracy for exotic derivatives. 🛠 Real Models in Action: ✔ Neural SDEs (Stochastic Differential Equations) – AI-driven models that learn underlying stochastic processes for better risk-neutral pricing. ✔ Physics-Informed Neural Networks (PINNs) – AI-enhanced solvers that significantly speed up complex derivatives pricing calculations. ✔ Deep Hedging Models – AI-powered dynamic hedging strategies that adjust in real time, outperforming traditional Black-Scholes delta hedging in volatile markets. 3️⃣ AI for Dynamic Portfolio Optimization (Reinforcement Learning & Bayesian ML) 👉 Why it matters: Traditional Mean-Variance Optimization (MVO) assumes fixed return distributions and correlations, which often break down during market shifts. AI allows adaptive asset allocation, helping investors manage risk dynamically and rebalance portfolios in response to changing market regimes. 🛠 Real Models in Action: ✔ Reinforcement Learning Portfolio Management (RLPM) – Uses deep Q-learning and policy gradient methods to find optimal asset allocation strategies under different market conditions. ✔ Bayesian Neural Networks (BNNs) – Introduces uncertainty estimation in return predictions, improving risk-aware decision-making. ✔ Hierarchical Risk Parity (HRP) – AI-powered clustering of assets for better diversification and tail-risk mitigation, outperforming classical Markowitz models. #AI #QuantFinance #MachineLearning #RiskManagement #DerivativesPricing #PortfolioOptimization #SentimentAnalysis #FinancialModeling #FinTech #HedgeFunds #MarketRisk #FinanceJobs

Tariff Turmoil Rattles Bond Markets: U.S. Skirts Financial Crisis, But Dangers Persist Tariffs Shake Investor Confidence in Treasury Markets The U.S. narrowly avoided a potential financial crisis as investor fears surged in response to President Trump’s tariff threats. While a 90-day pause on some tariffs brought momentary relief, the bond market’s volatile reaction exposed deeper vulnerabilities in U.S. financial stability. Long-term Treasury yields remain elevated, and any renewed tariff escalation could reignite market chaos. Market Reactions and Underlying Concerns • Bond Market Volatility • Yields on long-term Treasury bonds fluctuated wildly during the week due to uncertainty over the administration’s tariff strategy. • The bond market showed signs of severe distress, prompting fears of a larger financial disruption. • Despite Trump’s reassurances, the yield instability indicated underlying fragility. • Tariff Pause Offers Temporary Relief • Trump announced a 90-day pause on additional tariffs, which briefly boosted stock market confidence. • The announcement helped reduce immediate panic in financial markets but failed to resolve long-term concerns. • Investors worry the administration could reimpose or expand tariffs, leading to renewed turmoil. • Investor Skepticism Grows • Market participants questioned whether the Treasury Department was adequately managing the crisis. • Doubts about leadership and strategy in Washington contributed to ongoing unease in bond markets. • Persistent uncertainty over trade policy continues to weigh heavily on investor sentiment. Why It Matters This episode highlights the precarious relationship between political decision-making and market stability. The bond market’s intense reaction to tariff threats underscores how quickly confidence can erode when fiscal policy seems unpredictable or mismanaged. If trade tensions flare again, similar or worse disruptions could follow, potentially undermining the U.S. government’s ability to borrow at stable rates. This serves as a warning that politically driven economic shocks—especially in the form of sudden tariff escalations—can have cascading effects across financial markets, threatening both domestic and global economic stability.

I really like this paper from researchers at HEC Montréal, titled Deep Implied Volatility Factor Models for Stock Options.  It elegantly solves the classic trade-off between modern machine learning and traditional financial modeling. Sam Cohen, Lukasz Szpruch, and myself have written about the benefit of these hybrid models in "Black-box model risk in finance". The computationally "heavy" part—training the neural network to learn the complex volatility shapes—is done only once. Pure machine learning models are often "black boxes," which regulators and risk managers dislike. It blends power with interpretability. 1. One-Time Training: A neural network is trained once on historical data to learn a stock's unique basis factors for volatility, including its specific pre-earnings ramp-up shape. 2. Daily Data Ingestion: Each day, ingest current market data for all traded options: their moneyness, maturity, implied volatility, and the time-to-earnings-announcement (TTEA). 3. Rapid Daily Fitting: Perform a fast daily linear regression (OLS) to calculate the factor loadings (betas) that best fit the day's observed market prices using the pre-trained basis factors. 4. Construct IV Surface: The daily betas yield a complete, smooth function for the entire IV surface, allowing for immediate and consistent pricing of any option, including non-standard strikes. 5. Derive Risk Metrics: Use the complete surface to compute advanced metrics like the stock's risk-neutral probability distribution or a custom, 30-day VIX-style index for targeted risk analysis.

Early in my career, I lost a client because I didn’t understand what was really going on. The market dipped. He panicked. I showed him charts, stats, and projections. He left anyway. Back then, I thought being the smartest person in the room was enough. It wasn’t. Because clients don’t make decisions based on logic. They make them based on fear, loss, ego, and emotion. Behavioral finance changed the game for me. Not as a theory... but as a skill. Advisors who win with high-net-worth clients know this: - They ask better questions. - They listen more than they talk. - They manage behavior, not just portfolios. That’s what builds trust when volatility hits. That’s what keeps clients from jumping ship. That’s what gets you referrals from people who feel safe in your process. If you want to stand out, don’t just deliver returns. Help your clients stay the course when it matters most. That’s the value they’ll never forget. What’s one thing you do to bring emotional intelligence into your client conversations?