Month: August 2026

The Effects of ETF Investing on Financial Markets

Market dynamics are changing rapidly. For example, we recently discussed how the growth of retail options trading is changing market dynamics. At the same time, passive investing, largely through ETFs, has grown substantially over the past decade. In this post, we discuss how ETF investing is changing market dynamics as well.

The Impact of Passive ETF Ownership on the Market

Passive investing, particularly through Exchange-Traded Funds (ETFs), has transformed the world of finance. With ETFs, investors can gain exposure to various asset classes, including stocks, bonds, commodities, and more, while maintaining a passive and diversified approach to their portfolios. Unlike actively managed funds, which aim to outperform the market, passive investing with ETFs seeks to replicate the performance of an underlying index. This approach often comes with lower fees and can be an alternative choice for long-term investors looking for broad market exposure and reduced stock-specific risk. The ease of buying and selling ETFs on stock exchanges makes them a flexible tool for building a well-diversified, cost-effective investment portfolio, appealing to both individual investors and institutions.

While ETFs offer various advantages, their impact on the market has raised questions about potential negative effects. Reference [1] delved into this very issue, investigating the market repercussions of ETFs.

Findings

-The study finds that higher passive ETF ownership leads to stronger and more persistent return reversals.

-Higher passive ownership is associated with wider bid-ask spreads, greater exposure to aggregate liquidity shocks, higher idiosyncratic volatility, and greater tail risk.

-The results show that passive ETF ownership reduces the importance of firm-specific information in stock returns.

-Higher passive ownership increases the importance of transitory noise and exposure to market-wide sentiment shocks.

-The study finds that increased passive ETF ownership reduces stock price informativeness and potentially weakens market efficiency.

-The findings highlight a potential trade-off between the benefits of passive investing and the costs of reduced price efficiency and market-making capacity.

Briefly, an increase in passive ETF ownership results in stronger and longer-lasting return reversals, greater idiosyncratic volatility, and elevated tail risk. Higher passive ETF ownership reduces the significance of firm-specific information for returns while increasing sensitivity to transitory noise and market-wide sentiment shocks.

Reference

[1] Höfler, Philipp and Schlag, Christian and Schmeling, Maik, Passive Investing and Market Quality (2023). SSRN 4567751

Price Fragility, ETF Flows, and Non-Fundamental Shocks

Financial asset fragility refers to the vulnerability of an asset’s price to sudden and disproportionate changes in response to shocks, even if those shocks are relatively small. This fragility often stems from factors like excessive leverage, crowded positioning, liquidity mismatches, or overreliance on certain market assumptions.

Reference [2] utilized the concept of stock price fragility to study the impact of ETFs on the market. Stock fragility is derived from information on an asset’s ownership composition, combined with data on the correlation between owners’ non-fundamentally driven trades.

The paper generalizes mutual fund (MF) fragility to ETF fragility because it argues that ETF flows are indicative of non-fundamental demand shocks. Theoretically, the creation and redemption of ETF shares mimic relative mispricing correction. Therefore, ETF premiums or discounts (i.e., relative mispricing) signal non-fundamentally driven price distortions.

Findings

-The study proposes an ETF-based measure of stock price fragility as an alternative to traditional measures based on equity mutual fund flows.

-The ETF-based measure significantly improves the ability of stock price fragility to predict future stock return volatility.

-The study finds that the explanatory power of mutual fund-based fragility has declined.

-The ETF-based measure partially captures the effect of institutional ownership on stock price volatility.

-The predictive power of ETF-based fragility for next-quarter stock price volatility is primarily driven by active ETFs.

-The study notes that the distinction between passive and active investing has become increasingly blurred as the ETF industry has evolved.

-Specialized, industry-specific, and characteristic-based active ETFs may reflect investors’ extrapolative beliefs, speculative demand, and sentiment-driven demand.

In summary, the article developed the concept of ETF fragility and showed that active ETFs have an impact on the market.

This is an interesting article, as it quantifies the concept of fragility. This concept can be further applied to study, for example:

  • The impact of income ETFs (those that sell options) on the market
  • Whether a price gap is filled, if it’s fundamentally induced or just the result of a demand shock.

Reference

[2] H. Galindo Gil and R. Lazo-Paz, An ETF-based measure of stock price fragility, Journal of Financial Markets 72 (2025) 100946

Closing Thoughts

Together, these studies highlight how the growth and evolution of ETFs can affect stock prices and market dynamics. Higher passive ETF ownership is associated with reduced price informativeness, greater non-fundamental noise, higher volatility, and increased tail risk, while the second study shows that ETF-based measures of stock price fragility can better capture and predict these effects, particularly through active ETFs.

Overall, the findings suggest that ETF ownership and flows have become increasingly important factors in understanding stock price volatility and market efficiency.

Market Regimes and Changing Market Dynamics

Markets have been behaving unusually lately. In May, equity indices rose while volatility and skew also increased, a relatively rare occurrence historically. Since last week, the same phenomenon has emerged again, with the spot/volatility correlation turning positive.

Is this still a rare occurrence? We don’t know. But one thing is clear: regime detection is becoming increasingly important in today’s markets. In this post, we explore a couple of approaches for detecting market regimes.

A Regime Classification Framework for Mean-Reverting and Trending Markets

Regime classification is important in asset and risk management. Traditional approaches classify regimes based on direction, bullish or bearish, and volatility, high or low.

Reference [1] departs from this framework and instead classifies markets as mean-reverting or trending. Specifically, it uses return thresholds of 0.5%, 0.75%, and 1% to define regimes and examines SPY, QQQ, DIA, and IWM over the period 2000 to 2024.

Findings

-The study evaluates Random Forest and Neural Network classifiers using macroeconomic announcement indicators and technical features, including VIX, RSI, and ATR.

-It uses 25 years of daily data from 2000–2024 for IWM, SPY, QQQ, and DIA.

-The study frames next-day ETF behavior as a binary classification problem between “oscillating” and “trending” days.

-Oscillating days are defined using intraday movement thresholds of 0.5%, 0.75%, and 1%, with movements exceeding these thresholds classified as trending.

-At the 0.5% threshold, Neural Networks outperform a naive classifier by 13.4% for IWM, 15.4% for SPY, 4.7% for QQQ, and 3.2% for DIA.

-SPY produces the strongest results, with AUC values reaching 0.67–0.74 at the 0.75% and 1% thresholds.

-IWM shows improvements of 5.7%–13.4% across thresholds, with evidence of predictive power at the 0.5% and 0.75% thresholds.

-QQQ shows improvements of 4.7%–6.1%, but its predictive performance is weaker at lower thresholds.

-The results show that predictive performance varies materially across ETFs and oscillation thresholds, with some configurations providing limited discriminatory power.

In summary, the results show that the best case achieves a 15.4% improvement in prediction over a naive strategy for SPY using a neural network with a 0.5% threshold; although in many cases the improvement is more modest, in the range of 1 to 5%, and varies significantly across ETFs.

While the study has several limitations, it points to a more relevant research direction: predicting the magnitude-based regime appears slightly easier than predicting direction, and machine learning is effective as a risk or regime filter rather than as a direct alpha-generating signal.

Reference

[1] Azizi, S. (2026), Leveraging Machine Learning for Financial Forecasting: Distinguishing Market Trends from Oscillations in ETFs, Journal of Risk and Financial Management, 19(4), 262.

Entropy-Based Regime Detection of Tail Risks

Reference [2] proposes an alternative regime classification by distinguishing between “normal” and heavy-tailed regimes. Specifically, the study develops a nonparametric method to detect financial market regimes using differential entropy rather than volatility alone. The underlying idea is that while volatility measures dispersion, entropy captures the full distributional uncertainty, including tail behavior, which becomes particularly important during crisis periods.

The authors estimate entropy using a kernel density estimator with a heavy-tailed kernel in rolling windows and compare entropy with variance. When markets behave approximately Gaussian, i.e., normally, entropy and variance move together; during turbulent periods, the relationship breaks down, revealing heavy-tailed regimes that volatility alone cannot identify.

Findings

-The study develops a differential entropy approach to identify financial market regimes through changes in distributional complexity rather than variance alone.

-The method uses a data-adaptive heavy-tailed kernel and combines entropy with tail-index analysis within a moving-window framework.

-Monte Carlo experiments show that the approach is robust and sensitive to changes in tail behavior.

-Applied to the Ibovespa, S&P 500, Nikkei, and SSE Composite from 1998 to 2025, the method identifies heavy-tailed regimes associated with major periods of market turbulence.

-These periods include the Dot-com Bubble, Global Financial Crisis, COVID-19 shock, and the 2025 tariff-related crisis.

-Gaussian regimes correspond to periods of relative stability and market efficiency.

-The results show that variance and entropy do not necessarily move together during crises.

-While volatility measures dispersion, entropy captures broader uncertainty and tail risk, providing a complementary measure of systemic instability.

In short, the paper developed a regime detection method based on entropy, which provides an alternative regime indicator that captures tail risk and structural shifts that standard volatility measures may miss.

This represents an important contribution to the literature, particularly in the context of managing tail risks and risk management more broadly.

Reference

[2] Raul Matsushita, Iuri Nobre, Sergio Da Silva, Beyond volatility: Using differential entropy to detect financial market regimes, Chaos, Solitons and Fractals 202 (2026) 117553

Closing Thoughts

Together, these studies highlight two different approaches to market regime detection. The first uses machine learning to classify next-day ETF behavior as oscillating or trending, while the second uses differential entropy to identify shifts in market uncertainty and tail behavior. Both demonstrate that market regimes can be characterized using information beyond conventional volatility measures, although their effectiveness varies across markets, thresholds, and conditions.

Making Option Pricing Models More Practical

The Black-Scholes-Merton model is one of the cornerstones of modern quantitative finance. Despite its elegance and widespread use, its simplifying assumptions limit its ability to capture many features of real financial markets. As a result, researchers continue to extend the model to make it more realistic and applicable in practice.

In this post, we discuss two such extensions. The first incorporates a stochastic volatility model and intraday momentum into the option pricing framework. The second applies stochastic volatility models under the real-world measure to portfolio construction and volatility targeting, highlighting their practical use in risk management beyond derivative pricing.

Incorporating Momentum into Option Pricing Models

The Black–Scholes–Merton (BSM) model is a cornerstone of derivative pricing; however, it is not without limitations, and researchers continue to extend it. Reference [1] proposes an extension by incorporating intraday momentum into the BSM framework. This is achieved by introducing a drift term that represents intraday momentum, measured using a simple moving average of returns.

The model also adopts a modified Heston-type structure in which volatility follows a mean-reverting square-root process, allowing it to capture volatility clustering and remain consistent with empirical features such as volatility smiles. The momentum-driven drift adjustment influences the expected price path, while the stochastic volatility process models uncertainty around that path.

Findings

-The study extends the BSM option pricing framework by incorporating intraday momentum into the drift term of a stochastic volatility-modified model.

-It models time-varying volatility using a Heston-type stochastic volatility model and derives the momentum term from recent relative price changes.

-The study analyzes the impact of intraday momentum on stock prices, volatility, and option valuations, with particular attention to high-momentum scenarios.

-Numerical simulations show that positive momentum increases option valuations, while negative momentum decreases them.

-The study finds that the proposed model converges to the classical Black-Scholes model under low-volatility or low-momentum conditions.

-It concludes that incorporating momentum provides a theoretical framework for evaluating momentum-driven effects in derivative pricing and establishes quantitative metrics for empirical testing.

In short, the paper introduces a momentum term based on recent price changes to dynamically adjust the drift, capturing short-term intraday effects. Numerical results show that strong positive or negative momentum leads to substantial deviations from standard BSM prices, indicating that momentum is an important factor in option pricing.

This represents an interesting and potentially useful extension of the BSM model for traders and risk managers. However, as noted by the authors, the findings are based on simulated results rather than empirical data, and it would be valuable to see the model tested on real market data.

Reference

[1] Hossain, M.S., Yuan, X. & Sultan, S. Momentum-Driven Option Pricing: Integrating Intraday Trends into Financial Derivative Models. Comput Econ (2025).

Use of the Real-World Measure in Portfolio Management

In the realm of finance, the risk-neutral measure takes precedence in pricing financial derivatives. However, the real-world measure remains valuable and indispensable across various domains. It plays an important role in risk management and asset-liability applications, facilitating comprehensive evaluation and mitigation of risks.

Real-world measures are useful for simulation-based analyses of trading and investment strategies, offering insights into the practical implications of decisions in complex market environments. Reference [2] undertakes the calibration of stochastic volatility models as a means to estimate the real-world measure.

Employing the efficient method of moments (EMM), the authors perform calibration on the Heston and Bates SVJ models. Subsequently, the calibrated models are used to explore and analyze the risk and returns associated with volatility-target strategies.

Findings

-The study shows how a real-world stochastic volatility model can be applied to test a simple volatility targeting strategy.

-The results suggest that both stochastic volatility and jumps are required to characterize equity returns.

-The results indicate that volatility targeting reduces the likelihood of extreme returns and lowers the volatility of volatility.

-The study finds that portfolio risk and return both increase as the volatility target increases.

-The 10% volatility target produces the lowest risk, measured by both the mean of volatility and the volatility of volatility, but also the lowest return.

-An equity-only strategy produces the highest risk and the highest expected return.

-The study states that volatility targeting provides an effective way to manage portfolio downside risk while limiting upside potential.

This article serves to exemplify the practical utility of the real-world measure by demonstrating its application in assessing investment strategies. Specifically, the study underscores the effectiveness of volatility targeting as a strategic approach that empowers investors to effectively manage and mitigate the downside risk inherent in portfolio management.

Reference

[2] Alexis Levendis and Eben Mare, On the calibration of stochastic volatility models to estimate the real-world measure used in option pricing, Orion, Volume 39(1), pp. 65 – 91

Closing Thoughts

Both papers highlight the practical importance of stochastic volatility models in real-world applications. While the first study extends the classical Black-Scholes framework by incorporating momentum into a Heston-type stochastic volatility model, the second demonstrates how stochastic volatility models calibrated under the real-world measure can be used to implement a practical volatility targeting strategy. Together, they illustrate how stochastic volatility models continue to evolve beyond theoretical option pricing and provide useful tools for derivative valuation and portfolio risk management.