Month: February 2026

Do Options Exhibit Momentum?

Momentum has been studied extensively across equities, commodities, and other asset classes, with well-documented evidence of cross-sectional and time-series continuation effects. More recently, an emerging line of research has shifted attention to momentum in option returns, examining whether derivative markets exhibit their own systematic return patterns.

In this post, we review the latest evidence on option return momentum across both monthly and intraday horizons and assess the economic mechanisms that may explain these persistent dynamics.

Momentum in the Option Market

In the financial market, momentum is the tendency for assets to continue moving in the same direction. It is a reflection of the underlying strength or weakness of an asset’s price action and can be used to identify trends. Momentum is one of the most pervasive market phenomena and can be observed in nearly all stock markets around the world.

Does this anomaly exist in other asset classes?

Reference [1] studied momentum in the options market. It examined the returns of delta-neutral straddles on individual equities.

Findings

-The study finds strong evidence of momentum in option returns, as options that performed well over the previous 6 to 36 months tend to generate high returns in the subsequent month.

-Momentum is observed under both cross-sectional and time-series definitions of past performance.

-The strategy is profitable across all five-year subsamples and carries significantly lower risk than short straddle positions on the S&P 500 Index or individual stocks.

-There is no evidence of momentum crashes in option returns, although the sample length may limit detection of such events.

-The authors find limited evidence of short-term cross-sectional reversal, where options that outperform in one month may underperform in the following month.

-There is no evidence of long-run reversal in option returns, in contrast to equities, and momentum persists even at 2- to 3-year horizons.

-Option momentum differs from stock momentum because the results are based on delta-hedged positions and remain robust after controlling for stock momentum effects.

-Momentum profits are unaffected by controls for implied versus historical volatility and other option characteristics, and remain significant after factor risk adjustments.

-The study shows that high historical-return options significantly outperform low-return options across multiple horizons, including when using out-of-the-money options or delta-hedged returns.

In short, like in equities, options also exhibit momentum. The options momentum is mean-reverting in the short term and trending in the long term.

Reference

[1] Heston, Steven L. and Jones, Christopher S. and Khorram, Mehdi and Li, Shuaiqi and Mo, Haitao, Option Momentum (2022), SSRN 4113680

Momentum in the Option Market, Intraday Case

While the previous article examines momentum in option returns across monthly horizons, Reference [2] extends this line of research by focusing on intraday option return dynamics.

Findings

-The paper documents novel seasonal patterns in intraday returns of individual stock option straddles.

-Despite being delta-neutral, straddle returns exhibit the same persistent intraday seasonality as their underlying stocks.

-Returns in a given half-hour interval predict returns in the same interval on the following trading day.

-This continuation effect is strongest at the market open and close, referred to as morning and afternoon momentum.

-Morning momentum is attributed to investors’ underreaction to volatility shocks.

-Afternoon momentum is driven by persistent inventory management behavior by option market makers.

In summary, it was shown that a straddle’s return during a particular 30-minute trading interval today positively predicts its return during the same interval on subsequent days. Morning momentum reflects a continued under-reaction to overnight volatility news. Afternoon momentum, on the other hand, is attributed to persistent price pressure caused by inventory management from option market makers.

Reference

[2] Da, Zhi and Goyenko, Ruslan and Zhang, Chengyu, Intraday Option Return: A Tale of Two Momentum (2024), SSRN 5018430

Closing Thoughts

Taken together, these studies show that option markets exhibit systematic return patterns across both monthly and intraday horizons. Momentum persists over 6 to 36 months without the long-run reversals observed in equities, while intraday straddle returns display predictable continuation at the market open and close.

The evidence suggests that option return dynamics are driven by distinct forces, including behavioral underreaction, inventory management by market makers, and structural features of volatility trading. Collectively, these findings reinforce the view that options are not merely derivatives of stocks, but markets with their own persistent and economically meaningful return patterns.

Herding in Commodities and Cryptocurrencies

Herding behavior has been extensively studied and is well understood in equity markets, but far less so in other asset classes such as commodities and cryptocurrencies. In this post, we explore key aspects of herding behavior in crypto and commodity markets.

Investor Behavior in Crypto During Geopolitical Shocks

Herd behavior refers to the tendency of investors to follow the actions of a larger group, often ignoring their own analysis or information. This collective movement can lead to asset bubbles during bull markets and sharp sell-offs during downturns. Understanding herd behavior is essential for identifying potential mispricings and avoiding emotionally driven decisions.

Herding behavior has been well studied in the equity markets, but less so in the cryptocurrency market. One might expect stronger herding in crypto due to the prevalence of young, inexperienced traders and the fact that crypto markets are under-regulated, less transparent, and highly volatile. However, existing studies have produced inconclusive results.

Reference [1] extends the research on herding in the crypto space by examining behavior during major geopolitical events, such as the COVID-19 pandemic and the Russia–Ukraine war.

Findings

-The study finds strong evidence of market-wide herding behavior in cryptocurrency markets by analyzing the relationship between return dispersion and market returns.

-Geopolitical risk (GPR) significantly amplifies herding, with severe herding detected across nearly all model specifications.

-The GPR Threat index has a stronger impact on herding than the GPR Act index, indicating that perceived geopolitical threats matter more than realized events.

-Herding behavior is asymmetric, occurring more intensely during bearish market conditions than bullish ones.

-Imitative trading is particularly pronounced during periods of market stress, confirming the presence of asymmetric herding.

-The strongest herding effects are observed during extreme geopolitical and global events, notably the COVID-19 pandemic and the Russia–Ukraine war.

-The findings suggest that herding in cryptocurrency markets is largely intentional, reflecting low information symmetry, weak disclosure, and limited information quality.

-Actual geopolitical events (GPR Act) tend to lose explanatory power because market participants rapidly process and price in the information once it is released.

-When realized geopolitical shocks exceed investor expectations, uncertainty rises sharply and herding intensifies.

In short, the authors found that herding intensifies during such events and is clearly present throughout these periods.

Reference

[1] Phasin Wanidwaranan, Jutamas Wongkantarakorn, Chaiyuth Padungsaksawasdi, Geopolitical risk, herd behavior, and cryptocurrency market, The North American Journal of Economics and Finance Volume 80, September 2025, 102487

Does Herding Behavior Exist in the Commodity Markets?

Herding behavior has been shown to exist in equity markets. Reference [2] examines the herding behavior in the commodity markets.

Findings

-The study investigates herding behavior in commodity ETFs using high-frequency microstructure data and a GARCH model that incorporates cross-sectional and market volatility at 15-, 30-, 45-, and 60-minute intervals.

-During periods of market instability and the COVID-19 pandemic, agricultural and metal-based ETFs generally exhibit weaker herding behavior, while energy-based ETFs tend to herd more.

-Under normal market conditions, herding typically emerges at frequencies longer than 30 minutes.

-Broad basket commodity ETFs and energy-based ETFs display herding behavior across multiple frequencies rather than at a single time scale.

-A notable exception is agricultural ETFs during the COVID-19 pandemic, where herding is observed across all frequencies, representing a key and unusual finding.

-Correlation analysis shows that commodity ETFs become less correlated with each other as time progresses.

-Lower observation frequencies are associated with weaker correlations across ETFs, except in the energy sector.

-The results suggest that herding behavior varies significantly by commodity type, market regime, and observation frequency.

The findings provide insights for investors, economists, and policymakers, particularly for designing diversification, hedging strategies and mitigating risks such as asset price bubbles and financial instability.

Reference

[2] Ah Mand, Abdollah and Sifat, Imtiaz and Ang, Wei Kee and Choo, Jian Jing, Herding Behavior in Commodity Markets. SSRN 4502804

Closing Thoughts

Taken together, these two studies show that herding behavior extends well beyond equity markets and plays a meaningful role in both cryptocurrencies and commodity ETFs, particularly under stress. In crypto markets, herding is strongly amplified by geopolitical risk, bearish conditions, and extreme events. In commodity ETFs, herding is more nuanced and highly dependent on asset class, market regime, and trading frequency, with energy and broad commodity baskets exhibiting persistent herding, while agricultural and metal ETFs remain relatively resilient except during extreme volatility.

Overall, the evidence suggests that herding is regime-dependent, frequency-specific, and asset-class-specific, with important implications for risk management, diversification, and the design of trading and hedging strategies during periods of market stress.