Gamma Exposure and S&P500 Return Predictability

Options trading volume has been increasing rapidly, potentially altering market dynamics. Reference [1] examines whether aggregate gamma exposure (GEX) in the S&P500 index options market contains predictive information about future equity returns and whether it can enhance short-term forecasting models. To do so, the authors construct an Autoregressive Distributed Lag (ARDL) model to predict S&P500 returns using GEX, and compare the results with models that exclude GEX and with a baseline random walk model. They pointed out,

This study set out to answer two central research questions. First, we examined whether changes in aggregated gamma exposure influence future stock market movements. Our results clearly indicate that variations in the derivative of GEX have a statistically significant relationship with subsequent returns on the S&P 500. This effect is robust across both the pre- and post-2020 subperiods, albeit with somewhat diminished strength in the latter. These findings suggest that shifts in gamma positioning among OMMs, likely driven by delta-hedging dynamics, can generate price effects that persist beyond intraday horizons and into the following trading days.

Second, we assessed whether the inclusion of GEX in a predictive modeling framework improves the forecast accuracy of S&P 500 returns. Based on out-of-sample testing, including a model comparison using Diebold-Mariano tests, we find that incorporating GEX significantly enhances the model’s forecasting performance relative to both a GEX-excluding specification and a random walk benchmark. This outcome reinforces the idea that GEX contains forward-looking informational value, which can be utilized to improve predictive modeling in equity markets.

In short, the results show that variations in GEX have a statistically significant relationship with subsequent returns, consistent across both pre- and post-2020 periods, although somewhat weaker in the latter. The paper also demonstrates that GEX contains forward-looking information useful for short-term return prediction.

The authors provide an economic interpretation, showing that dealer hedging creates mechanical directional flows, where positive GEX dampens volatility and supports returns, while negative GEX amplifies moves and is associated with risk-off conditions.

An interesting finding of the paper is that market makers’ net gamma exposure is positive most of the time.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Jonsson, G., & Nyberg, T. (2025). Convexity in Motion: Leveraging Gamma Exposure to Predict Equity Market Returns and Improve Predictive Modeling. Linköping University.

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