Degree of Irrationality: Sentiment and Implied Volatility Surface

TL;DR

This paper constructs high-frequency sentiment indicators from social media data and demonstrates their effectiveness in predicting the implied volatility surface, revealing nuanced market sentiment insights beyond panic signals.

Contribution

It introduces a novel approach combining deep learning and signal decomposition to enhance implied volatility surface prediction using high-frequency sentiment data.

Findings

High-frequency sentiment correlates strongly with ATM implied volatility.

Low-frequency sentiment is more related to DOTM implied volatility.

Sentiment-based features improve prediction accuracy of the volatility surface.

Abstract

In this study, we constructed daily high-frequency sentiment data and used the VAR method to attempt to predict the next day's implied volatility surface. We utilized 630,000 text data entries from the East Money Stock Forum from 2014 to 2023 and employed deep learning methods such as BERT and LSTM to build daily market sentiment indicators. By applying FFT and EMD methods for sentiment decomposition, we found that high-frequency sentiment had a stronger correlation with at-the-money (ATM) options' implied volatility, while low-frequency sentiment was more strongly correlated with deep out-of-the-money (DOTM) options' implied volatility. Further analysis revealed that the shape of the implied volatility surface contains richer market sentiment information beyond just market panic. We demonstrated that incorporating this sentiment information can improve the accuracy of implied volatility surface predictions.