Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists

Abstract

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.JEL classification: G12, C14, C51, C58, G4 Antweiler and Frank, 2004). Whereas previous research considers low-frequency data, witnessing tremendous growth in the number of released news having shorter distances between the releases, it is tempting to ask a question if this almost "continuous" news feed can be used to measure a "continuous" changes in market sentiment. Even more interesting would then be to learn if such high-frequency news sentiment contains financially relevant information concerning stock price and volatility. To understand the evolution and dynamics of high-frequency news sentiment, we propose to use machine learning techniques to quantify sentiment from high-frequency news from the NASDAQ news platform, and we introduce a new high-frequency text-based sentiment measures to economists. Second, we propose to model the sentiment measures as a stochastic process and document how news sentiment influences volatility and price dynamics empirically by calibrating a sentiment-driven stochastic volatility model on data. Third, we provide explicit analytical moments that allow studying the non-trivial correlation of sentiment and volatility theoretically.Sentiment and its role in financial markets have been attracting the attention of researchers from the early days. Keynes (1936) argued that markets can fluctuate wildly due to investors' "animal spirits," which can move prices in a way unrelated to fundamentals. Decades later, (De Long et al., 1990) formalized the role of investor sentiment in financial markets arguing that uninformed noise traders basing their decisions on sentiment lead to noisy trading, mispricing, and excess volatility.The growing consensus that noise traders can impact stock markets in the short run is nicely surveyed by Baker and Wurgler (2006).Yet another decade later, the literature quantifying the investor sentiment and its impact on stock markets already grew into several strands. While sentiment is often measured using certain market-based variables as in Baker and Wurgler (2006), market-based measures have the drawback of being equilibrium outcome of many economic forces other than investor sentiment Da et al. (2014). In turn, one can use survey-based measures including the University of Michigan Consumer Sentiment Index, or AAII investor sentiment survey, ...