Improved Volatility Prediction and Trading Using StockTwits Sentiment Data

18 Pages Posted: 29 Feb 2020

See all articles by Shradha Berry

Shradha Berry

OptiRisk Systems

Gautam Mitra

OptiRisk Systems

Zryan Sadik

OptiRisk Systems

Date Written: November 15, 2019

Abstract

Volatility prediction plays an important role in the financial domain. The GARCH family of prediction models is very popular and efficient in using past returns to forecast volatility. It has also been observed that news, scheduled and unscheduled, have an impact on return volatility of assets. An enhanced GARCH model, called News Augmented GARCH (NAGARCH) includes an additional component for news sentiment. With the rise in popularity of the world wide web and social media, it has become a rich source for opinions and sentiments. Twitter is one such platform. It is a micro-blogging site and a popular source for public view on different topics. StockTwits is a social media platform that started as an application built using Twitter’s API. It has since grown into an independent financial social media platform for news and sentiment. StockTwits is a rich source of opinions from subject experts and analysts. This data provides first systematic exploration of social media. It reflects raw sentiments of traders, investors, media, public companies, and investment professionals as opposed to sentiments from curated news wires. This research attempts to determine if the sentiment on stocks from StockTwits micro-blogs can improve volatility prediction. The experiment is performed on 9 NASDAQ100 stocks. The GARCH model with stock returns, and the NA-GARCH model with stock returns and micro-blog sentiment are tuned and their prediction results are evaluated. NA-GARCH, with the sentiment data from StockTwits performed better than the GARCH model in 7 out of the 9 cases.

Keywords: Volatility prediction, GARCH, NA-GARCH, Sentiment, Micro-blog, StockTwits

Suggested Citation

Berry, Shradha and Mitra, Gautam and Sadik, Zryan, Improved Volatility Prediction and Trading Using StockTwits Sentiment Data (November 15, 2019). Available at SSRN: https://ssrn.com/abstract=3527557 or http://dx.doi.org/10.2139/ssrn.3527557

Shradha Berry (Contact Author)

OptiRisk Systems ( email )

The Atrium, Suites 536 & 537
1 Harefield Road
Uxbridge, UB8 1EX
United Kingdom

Gautam Mitra

OptiRisk Systems ( email )

The Atrium, Suites 536 & 537
1 Harefield Road
Uxbridge, UB8 1EX
United Kingdom

Zryan Sadik

OptiRisk Systems ( email )

The Atrium, Suites 536 & 537
1 Harefield Road
Uxbridge, UB8 1EX
United Kingdom

HOME PAGE: http://www.optirisk-systems.com

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