10/22 Update: Join us and Kumesh from Accern as we walk through this research & discuss how to use negative news sentiment in trading strategies in this recorded webinar - http://bit.ly/1OL3iT5
10/16 Update: The first, original version of this research had a few errors. This is an updated version with the correct findings.
A few years back, Paul Tetlock released his influential paper More Than Words: Quantifying Language to Measure Firms' Fundamentals. One of the most startling finds from his research was that negative words in articles captured "otherwise hard-to-quantify aspects of firms' fundamentals." Even more surprising was that "that stock market prices incorporate the information embedded in negative words with a slight delay." In other words, by capitalizing on this delay, there is potential for profit in trading strategies. That being said, the markets are efficient, and much of the information found in news articles are priced in previous to the release of any news piece.
In the graph above, you can see that much of the alpha has been squeezed out previous to the release of a WSJ article with a succeeding negative drift in price. However, what if you looked at non-mainstream news sources?
There are thousands and thousands of news sites & blogs that aren't followed by the general public so what if we followed the return streams following these negative news sentiments?
Accern gathers and analyzes almost every blog and news source available on the web. They provide both
- An article sentiment score (a pure estimate of how positive or negative the article) using NLP and ML
- An impact score (the probability that a stock's price will change by more than 1% on the next trading day).
They also created multiple trading strategies using both positive and negative sentiment as trading signals. Interestingly, it looks like negative news sentiment can signal a reversal in a stock's price. Take a look at my notebook for more information including a quick summary for anyone that wants a quick skim.
I encourage you to take this research and expand upon it by filtering down securities by sector exposure or using different sentiment score limits (only negative ones are used in this study - explained in the notebook).
As always, we're looking for feedback on how we can make research like this better for you guys. Post below what you'd like to see in further notebooks and backtests.
You can get access to the data now by either cloning the notebook or going here: https://www.quantopian.com/store/accern/alphaone