According to Google Trends, the word "sentiment analyis" has been gaining steady traction over the past 5 years. Sentiment refers to the attitude expressed by an individual regarding a certain topic.
When it is applied to trading, sentiment can simply be used as a directional signal to figure out whether you should go long or short within your portfolio. The most conventional way sentiment has been applied to trading is to long stocks when there are positive information on a company and short stocks when there are negative information on a company. A typical behavioral assumption is assuming that if there are positive information on a particualar company, it will draw other traders to purchase the stock and in return increase the stock pice. Whereas, if there are negative information, it will put fear into traders and tempt them to sell their stocks which in return will decrease the stock price of that particular company.
As human, we can easily interpret the attitude of an article. However, the problem lies when we have 10 or even 1000 companies in our portfolio, most of which are being mentioned frequently in the news or social media sties. The average adult reads 300 wpm and takes approximately 2 minutes to read an article with 600 words. Within 2 minutes, 10 of your portfolio companies may have generated over 100 articles or 100,000 mentions across social media sites. It is impossible for humans to keep up with the massive data overload which is where sentiment analysis comes into play.
Bag-of-words is one of the most conventional approaches to sentiment analysis. On a very basic level, it identifies positive and negative words within an article. The sum of the positive and negative words (assuming positive words holds a weight of 1.0 and negative words holds a weight of -1.0) is your sentiment score. This score is typically normalized on a scale from 1.0 to -1.0.
Many quantitative hedge funds have been incorporating the use of sentiment analysis in their trading strategies and it is a growing trend. The total time it takes a human to read 1 article and interpret its attutude towards a certain matter, a computer can read millions of articles and identify a specificsentiment score or attitude for each.
A common trend within the quantitative trading community is combining sentiment data with other types of data sources to bring a edge to a specific strategy.
Quantitative traders are becoming more receptive to incorporating sentiment indicators into their trading models and the trend will continue in the future as more traders are actively looking for an edge in the market. Many startups are competing in this space to provide sentiment and other types of innovative analytics for quantitative traders to utilized in their strategies.
And the growth of data is steadily increasing, making it more attractive to use innovative analytics such as sentiment to generate alpha.