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Using Social Media Activity as a Trading Signal

Article Quant Q&A · Author: siamii

Summary

The document surveys proposed uses of social media and online discussion data in trading. Suggested signals include investor attention, the popularity of individual stocks, shifts in discussion volume, and changes in sentiment. One cited behavioral explanation is that retail investors may be drawn to prominent stocks and news, while their selling behavior differs from their buying behavior. Traders might also treat spikes in discussion as clues about volatility or use text analysis to interpret posts and related news.

The responses express substantial caution: public claims that online mood predicts markets are not presented as settled evidence, and access to sufficiently detailed data may be limited. Posts have distinctive vocabulary and variable length, requiring normalization before text analysis. Discussion activity can reflect news, speculation, or manipulation, and a useful relationship may concern volatility rather than direction. The document names examples of research and commercial activity, but supplies no performance results or validated trading rules.

Key ideas

  • Social media activity may serve as a proxy for investor attention and interest in particular securities.
  • Discussion spikes could relate to volatility, news, or speculative activity rather than reliably predict price direction.
  • Retail investors may respond asymmetrically to popular stocks, buying attention-grabbing names but selling mainly existing holdings.
  • Text mining requires adjustments for short messages, variable length, and platform-specific vocabulary.
  • The document cautions that public evidence is limited and that data quality and access constrain research.

Tags

Full text
# Can social media be applied to algorithmic trading?


# Can social media be applied to algorithmic trading?












Can social media sites, like Twitter, be used to analyze financial markets for algorithmic trading? How much research has been done on this topic?

## Answer by Ryogi (score 7)

https://quant.stackexchange.com/a/2317

Personally, I am very skeptical of the claims in "Twitter mood predicts the stock market". There are several other papers with similar claims, but not so much good quality research is available. Arguably, the sweet bits of these approaches are not public. A sounder approach is to dig at the relationship between social media activity and relate it to the stock market. One idea is take social media as a proxy for investors' attention and exploit this channel to say something sensible about the stock market.

The typical viewpoint is that social media mining provides info on the interests of individual investors, in contrast to big investment firms. Within this framework the hypothesis of Barber and Odean (2008, RFS, "Do retail trades move markets?") makes a lot of sense: retail investors buy popular and news-making stocks, pushing prices up. Interestingly, their investing activity is asymmetric: buy news-makers, but only sell what they own - rarely on news.

Some fresh good work on the topic, based on google searches, is the paper "In search of attention" by Da, Engelberg, and Gao (2011, JF) (EDIT: this is the same work pointed out by wburzyns in his comment above).

## Answer by SRKX (score 6)

https://quant.stackexchange.com/a/2312

I assume that by "how much research" you mean "could you provide me with some links"....

So, as @Shane mentioned in its comment, a hedge fund recently started and is focusing on twitter analysis (here is another link).

From what I understand they are basically implementing a trend-following strategy based on the "mood" of twitter users (I, of course, don't know the details, but it's surely more complicated than that).

As for the links, we already discussed the subject of speech recognition in trading in this post.

## Answer by Steve Severance (score 4)

https://quant.stackexchange.com/a/2315

The original paper on using Twitter to predict the Dow can be found here. The hedge fund the authors are working with on trading is called Derwent Capital. An interview with the gentleman in charge can be found here. A quick Googling for "twitter analysis paper" turns up a number of results, although I am not going to post links as I have not reviewed them.

The search term factors for a model would be very interesting but very few people have access to the data in sufficient granularity for it to work. You could try licensing data from network providers such as Comcast or Verizon.

Most of the literature on traditional text mining will be applicable to Twitter although some work will be needed to normalize for document length and the unique vocabulary on Twitter.

## Answer by Meh (score 2)

https://quant.stackexchange.com/a/2316

The was an article about how ten years ago some option market-makers were crawling online discussions forums (like the Yahoo! stock board) to see what people talked about.

If discussion about some ticker spiked, they assumed the volatility for that ticker will increase and factored this into their pricing models. Of course, most of the time is was because of news related to that stock, but not always (think about pump'n'dump schemes).

I assume that today they are much more sophisticated about this.

## Answer by Samik R (score 2)

https://quant.stackexchange.com/a/2341

A related idea is to text mine news and use it for predicting movements of stock market. See this NYTimes article for more information.

## Answer by Suminda Sirinath S. Dharmasena (score 2)

https://quant.stackexchange.com/a/2946

There is a product called Wall Street Birds which can be used for trading. Still in invitation mode. Another sentiment based product is Piqqem

## Answer by user78654 (score 0)

https://quant.stackexchange.com/a/61012

I know some companies provide this kind of data to various financial institutions, like Sesamm, or there are other services looking at stocks which are discussed about. If they exist and make money, it means some people consider this data is valuable.

However, the questions addressed by these companies goes beyond trying to guess if a security will go up or down. Most of the time, they are "just" data providers. If, per example, a high number of discussion about a specific topic drives an increase in volatility of a security, this information is valuable as well, as some strategies based on derivatives may enable someone to make a profit out of it.

Obviously, any valuable research (if it exists) is likely to be secrectly kept by the institutions.

Aside: you may be interested in this article, which uses alternative data to predict the performance of a compay.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.