sentiment Archives | Social Alpha

Posts

3 Ways Active Traders Can Profit Using Social Media Analytics

September 22, 2015/0 Comments/in Active Traders /by Lawrence Klamecki

One of the big questions we often hear from active stock and ETF traders is, “How can I use social media analytics to increase my profits?”

Here are three techniques you can use:

Technique #1: Event-Driven News

Major social media platforms like Twitter are now significantly larger and faster than traditional financial news sources.

You can get ahead of the news and make big profits using social media analytics to identify and trade market events ahead of the crowd.

A classic situation was Twitter’s “earnings leak” on April 28, 2015. Social analytics allowed traders to enter short trades before trading was halted.

There are billions of social media messages per day related to stocks and ETFs. Finding these market-moving messages is like finding a needle in 1,000 haystacks.

To achieve this, you need an advanced social analytics platform designed to isolate high magnitude trade-worthy messages from the mass of social media data, and deliver them to you for your trading decisions.

Technique #2: Sentiment Trends

“The trend is your friend” is as true for social media analytics as it is in traditional technical price analysis.

When the longer-term trend of social sentiment lines up with a price trend, this tends to support a trend continuation. Take a look at this example with Agnico Eagle Mines (AEM):

Similarly, if price makes a counter-trend move and social sentiment does not follow, you can use this information to “buy the dip” (uptrend) or “sell the peak” (downtrend).

Trading social sentiment trends requires social analytics that can be aggregated across different time periods to compare against intraday, daily and weekly price trends.

Technique #3: Reversals

In most markets, significant price reversals tend to occur in conjunction with high volatility and large volume. Social media is similar in this respect.

During periods of reversal, social chatter (aka “buzz”) tends to increase. The spread between bullish and bearish opinions widens as the crowd diverges on the future direction of the stock or ETF. An example of social media analytics signaling a potential price bottom is shown below with NRG Energy.

The most powerful reversals tend to occur in conjunction with surprise events, such as a merger, operational failure (e.g. oil spill) or regulatory approval (e.g. successful drug trial).

To help identify price reversals, your social analytics platform should have social volume, momentum and volatility indicators similar to traditional share volume, Relative Strength (RSI) and Average True Range (ATR).

Learn More

To learn more and get hands-on access please request a FREE TRIAL of our social analytics dashboard and real time alerts.

Capitalizing on Rumors in Social Media

January 27, 2015/0 Comments/in Social Indicator Analysis /by Matt Holtzman

Using social media as a source of breaking news has many advantages, but one that made itself readily apparent on Sunday was the ability to react to rumors. Whereas traditional news channels tend to focus on substantiated, double- and triple-fact-checked stories that are generated from official press conferences and earning reports, social media presents no such restrictions to its users. Twitter has the ability to broadcast tons of information to an extremely wide audience. In addition to being an outlet for established news sources, both professional and amateur analysts have the ability to speak whatever is on their mind. Nothing is automatically filtered and there are only minor regulations on content. While relying on unsubstantiated data can obviously be a double-edged sword, being able to process early fragments of information and make sense of them can be a huge advantage in analyzing how the market will react to breaking news.

Early Sunday evening, rumors began to swirl on Twitter that IBM was planning a massive reorganization that would result in laying off anywhere from 100,000 to 112,000 employees.

This is obviously shocking news, and if it came from an isolated, untrusted user it could almost be dismissed out of hand. However, several tweets started to become a trend. The Social Alpha analytics engine began to notice that these rumors were coming from reliable, influential sources, and social market sentiment indicators started to drop, indicating a “Bearish” outlook.

IBM social sentiment begins to drop as layoff rumors start to spread on Twitter

While this trend started around 5:30 PM on Sunday evening and quickly picked up steam on social media, the market was slower to react. The story wasn’t yet heavily reported through traditional news sources, and on a day when the market as a whole opened down, IBM’s stock opened up 2.2%. As the rumors proliferated, the stock began to mirror the trend in social sentiment and fell steadily throughout the day, losing almost all of its opening gains as it closed up only 0.3% for the day.

After a bullish open, the market catches on to the bad news and the stock slides 2% throughout the day

While lots of information about IBM is broadcast each day, in this case Social Alpha was able to identify noteworthy tweets that would have a future impact on the market, filter out the ones that weren’t important, and produce a sentiment indicator that correctly predicted the next day’s movements. This in turn presented a good opportunity: while the rest of the market was still bullish, paying attention to the social indicators on the night before the market opened would have allowed an investor to adopt a bearish outlook before everyone else adjusted.

Paying attention to rumors can be dangerous. It’s still not certain how much truth is in the IBM story, and taking everything you read on Twitter as fact is certainly not prudent. But being able to accurately filter and interpret data from all sources — particularly social media where the challenge is greatest — can present great opportunities for investment in a fast-evolving market.

How Twitter Predicted Apple Stock Movement Before Its Product Announcements

September 26, 2014/0 Comments/in Social Indicator Analysis /by Prem Melville

On September 9^th, Apple announced two new iPhones, the Apple Watch and a new Apple payment system. Shares of the company closed with a slight decline after having risen almost 5 percent, in what was a very volatile day for Apple. (See Business Insider for extended coverage of Apple’s announcements.)

But even before the markets opened, the more than usual social chatter around Apple on Twitter forecasted the upcoming volatility in the price of AAPL. The Apple stock continues to be a social media sweetheart, with more than 3,000 tweets about it on regular days. But on the morning of September 9th, there was an unusual uptick in social buzz around AAPL, leading up to a staggering 13,000 messages on Twitter about the stock, just within a 6-hour period. Intelligent anomaly-detection algorithms were able to detect this unusual trend early on; and half an hour before the market opened, messages were sent out alerting investors to the rocky day ahead for Apple.

Sophisticated Computational Linguistic techniques are able to rapidly assess the opinions of thousands of analysts and investors in the matter of nanoseconds, and thus gauge the social pulse of the market. This social market-sentiment tends to be a leading indicator of stock price movements. The significant rise and fall of sentiment around AAPL was clearly reflected in the price 6 hours later.