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).

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The post 3 Ways Active Traders Can Profit Using Social Media Analytics appeared first on Social Alpha.

This in itself isn’t extraordinary, but the problem was that the earnings call wasn’t supposed to take place for about another hour. Rumors swirled that this information had somehow been “leaked” and Twitter’s share price dipped about 5% until a trading halt was placed on the stock in order to determine what had happened. When the halt was lifted a little less than 20 minutes later, the results were staggering: TWTR had fallen by over 20% in the course of a few minutes.

How did something like this happen? Despite early claims of a hack or leak, it instead turned out just to be a case of misplaced information, as Selerity later clarified:

Since then, the Internet has been inundated with many different stories about why this happened and what it means for Twitter. While the fact that Twitter’s stock price was hurt by a flood of information broadcast on Social Media is an ironic twist, the more important point to focus on is that this entire story played out in real-time on Twitter. Tools that can quickly identify market trends via social media and reliably turn that information into actionable intelligence are extremely valuable in situations like these.

Here is a chart that tracked the social sentiment around TWTR as the news began to develop:

Social Sentiment Declines Sharply After the Initial Tweet

Social sentiment tracking utilities were able to filter through the noise of Social Media at large and pick up this negative trend well before the information would have been made available by any traditional news sources. As noted above, once the news became more widespread and Twitter requested that trading of their stock be halted, 12 minutes had already elapsed since the swell of negative social sentiment was identified. Being able to quickly find, process, and react to this type of information is invaluable during these few otherwise uncertain minutes.

Another measure that can be useful in identifying actionable events is the amount of buzz a given security is getting on Social Media. Are people mentioning this stock more than usual? How does the current amount of activity compare to the relative baseline of activity for this security? In this case, Social Alpha’s volatility indicators picked up on the huge increase in activity around TWTR at this time:

Buzz Around TWTR Nearly Triples in a Few Minutes

The number of mentions that TWTR received on their own platform almost tripled in the span of a couple minutes, more than enough of a shift to set off some alarm bells.

The final interesting aspect here is just how accurately social sentiment can be as a leading indicator of stock price:

Price of TWTR Stock Followed the Trend in Social Sentiment

As can be seen in the chart above, sentiment begins to dive, and the price soon follows. Also noticeable is that after trading resumed and the stock price plummeted, sentiment then took a slight positive turn, and after a few minutes, the price rebounded a bit.

For one thing, this story shows why it’s important to protect information in a world where Social Media has dramatically increased the speed with which that data can be shared. But equally as importantly it shows how, using the right tools, the power of social media can be harnessed to provide insight and risk management during those crucial few minutes when a story is still breaking.

The post TWTR Tweets Tell the Tale appeared first on Social Alpha.

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.

The post Capitalizing on Rumors in Social Media appeared first on Social Alpha.

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.

The post How Twitter Predicted Apple Stock Movement Before Its Product Announcements appeared first on Social Alpha.