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There are way too many sources of -investing intelligence- out there

Posted by stockal on September 8th, 2017

When Information Alone is Not Enough

As an avid investor, we’re sure you keep up with the market movements with a lot of reading. You subscribe to several free and premium newsletters, blogs, financial columns, magazines, and consume information through books as well.

Have you sometimes wondered - “what after reading this?” or “only if there was an easier way to apply all this knowledge!” How easy has it been to turn that knowledge into cash? If you’re like most people we’ve met, you would probably reply, “not at all”.

That’s an important distinction you need to make. Reading dozens of newsletters and blogs, and manually tracking your stocks on SeekingAlpha, Fool, MorningStar, CNBC, Twitter, or StockTwits is no correlation to your ability to integrate all the info into an insight that’s profitable to you.

The enormous effort it takes to pick the gems from dirt makes it simply impossible for humans to do it well.

This job is best left to intelligent algorithms. Computers can be trained to look through a mountain of data and extract useful insights. They can quickly assign each piece information weighting based on its source and track record.

Stockal, a big-data based investment analysis tool, exactly works this way. It monitors thousands of news sources, blogs, analyst opinions, social media chatter round the clock. Using cutting edge content analysis algorithms, it can near perfectly assess the value of the piece. It then assesses all other related buzz and makes an informed decision about whether a stock’s price would go up or down.

This is direction in which stock monitoring tools are heading towards. The implementation is yoked with the knowledge. They are no longer separate. Big data-based apps, stock monitors and recommendation engines will become the new normal. Get ready for an exciting future.

Stockal offers a free trial account. Give it a whirl.


Also See: Data Based, Big Data, Weve Met, Weighting Based, Way, Reading, Knowledge

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