Stock Market Prediction With Natural Language Machine Learning

Machines – is there anything they can’t learn? 20 years ago, the answer to that question would be very different. However, with modern processing power and deep learning tools, it seems that computers are getting quite nifty in the brainpower department. In that vein, a research group attempted to use machine learning tools to predict stock market performance, based on publicly available earnings documents. 

The team used the Azure Machine Learning Workbench to build their model, one of many tools now out in the marketplace for such work. To train their model, earnings releases were combined with stock price data before and after the announcements were made. Natural language processing was used to interpret the earnings releases, with steps taken to purify the input by removing stop words, punctuation, and other ephemera. The model then attempted to find a relationship between the language content of the releases and the following impact on the stock price.

Particularly interesting were the vocabulary issues the team faced throughout the development process. In many industries, there is a significant amount of jargon – that is, vocabulary that is highly specific to the topic in question. The team decided to work around this, by comparing stocks on an industry-by-industry basis. There’s little reason to be looking at phrases like “blood pressure medication” and “kidney stones” when you’re comparing stocks in the defence electronics industry, after all.

With a model built, the team put it to the test. Stocks were sorted into 3 bins —  low performing, middle performing, and high performing. Their most successful result was a 62% chance of predicting a low performing stock, well above the threshold for chance. This suggests that there’s plenty of scope for further improvement in this area. As with anything in the stock market space, expect development in this area to continue at a furious pace.

We’ve seen machine learning do great things before, too – even creative tasks, like naming tomatoes. 

Talking Star Trek

Speech generation and recognition have come a long way. It wasn’t that long ago that we were in a breakfast place and endured 30 minutes of a teenaged girl screaming “CALL JUSTIN TAYLOR!” into her phone repeatedly, with no results. Now speech on phones is good enough you might never use the keyboard unless you want privacy. Every time we ask Google or Siri a question and get an answer it makes us feel like we are living in Star Trek.

[Smcameron] probably feels the same way. He’s been working on a Star Trek-inspired bridge simulator called “Space Nerds in Space” for some time. He decided to test out the current state of Linux speech support by adding speech commands and response to it. You can see the results in the video below.

Continue reading “Talking Star Trek”

Quad-copter controlled with voice commands

In the video above you’ll see two of our favorite things combined, a quad-copter that is voice controlled. The robot responds to natural language so you can tell it to “take off and fly forward six feet”, rather than rely on a cryptic command set. The demonstration shows both an iPhone and a headset used as the input microphone. Language is parsed by a computer and the resulting commands sent to the four-rotor UAV.

This makes us think of the Y.T.’s robot-aided assault in Snow Crash. Perhaps our inventions strive to achieve the fiction that came before it.

[Via Bot Junkie]