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Guide Stock Market Trading : The Event Driven Method

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Suppose a big institutional investor in hedge funds , mutual funds , and insurance companies zeroes in on a stock that it owns and begins selling it off. As the large investor dumps the stock onto the market, the price will naturally begin to take a nosedive.

Stock Market Trading: The Event Driven Method

Other investors might start to panic, and then begin to unload the stock as well. As a result, the stock's popularity, and of course price, continues to fall. At some point, the institutional investor decides that it's time to jump back into the market and it begins an aggressive buying program to acquire new shares of a given stock.

Soon other investors notice that the stock's price has begun to rise again, and they also begin to buy up the stock so they can ride the price up and make a profit. This demand continues to push the price up higher.

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The cycle might begin again when the price hits a sufficiently high price, and it often does. Large institutional investors, because of their huge purchasing power, have the ability to drive prices down by selling off large positions in a given stock, and then buying back into the stock at a significantly lower price.


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Then, these large investors ride that price up as others join the rally, and then pocket a hefty profit as a result. This is called the slingshot effect and it was well described in a much-quoted article by Jason Schwarz back in In the article, he referred specifically to Apple stock.


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The shuffiling of data can create a very large mistake and untraceble. Consider we have news that are similar in contex but the language of news are slightly different and the got separated in training and the cross validation set. Then the error in cross validation set get biased as the model is already trained against that model so that example will be of no use and effective cross validation set reduces.

To use the model.

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Event-Driven Strategy Definition

Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price m….

Python Branch: master New pull request. Search Code Find File.

This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1 visual surveillance of market performance, and 2 behavior-driven visual analysis of trading networks. In the first stage, we use a 3D treemaps to monitor the real-time stock market performance and to identify a particular stock that produced an unusual trading pattern.


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  3. Beatrice Beaufort and the Serpenti Quest.
  4. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social or trading network, analysts may finally identify the attackers the sources of the fraud , and further attack plans. Published in: 13th International Conference Information Visualisation.