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Practical Applications Using Event Studies and News Analytics | Event Study Tools

By now you are saying to your self, I know that. Well could you have known that the stock was going up without a tip from some one? Even with that tip, could you have analyzed the stock to see for your self?

If not, then this book is for you. Its main purpose is to enable you to find, analyze, purchase, and sell stocks on your own while making a hansom profit along the way. With this book you will be able to find main events in a stock's history right up to the current time to let you know if this is a stock to purchase and when, or to leave it alone.

Geary Hooper resides in Texas, with his wife and four children.

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Using this approach you can take the guess work out of trading in the stock market. Sign in.

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Hidden fields. The learning process is based on data, past experience, and observations. The more data the computer processes, the better it becomes in the conclusions it makes. The trading process has evolved massively, to a state where traders employ sophisticated parameters and combinations of factors to come up with a decision. From social sentiment scores, through technical indicators, to fundamental information — investing today is more complicated than ever.

Machine learning has the potential to ease the whole process by analyzing large chunks of data, spotting significant patterns and generating a single output that navigates traders towards a particular decision based on predicted asset prices.

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  • How Does it Work in Practice? In their core, financial markets tend to be unpredictable and even illogical, just like the outcome of the Brexit vote or the last US elections. Due to these characteristics, financial data should be deemed to possess a rather chaotic structure[1] which often makes it hard to find sustainable patterns.

    In order to solve this, the algorithm should be fed with as much unbiased information as possible. Modeling chaotic structures requires machine learning algorithms capable of finding hidden laws within the data structure and predict how they will affect it in the future. Deep learning can deal with complex structures easily and extract relationships that further increase the accuracy of the generated results. The way machine learning in stock trading works does not differ much from the approach human analysts usually employ.

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    • The first step is to organize the data set for the preferred instrument. It is then divided into two main groups — a training set and a test set. Why is that? Before the algorithm is tested, it needs to be trained and fine-tuned which is what the training set serves for.

      2.Model Component

      After it becomes clear that the algorithm fits all requirements, it is then put into action with the test set. After the algorithm generates a result, it is then compared to the real-life performance of the particular stock. There are plenty of ways to build a predictive algorithm. However, most of them usually follow the logic presented below as it is an easy and efficient way for basic stock market predictions:. As we have already mentioned, financial markets are chaotic structures.

      And chaotic processes have proved that past events can have a massive influence on the present and the future.

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      This makes historical data a good source for predicting future prices of instruments. First of all, the trader has to figure out which instruments interest him and download and prepare the respective historical data in a time series format. Next, the trader should choose a benchmark, so that he can compare the algorithm results with its performance. Others rely on the myriad of different technical indicators there are.

      Stock Market Trading the Event Driven Method

      I like moving averages. I must have looked at or written about a hundred different systems over the years, but the most effective I have found is simple trend-following using moving averages. I stress that it is the most effective method for me. You will have your own methods that better suit your intellect, psychology, circumstances and emotions.

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      I outline my method here. I take the and nine-week moving averages.

      When they are sloping up and the price is above, I am long. When they are sloping down and the price is below, I am short. The message is not great. But now the moving averages have turned down. The signal is currently saying do nothing. The market is obviously trending down. You want to sell.