Description

In stock traffic approach introducing a new framework for automatic exploitation of messages for e.g. news without explanation events is extracted from the messages what are needed that in text. Extracted events under testing based on developing from traffic approaches on technical gauge. Developing events are exposed from side to side of genetic programming. Messages are incorporating automatically and also enhancing the predicating and validating messages. Markets are becoming more efficient and more accessible because of the use of ever faster methods for communicating and analyzing financial data. Algorithms developed in machine learning can be used to automate parts of this translation process. In other words, we can now use machine learning algorithms to analyze vast amounts of information and compile them to predict the performance of companies, stocks, or even market analysts. In financial terms, we would say that such algorithms discover inefficiencies in the current market. These discoveries can be used to make a profit and, in turn, reduce the market inefficiencies or support strategic planning processes. Our analysis of the relationship between news and the stock market, as apparent from the collected dataset is focused on discovering the influence that news have on the share price of the concerned companies, as well as on whether this influence can be captured through the extraction of events from news messages and employing a predefined impact for determining the direction of this influence on prices. Some rules are implemented over the decision of how much to trade over the course of the trend. This includes decision of how much to trade at each time, and how much money to risk in each trade. In order to minimize risk, for example, the trading size is reduced during periods of higher market volatility or losing periods. The trade is managed to let the profit runs when market is good, and holds back during bad times to preserve capital until more positive price trends reappear.