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This thesis focuses on the field of financial forecasting. Most studies that use the financial news as an input in the prediction process, take it for granted that news has an effect on financial markets. The starting point for this research is the need to question this assumption, and if confirmed, to attempt to quantify it. Therefore, the first study investigates the correlation between news and stock performance based on a dataset covering both trading data and news of 25 companies. We propose a novel framework to quantify the relationship based on two matrices of pairwise distances between companies. The first matrix represents distances between sets of news articles, while the other represents the pairwise distances between the financial performances. The detected correlation varies with time and reaches statistically significant. The next study focuses on testing if news can be used as a proxy for future financial performance in a profitable trading strategy. The one proposed here uses our previous findings to select the stock for which news affects most strongly on financial performance. The results show that this strategy outperforms competitive baselines. Based on the proposed framework, a textual feature ranking method is proposed. This method assigns weights for textual features, and those weights are optimised to maximise the value of the relation to be quantified. A gradient descent algorithm is applied to obtain the optimal weights. There are two findings: first, named entity related words are weighted more than other words. second, optimal weights lead to a significantly better indicator for selecting winner stocks hence better profitable strategies. Lastly, the popular convolutional neural work is used to implement a novel financial forecasting approach, which uses the stock chart as input. The results show that this approach can provide effective predictions of future stock price movements.