| dc.description.abstract | Nowadays, a plethora of financial news related to different businesses and stocks are available through various sources. Market movements directly or indirectly get affected by various factors such as economic, political, etc. All of this information is captured in financial news and hence analyzing news can be very much helpful in stock market trend prediction. The retrospective analysis aims to find the relationship between financial news articles and the NIFTY 50 curve movement over the past few years. Understanding this relationship helps the system in detecting whether the given news article will affect the future NIFTY 50 curve or not. And if it does, the system can tell whether the effect on the NIFTY 50 curve is negative or positive. This kind of analysis helps traders in making an informed decision without manually going through all sorts of available news. In analyzing textual data, several natural language processing techniques have been used. As the dataset used for this work is not labeled, two automatic data labeling techniques have been used. SVM and Deep learning methods are employed for the news classification task and these experiments are performed on both the datasets labeled using two different techniques. | |
