Forecasting the stock market has become very important in planning business activities. The prediction of stock price has driven many researches in a variety of disciplines, including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the enormous amount of online information that is available in the public domain, such as Wikipedia, the social forums, news from media, have a significant impact on the investor’s opinion towards the financial markets. The reliability of the computational models on prediction of the stock market is very important, because it is highly responsive to the economy and may result in financial losses. In this paper, we have made an extensive analysis on various stocks. First, we have performed Stock Volatility Analysis on 1000 stock dataset of NYSE. The main contributions in this paper include the development of a dictionary-based sentiment analysis model for the financial sector, and the evaluation of the model for scaling the effects of news sentiments on stocks for other markets. By using only the news sentiments, we have achieved a good accuracy of 70.59% in predicting the trends in short-term stock price movement.
Financial Analytics, Data Visualization, Predictive Analysis, Secure Transactions, Real-Time Insights, Scalable Architecture, Investment Tracking, AI-Driven Recommendations
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