Stock prices are influenced by investors who buy and sell actively, traders with particular expertise (e.g., value investors or short-term traders), hedge fund managers, and technological advances (e.g., hedge funds or trading algorithms). Their investment decisions are heavily influenced by their time horizons, trading strategies, research methods and capital markets processes. Each group’s sentiments towards shares can significantly impact the financial markets.
The application of sentiment analysis to financial markets demonstrates how sophisticated machine learning algorithms can assess and predict market trends using natural language processing. This can help financial service providers build forecasts based on consumer behavior for strategic planning and policy making purposes. Therefore, sentiment analysis has become more important to financial markets and can be used as a complementary approach to conventional forecasting tools. Sentiment analysis uncovers how groups of investors think about the market performance, streamlines information data and assists firms in their crisis management and marketing efforts.
Systems powered by artificial intelligence and advanced machine learning algorithms provide important tools to financial service providers, helping them make informed decisions regarding bank management, capacity planning, customer targeting/retention, risk management, sales campaigns and customer service enhancements. As technology allows companies to store and analyze large quantities of data with ease, sentiment analysis technology helps financial institutions fine-tune their customer relationship management (CRM) efforts to strengthen customer engagement and grow profits.
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