THE STRATEGIC ANALYSIS AND MACHINE LEARNING BASED STOCK PRICE FORECASTING FOR A CLIENT
Keywords:
Stock markets, Stock Brokers, Trading strategies, Market dynamics, Rational behaviors, Irrational behaviors, Machine learning, LSTM networks, ProphetAbstract
Stock markets are dynamic financial marketplaces where various products are traded among brokerage companies, requiring careful evaluation of future asset prices. Despite a wealth of data, finding meaningful patterns and creating effective trading strategies remains difficult due to complex factors like psychological and market dynamics. The interaction between rational and irrational behaviors makes stock prices unpredictable and challenging to anticipate. Machine learning shows potential in addressing these issues by revealing hidden patterns and generating insights from large datasets. Algorithms such as Regression Analysis, Prophet, and Long Short-Term Memory (LSTM) networks are essential for evaluating historical market data, identifying trends, and projecting future stock values. By using these advanced methodologies, financial experts can enhance their investment strategies and make decisions that are more informed. This improves precision and confidence in navigating the volatile stock market landscape.
