The stock market refers to a marketplace where buyers and sellers trade shares of publicly listed companies. It is a centralized platform where investors can buy or sell stocks, which represent partial ownership in a company. The stock market provides a means for companies to raise capital by issuing shares to the public and allows investors to participate in the company's growth and profitability. The stock market operates through exchanges, such as the New York Stock Exchange (NYSE) or the Nasdaq, where stocks are bought and sold. These exchanges serve as intermediaries, facilitating the trading of stocks between buyers and sellers. Investors can place orders to buy or sell stocks through brokerage firms that are members of these exchanges. Stock prices in the market are determined by supply and demand dynamics.
Through Pattern recognition, Machine learning models can be trained to recognize complex patterns and relationships in historical stock price data. By analyzing large datasets, these models can identify recurring patterns, correlations, and trends that may not be apparent to human analysts. This enables them to make predictions or generate trading signals based on historical patterns.
In this notebook, our primary goal is to use three types of machine learning models including time forecasting model, ARIMA (Auto-regressive integrated moving average), LSTM (long short term memory) model and, in order to predict the future stock prices of the tech giant Apple Inc.
According to Apple's latest financial reports and stock price the company's current number of shares outstanding is 15,787,154,000 (17.52 billion shares). The market capitalization of Apple as of now is over $2.76 trilliion dollars. We now use machine learning models to predict future stock prices of Apple and look over how Apple grew as a company since it went public back in 1980.
