The stock of a corporation signifies ownership in the corporation. A single share of the stock represents a claim on fractional assets and earnings of the corporation in proportion to the total number of shares. For example, if an investor owns 50 shares of stock in a company that has, in total, 1,000 outstanding shares, that investor (or shareholder) would own and have claim on 5% of the company's assets and earnings.
Stocks of a company can be traded between shareholders and other parties via stock exchanges and organizations. Major stock exchanges include New York Stock Exchange, NASDAQ, London Stock Exchange Group, Shanghai Stock Exchange, and Hong Kong Stock Exchange. The prices that a stock is traded at fluctuate essentially due to the law of supply and demand. At any one moment, the supply is the number of shares that are in the hands of public investors, the demand is the number of shares investors want to buy, and the price of the stock moves up and down in order to attain and maintain equilibrium.
In general, investors want to buy low and sell high. This sounds simple enough but it's very challenging to implement as it's monumentally difficult to say whether a stock price will go up or down. There are two main streams of studies attempting to understand factors and conditions that lead to price changes or even to forecast future stock prices, fundamental analysis and technical analysis:
- Fundamental analysis: This stream focuses on underlying factors that influence a company's value and business, including overall economy and industry conditions from macro perspectives, the company's financial conditions, management, and competitors from micro perspectives.
- Technical analysis: On the other hand, this stream predicts future price movements through the statistical study of past trading activity, including price movement, volume, and market data. Predicting prices via machine learning techniques is an important topic in technical analysis nowadays. Many quant trading firms have been using machine learning to empower automated and algorithmic trading. In this chapter, we'll be working as a quantitative analyst/researcher, exploring how to predict stock prices with several typical machine learning regression algorithms.