To define a stock, we must understand ownership. Investors buy a stock to represent said ownership in a company. A company issues stock to raise money and investments. Once an investor holds a stock, he is called a shareholder because he shares in the company’s profits. The stock market is unstable but otherwise dynamic and nonlinear; thus, predicting stock prices is not air. Many factors play a part in said game, such as political, economic, and social factors.
However, this means that there is a lot of data to analyze, which paved the way for the concept of algorithmic trading. This article will explore the traditional quantitative finance methodology and machine learning algorithms used in predicting stock prices.
What is a stock market?
It is hard to define. When you buy stock online, you usually buy it through the stock market, accessible to everyone with a brokerage account, Robo-advisor, or employee retirement plan. You don’t become an investor once you buy from a stock market. It is regulated by the U.S. Securities and Exchange Commission. Even though it is hard to define, the stock market’s concept is straightforward. It let buyers and sellers make trades. Companies list shares of their stocks through the IPO, and investors purchase them, allowing the company to grow its business. Later, investors can buy or sell the stocks among themselves, where the exchange track each trade.
Two ways to predict market performance
Momentum: “Don’t fight the tape.” It means that the investors do not have to get in the way of the market trends and fight it. Instead, they should continue in the same direction as the trend. The stock has a “momentum,” where a study has shown that a stock that has been underperforming for a while is expected to perform well after a couple of years, and vice versa.
Martingales: In this possibility, the past doesn’t matter. In 1965, Paul Samuelson had studied the market and shown that past pricing doesn’t affect future ones. A martingale is a mathematical series where your best guess is your current number. If you have $50 and you bet it on a coin toss. You could have $100 or $0, but your best prediction is $50, the same number you had first. The same goes for stock, according to the martingale theory, the current price of the stock is the most important input. If the stock’s returns are random, the best prediction would be today’s stock, rather than focusing on past momentum.
Stock analysis: fundamental analysis
A fundamental analysis measures the stock intrinsic’s value by studying any macroeconomic factor that can affect it. The end goal is to see whether the stock is undervalued or overvalued. In addition, fundamental analysts usually try to identify securities that are not correctly priced by the market. They start studying the overall economy, then the industry, before concentrating on reaching the stock fair market value. First, the analyst determines the value of the company’s share price, which in his opinion, is compared to the current market price value. If the calculated price were higher than the market price, the analyst would publish an overweight rating for the stock and recommend it to the investors. If the calculated price was lower than the market price, the stock is considered overvalued, and the analyst’s underweight recommendation would be issued to the investor. Investors who follow those recommendations are expected to have a rise in the stock over time.
Stock analysis: Technical analysis
Technical analysis is a trading discipline that focuses on price and volume. Technical analysis is used to generate short-term memory systems, which helps the analysts in improving their estimation. It can be used on any security with historical data like stocks, commodities, currencies, fixed incomes, etc. In this article, we will be analyzing a stock.
Technical analysis operates knowing that the past trading activity and security price changes can be an influential factor in its future prices. Technical analysts have developed many systems to help them in forecasting the price movement. We will be talking about Long Short-Term Memory Network.
What are Long Short-Term Memory Networks?
Long Short-Term Memory Networks (LTSM) is a complex type of deep learning. They are a type of recurrent neural network that is capable of learning order dependence. A recurrent neural network can store information for an arbitrary duration, is resistant to noise, and is trainable. Unfortunately, though, recurrent neural networks can access context that should be learned before. LTSM solves this problem. Thus, LTSM can solve thousands of unsolvable tasks, including stock prediction, because they can capture long-term temporal dependencies.