Introduction

The idea of the stock market is for companies to raise money by offering a piece of ownership against money, called stock shares. A person could become an investor by buying a stock share from a company and thereby participate in the financial achievements of the company. The investor has an opportunity to earn profits by dividends and through capital gains made by the company. The company profits by getting capital for business expansion.

The price of a stock share is set by demand and supply and the company’s projected performance vs present value. A company that shows good earnings potential may attract new buyers and thereby an increase in the share price. A poor looking company may attract more sellers than buyers. Since there are more sellers than buyers at the time, the stock price will fall.

Certain events could also increase the price of a stock. For example, information such as earnings reports, political events and economic news. A medical company that wants to release a new medication could get a positive news report by getting approval to sell this new medication.

Stock prices could also be affected by people mimicking actions of a larger group. For example, if more and more people buy a certain stock and push the stock price higher and higher, other people would want to do the same since they may be assuming that the other investors may know something that they don’t know.

To maximize the profit when trading stocks, the goal is to buy a stock at the lowest price possible and then sell it at its peak price.

Step 1: Pattern Recognition (day trading)

Train an AI to recognize patterns in a stock prize movement curve. The AI should recommend if there is a good opportunity to buy a stock at a certain price. It should also be able to tell when to sell the stock, by analyzing the current state of the price curve.

There are certain patterns to look for in day trading in order to analyze a stock buying opportunity. Some of them are defined in figure 2.

Step 2: Stock Market Simulation

Build a stock market simulation environment to train the AI in. Simulate stock price movement as realisticaly as possible, to prepare good and realistic data for the AI. The AI could take actions in the environment and get judged based on those actions using reinforcement learning.

Step 3: News Recognition

Design an AI to analyze news related to a stock. It should be able to determine whether a piece of news will be positive, neutral or negative for the stock price and recommend you to act accordingly. Start by training a natural language model (NLP) in order to analyze news text. Read about NLPs at: https://www.oecd.org/publications/ai-language-models-13d38f92-en.htm

For a successful thesis work, we believe that the applicants should have:

• Experience in C++, Python and/or MATLAB as well as respective machine learning libraries (TensorFlow and/or PyTorch etc…).
• Experience in machine learning is recommended."

To give you the best possible support during your thesis, we’d like you to be able to come to the office connected to the project and spend most of your time working from there.

Application:

We look forward to receiving your resume, and preferably, a personal letter in which you explain why you want to write your thesis with Syntronic.

We screen and evaluate applications on an ongoing basis. The thesis project may be filled before the application deadline.