8 AI for financial research
This chapter covers
- The challenges of using machine learning to predict stock prices
- Research investments with LLMs
- Submitting prompts to LLM with Python
- Using prompt engineering to improve our queries
Let’s start this chapter with a radical idea. What if we stopped using code for financial research and relied on generative AI chatbots for investment advice? We can ask a large language model(LLM) questions such as “I am a risk-averse investor, and I want to invest in stocks in the Information Technology sector. What are three stocks with maximized returns and minimized risks?” Would you buy the three recommended stocks without additional due diligence?
This chapter examines the discriminative applications of machine learning in finance in the first part. We will highlight the limitations of linear regression and similar algorithms in chaotic systems, such as stock prices, which often lack patterns and are typically characterized by “random walks.” We will also outline how these algorithms can be used meaningfully in more isolated scenarios.