AI in Trading with Professor Esfan

Summary

This introductory lecture explains the goal of building AI and machine learning background to apply these techniques to trading and finance. It poses questions about whether finance offers distinct challenges for AI compared to other fields, if finance qualifies as big data, and if AI can outperform classical econometrics. Additional potential obstacles like constantly evolving markets and low signal-to-noise ratios are noted. The lecture advocates a foundational approach focused on precisely defining the problem, understanding limitations of techniques, and questioning assumptions rather than jumping to solutions, to ultimately apply appropriate AI methods to trading.

Takeaways

• 😊 The course will provide an introduction to using AI techniques in trading
• 📚 It will cover the fundamentals of AI and machine learning relevant to trading
• 💡 Understanding the problem is key before applying solutions or techniques
• ❓ There are open questions around how suitable AI is for trading vs other fields
• 🔍 Finance may not have the same levels of big data as other areas that use AI
• 🤔 Using AI in trading faces challenges like constantly changing markets
• 📈 Econometrics is an existing technique - is AI better or complementary?
• 🚨 AI techniques have limitations that need to be understood
• 🤯 Hidden assumptions in models can lead to issues if not spotted
• 📚 The course takes a foundational approach to build understanding

Q & A

What are some of the key questions to ask when considering applying AI techniques to finance?

Some key questions include: Is finance a distinct field for AI applications compared to other domains like computer vision? Does finance have 'big data' like other areas where AI is applied? Are AI techniques more useful than classical econometrics in finance? How can AI techniques adapt to constantly changing and evolving markets?

What are some of the challenges AI techniques may face when applied to finance?

Some challenges AI may face in finance include: the low signal-to-noise ratio, constantly changing markets and buyer/seller behaviors, needing different techniques for different areas of finance like high frequency trading vs macro considerations, and having appropriate techniques for the constraints and assumptions of a particular finance problem.

What approach will be taken in the course for applying AI to finance?

The course will take a foundational approach focused on understanding the core concepts and mathematical foundations of techniques rather than just jumping to applications. There will be an emphasis on precisely formulating the finance problems, understanding limitations of techniques, and scrutinizing assumptions.

What role do assumptions play when applying AI techniques?

Assumptions, especially hidden ones, are very important to scrutinize when applying AI techniques. Things like distributional assumptions or smoothness assumptions of techniques must match the actual data and problem constraints, or results may be inappropriate or imprecise.

What background knowledge or skills are needed for the course?

Some mathematical maturity is required to follow the conceptual development, but no background in finance or AI is assumed. The focus will be building an understanding from basics rather than assuming domain knowledge.

Why emphasize understanding problems over just applying techniques?

Understanding problems is emphasized because solutions cannot be effectively developed without fully grasping the nuances and details of the problems. Jumping into techniques without this understanding often leads to ineffective or invalid solutions.

How can constantly evolving markets be a challenge for AI techniques?

Markets are constantly changing, with regime shifts and evolving buyer/seller behaviors. Many AI techniques make assumptions of stability, so being able to adaptively update and change with markets is an important consideration.

What are some examples of books on applying AI in finance?

The lecture mentions several book titles like 'Artificial Intelligence in Finance,' 'Deep Learning for Financial Market Prediction,' and 'Machine Learning in Business' among others focused on using AI and machine learning in finance.

What is meant by the signal-to-noise ratio in finance data?

The signal-to-noise ratio refers to the amount of useful signal versus irrelevant noise in data. Finance is said to often have a low signal-to-noise ratio, making it difficult to extract meaningful signals.

How can understanding limitations help apply AI to finance?

Understanding the limitations of each technique allows for selecting the proper methods for a given problem and sets expectations appropriately. Since no one technique excels universally, matching their strengths and weaknesses to problem constraints is key.