Understanding AI and Machine Learning in Quantitative Investment

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Artificial intelligence (AI) is no longer just a concept from science fiction—it's a vital tool that is reshaping the financial landscape. In the world of quantitative investing, managers are increasingly discussing the role of cutting-edge technologies. But what do terms like AI, machine learning (ML), neural networks, and deep learning mean, and how are they applied in both daily life and investment decision-making? This article will introduce these concepts in a straightforward, accessible way.

Let’s imagine Xiao Yan wants to buy a car. After some research, he notices a pattern: the price of a particular car decreases as it ages. For instance, a new car might cost $20,000, a one-year-old version of the same car $17,000, and a two-year-old car $14,000, with the price decreasing by $3,000 per year. Xiao Yan has identified a linear relationship between car age and price.

However, this insight alone isn’t enough to pick the best car, as factors like origin, fuel efficiency, brand, and parts add non-linear complexity. This is where computers come into play—feeding them this data enables machines to analyze the relationships between these factors.

Now that we’ve touched on the purpose of AI, let’s clarify some key concepts:

1. Artificial Intelligence (AI): AI refers to machines simulating human thought processes. It’s a broad concept, similar to fields like “medicine” or “mathematics.”

2. Machine Learning (ML): A subset of AI, ML focuses on teaching machines to learn from data, allowing them to make decisions or predictions.

3. Neural Networks: This type of machine learning model mimics the way the human brain works, using interconnected “neurons” to process information and make decisions. These networks are foundational to deep learning.

4. Deep Learning: A method for building and training neural networks. Since the early 2010s, deep learning has transformed AI applications, such as image classification.

With these definitions in hand, you’re now better equipped to understand how these concepts play out in the world of quant finance.

How Machine Learning Powers Quantitative Investment

Machine learning can rapidly process vast amounts of information, learning from data patterns to make informed decisions. But how exactly does this process work?

1.Data: The foundation of any ML model is data. The accuracy of a model’s predictions depends on the volume and quality of the data used. In finance, standardized databases and APIs provide access to critical information, which can be supplemented with data scraped from websites or apps.

2. Feature Extraction: Raw data often contains noise and is formatted in various ways. Before training a model, key features (also known as variables or parameters) must be extracted. For instance, in car pricing, variables like origin, fuel consumption, and parts quality can be features. In quant investing, price-volume data or fundamental factors are extracted as features.

3. Learning Models: There are several approaches to solving machine learning problems, such as:

  • Supervised Learning: Involves training a model with labeled data, where the machine learns the relationship between input features and the desired outcome. This method is often used for classification tasks (e.g., identifying stocks with growth potential).

  • Unsupervised Learning: This approach involves finding hidden patterns in data without labeled outcomes. It’s commonly applied in areas like market segmentation or recommendation systems.

  • Reinforcement Learning: A more recent approach, reinforcement learning teaches models to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. It’s used in areas like algorithmic trading and autonomous driving.

ML in Quantitative Investing

In the context of quantitative investing, ML offers several advantages over traditional methods. Traditional quant models, such as factor-based approaches, often rely on the subjective experience of analysts to identify patterns in market data. These models typically assume linear relationships between variables. ML, however, excels at identifying complex, non-linear relationships in the data, allowing it to discover patterns that humans might miss.

At Jasper Capital, we’ve integrated an end-to-end machine learning framework into our quantitative investment process. This system is capable of analyzing tick-level time-series data and researching alternative factors, which are then used to enhance portfolio decisions. However, it’s important to note that ML models often deal with high levels of noise, with over 99% of market data appearing random. This makes research and data quality critical to success.

In future articles, our research experts will delve deeper into the core methodologies behind machine learning and explore the growing intersection between technology and finance.