Why Quants Use Synthetic Data to Train Machine Learning Models Despite Access to Real Market Data

Quantitative finance professionals often generate synthetic data to train machine learning models, even when they have access to real market data. This practice is rooted in the need to enhance model performance, manage privacy and compliance, and conduct robust testing. In this article, we explore the reasons behind this choice from both quants and data scientists in marketing tech perspectives.

Why Quants Use Synthetic Data

Data Augmentation

One of the primary motivations for generating synthetic data is to augment limited or unbalanced datasets. By doing so, quants can improve the robustness and generalizability of their machine learning models. Scenarios where real data is scarce or unbalanced benefit greatly from synthetic data augmentation, ensuring that models are better equipped to handle real-world variability.

Privacy and Compliance

Real market data often contains sensitive information and is subject to regulatory restrictions. Quants can use synthetic data to train machine learning models without compromising privacy or violating regulations. This approach allows them to maintain compliance while still leveraging data for predictive modeling.

Stress Testing and Scenario Analysis

Synthetic data enables quants to simulate extreme market conditions or rare events that may not be present in historical data. This is crucial for stress testing models and assessing their performance under various scenarios. Additionally, synthetic data can be tailored to explore hypothetical scenarios, providing valuable insights for strategy development. For instance, in the context of e-commerce, synthetic data can help predict sales performance under different marketing campaign scenarios.

Control Over Features and Overfitting

Generating synthetic data allows quants to control specific features and distributions. This control is invaluable for testing hypotheses or modeling behaviors that real data may not allow. Furthermore, by creating diverse synthetic datasets, quants can help prevent overfitting to the noise present in real market data, leading to more robust models.

Improving Model Training

Synthetic data can help balance classes or fill gaps in data, leading to improved training outcomes for machine learning models. This is particularly useful in balancing imbalanced datasets and ensuring that the model is more robust to variations in the data.

Data Science Perspectives in Marketing Tech

Data scientists in marketing tech can also benefit from synthetic data, as it provides a powerful tool for enhancing model training, testing, and validation. From a marketing tech perspective, one of the first questions to consider is what is being observed and how to ensure the model's focus on the underlying behavior is maintained.

Observing Web Traffic in E-commerce

Consider the scenario of e-commerce where web traffic is key to understanding user behavior. Web traffic can be notoriously noisy, with factors such as bot traffic, non-seasonal promotions, and unforeseen marketing events all contributing to signal degradation. These factors can significantly impact models and provide false insights into the underlying behavior.

Addressing Data Quality Issues

Data quality issues such as bot traffic and non-seasonal marketing promotions can lead to inaccurate model predictions. As a data scientist, it is crucial to identify and mitigate these issues to ensure the model's performance is reliable. Synthetic data can be used to simulate ideal conditions and test the model's robustness under various scenarios.

Pilot Projects with Synthetic Data

In pilot projects aimed at forecasting consumer behavior for inventory planning, synthetic data is an invaluable tool. Instead of diving into the complex engineering tasks of building and deploying a production-ready model, synthetic data allows data scientists to focus on model selection and optimization. Synthetic data helps avoid overfitting and ensures that the chosen models are robust and reproducible.

Reproducibility and Robustness

Two critical concerns in data science projects are reproducibility and overfitting. Modelling with synthetic data addresses both these concerns. It is easy to reproduce and highly customizable, allowing other team members to develop models without worrying about access issues. Synthetic data can also be built to be as complex and sophisticated as real environments, helping to avoid overfitting and ensuring the model's performance is robust.

In conclusion, synthetic data plays a vital role in enhancing the training, testing, and validation of machine learning models in both quantitative finance and marketing tech. Whether addressing data augmentation, privacy and compliance, stress testing, or improving model training, synthetic data is a valuable asset for achieving more accurate and reliable models.