What’s a Good Measure of Synthetic Data Accuracy?
The short answer: it depends on the application. The long answer: it’s nuanced. At Ahead Innovation Laboratories, we believe there’s a pressing need for clarity on this topic, particularly to dispel common misconceptions.
Rethinking Accuracy
Traditionally, accuracy is a relatively straightforward concept—measured by comparing an algorithm’s output to ground truth data. When outputs closely match the real-world data, we consider the algorithm precise. While technical nuances exist, the concept itself is intuitive.
But generative models rewrite the rules. Their purpose is to create data that resembles real-world examples without copying them. With no "ground truth" to reference, how can we quantify if synthetic data truly “looks real”?
Measuring “Realistic” in Synthetic Data
A common approach is to evaluate synthetic data against collective properties and real-world constraints. Consider video generation for autonomous driving systems: the synthetic data must reflect the physical laws governing real-world dynamics. If not, a self-driving algorithm might learn flawed object kinematics, leading to disastrous real-world decisions.
In finance, where real-world dynamics are less understood, the challenge is even greater. Some market patterns are well-documented and can validate synthetic data, but much of the field is still being researched. This complexity limits the use of frameworks like RLHF (Reinforcement Learning with Human Feedback), where even humans struggle to distinguish real from synthetic data.
The Downstream Task Defines Accuracy
Ultimately, accuracy depends on how synthetic data supports downstream applications. For example, in trading, synthetic data must replicate patterns that trading algorithms use to make informed decisions. This requires more than superficial realism—it demands the reproduction of cause-and-effect relationships observed in real-world data.
Often, Generative Adversarial Networks (GANs) are seen as a silver bullet. But their effectiveness hinges on the choice of a loss function, which determines how realism is defined. Without a robust heuristic to guide this choice, GANs can fail to deliver usable results for specific tasks.
Generative AI in Finance
Most existing synthetic data generation methods in finance lack the flexibility needed for real-world applications, remaining confined to academic research. Generative AI, with its data-driven approach, offers a promising alternative. However, success hinges on close collaboration between synthetic data providers and end users to validate models effectively.
At Ahead Innovation Laboratories, we are developing frameworks that allow users to validate synthetic data and predictive models in unison. By deepening our understanding of financial market dynamics, we aim to unlock synthetic data’s full potential.
Recommended Reading
For more insights, we recommend the recent paper Data Dreamer, which offers valuable lessons from adjacent fields that can benefit quantitative finance. Explore the paper and references here.
Ready to join the conversation? Let’s work together to push the boundaries of synthetic data.
