Synthetic Data in Investment Management
The investment management industry depends increasingly on timely and high-quality data to drive investment decisions. Yet firms regularly encounter challenges around both data quality and data quantity, such as lack of historical data, costly data collection, data imbalances, and privacy concerns. Synthetic data, which is data that has been artificially generated to replicate the statistical properties of real data, offers a potential solution to these challenges.
The Data Dilemma in Investment Management
“Synthetic Data in Investment Management” discusses the potential of synthetic data in investment management. The report focuses on generative AI approaches to synthetic data generation, including variational autoencoders, generative adversarial networks, diffusion models, and large language models. Unlike more-traditional methods, such as Monte Carlo simulation and bootstrapping, these generative techniques are better suited to modeling the complexities of real-world data and are capable of generating data modalities frequently encountered in finance, such as time-series, tabular, and textual data.
[....]
