A case study demonstrating how Large Quantitative Models (LQMs) within a VAE-GAN framework can generate missing market data to outperform standard benchmarks.
Developing markets like the JSE lack deep options data. This "blind spot" prevents sophisticated analysis of implied volatility and sentiment, hindering optimal portfolio weighting.
FinanceGPT deployed a Variational Autoencoder Generative Adversarial Network (VAE-GAN) to "imagine" mathematically accurate synthetic options chains where none existed.
Portfolios utilizing synthetic data achieved a 50.48% return compared to 42.46% for the baseline, proving the value of generative financial data.
The study conducted two rigorous backtests. The "Blind Portfolio" relied on standard market data. The "Synthetic Portfolio" was augmented with Al-generated implied volatility data produced by FinanceGPT's LQM.
"This improvement is attributed to better stock selection and dynamic weighting enabled by the inclusion of implied volatility and market sentiment."
How FinanceGPT generates accurate financial data from noise.
The model ingests sparse historical data from the JSE and correlated global indices.
The Variational Autoencoder creates probabilistic "dreams" of what the missing options chains should look like.
The Adversarial Network critiques the data against strict mathematical constraints (e.g., Put-Call Parity) to ensure realism.