Beyond Large Language Models

Large Quantitative
Models (LQMs)

While LLMs master language, LQMs master logic. We built a new class of generative AI designed specifically to understand stochasticity, non-linear relationships, and the mathematical rigor of finance.

The Limitation of LLMs

Large Language Models (like GPT-4) are probabilistic token predictors. They "hallucinate" numbers because they treat math as a linguistic problem, not a logical one. They cannot natively perform Monte Carlo simulations or understand the concept of "variance."

Unreliable for Financial Forecasting

The LQM Advantage

LQMs are pre-trained on numerical time-series data and financial formulas. They utilize a VAE-GAN architecture to generate "synthetic futures"—mathematically plausible scenarios that respect the laws of economics.

Built for Quantitative Rigor

The Engine: VAE-GAN Framework

How we generate accurate financial insights from noise.

Variational Autoencoder (VAE)

Encodes sparse historical data (e.g., JSE prices) into a latent probability distribution.

Latent Space (The "Imagination")
Generative Adversarial Network (GAN)

The Generator creates synthetic data points. The Discriminator critiques them against GAAP rules and market logic.

Handling Stochasticity

Markets are random walks. Unlike linear regression models that draw a straight line, LQMs generate a "cloud" of probable outcomes, capturing volatility and tail risks.

Synthetic Data Generation

In developing markets with limited data (like sparse options chains), LQMs can "fill in the blanks" by generating synthetic data points that are statistically indistinguishable from real history.

Non-Linear Dynamics

Financial relationships change. Correlations break during crashes. LQMs adapt to these regime changes dynamically, unlike static predictive models.

The Evolution of Financial AI

Feature Predictive AI (Legacy) Large Language Models (LLMs) Large Quantitative Models (LQMs)
Primary Data Structured Tables Text & Documents Numerical Time-Series & Logic
Core Task Regression / Classification Token Prediction Probability Distribution Generation
Handling Volatility Poor (Assumes Linearity) N/A (Hallucinates) Excellent (Captures Stochasticity)
New Data Creation None Text Generation Synthetic Financial Data
Use Case Simple Forecasting Report Writing / Sentiment Complex Modeling / Risk Simulation

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FinanceGPT Agentic Platform is powered exclusively by LQMs.