For decades, mathematical models dominated finance. The Capital Asset Pricing Model (CAPM), the Black-Scholes equation, and Discounted Cash Flow analysis were considered the gold standard of stock valuation. Their purpose: to capture a complex world in a handful of variables. Betafold GmbH believes this paradigm has reached its limits.

The Problem with Simplification

Traditional financial models work on the same principle as early weather models. The real world is unimaginably complex — so you reduce it to manageable equations. You select a few variables you consider relevant and ignore the rest. The result is an approximation that delivers reasonable results under stable conditions but fails when faced with unforeseen events.

In financial analysis, this means looking at revenue, profit, debt ratios, and perhaps a few industry metrics. Everything else is discarded — not because it is unimportant, but because no human and no traditional model can process the volume of data.

Weather Forecasting as a Blueprint

Meteorology shows where things are headed. Just a few years ago, supercomputers could barely manage one weather calculation per day — for a coarse grid with limited resolution. The physical equations were well understood, but computing power was insufficient for higher precision.

Today, AI models like Google DeepMind’s GenCast or Huawei’s Pangu-Weather solve the same task faster and more accurately than classical numerical weather models. They learn patterns directly from historical data without relying on simplified physical equations. Resolution is higher, and computation takes minutes instead of hours.

What This Means for Company Analysis

The same shift is coming to financial analysis. If AI algorithms can model complex systems better than hand-crafted formulas, the same applies to company valuation. The decisive advantage: AI models can incorporate all available data without an analyst having to decide in advance which variables matter.

Betafold GmbH applies this approach consistently. Rather than limiting analysis to traditional financial metrics, the Betafold platform incorporates alternative data sources:

  • Bitcoin and crypto transactions — Capital flows in digital currencies can reveal insights about the liquidity and risk profile of companies and entire industries.
  • Flight movements via Flightradar24 — Executive travel patterns, charter flights to specific destinations, or cargo aircraft utilization provide real-time indicators of business activity.
  • Ship movements via AIS data — The Automatic Identification System shows global trade flows in real time. Port congestion, altered routes, or unusual anchor positions signal supply chain problems before they appear in quarterly reports.
  • Satellite imagery — Parking lot occupancy at retailers, construction activity at factory sites, or agricultural yield forecasts — satellite data makes business activity visible that appears in no balance sheet.

AI Decides What Is Relevant

The crucial point of this approach: Betafold does not need to define in advance which data points matter for which industry. The AI independently identifies which signals are relevant for a logistics company, which for a retailer, and which for a bank.

For a shipping company, AIS data may be the strongest indicator. For a retail chain, satellite images of parking lots may deliver better forecasts than the latest quarterly report. For a technology company, flight movements between offices could point to an upcoming acquisition.

Traditional models cannot detect these correlations because they are limited to predefined variables. AI models, on the other hand, process thousands of signals simultaneously and weight them dynamically — without human bias, without arbitrary simplification.

From Simplification to Completeness

The financial industry faces the same disruption as meteorology. Just as AI-powered weather models are replacing classical numerical forecasting, AI systems will replace formula-based company analysis. Not because the old models were wrong — but because they had to oversimplify the world.

Betafold GmbH is building the platform that enables this transition for company analysis: from a few financial metrics to all available data, from rigid formulas to learning algorithms, from human simplification to machine completeness.