Company Sensitivity Vectors

The same news event hits different companies in completely different ways. A rate hike can be positive for banks (wider net interest margins) and negative for highly leveraged growth companies (higher cost of capital, compressed multiples). A trade tariff announcement can be a tailwind for domestic producers and a cost shock for companies with complex global supply chains.

Most platforms stop at scoring the news itself. Swingtrader goes one step further: it maps every news impact vector against each company's fundamental sensitivity profile to tell you exactly who wins and who loses.

What sensitivity vectors are

A sensitivity vector is a numerical fingerprint of how a company is structurally exposed to different market forces.

Each vector is built from real fundamental data:

• Income statements
• Balance sheets
• Ratios
• Market cap
• Sector and industry

This data is compressed into nine dimensions that mirror the news scoring clusters.

Think of it as a company's DNA relative to the macro environment:

• A company with high gross margins, low debt, and domestic revenues will have a very different vector from a highly leveraged exporter with thin margins.
• When a news event scores high on "interest rate sensitivity" and a company scores high on "floating rate debt exposure", the system automatically flags a headwind.

The nine dimensions

Each company is scored across the same nine clusters used in news impact scoring:

1. Macro Sensitivity

Exposure to rates, FX, inflation, credit, and commodities.

1. Sector Rotation

How the company's sector tends to benefit or suffer when capital rotates between sectors.

1. Business Model

Capital intensity, operating leverage, and pricing power.

1. Financial Structure

Debt load, refinancing risk, and covenant exposure.

1. Growth Profile

Earnings quality, growth acceleration or deceleration, and guidance trajectory.

1. Valuation & Positioning

Valuation multiple, mean reversion risk, and hedge fund crowding.

1. Geography & Trade

China exposure, supply chain complexity, and tariff sensitivity.

1. Market Behaviour

Institutional flow and technical positioning.

1. Ticker Relationships

Supplier, customer, and competitor linkages.

Every score is rank-normalised against all companies on the same exchange.

A score of 0.9 on debt burden means the company is in the top 10% of leverage exposure on that exchange — not just that it "has some debt".

How it connects to news

When an article is scored, Swingtrader converts it into a news impact vector across the same nine dimensions.

For each company, the platform then:

1. Takes the company's sensitivity vector.
2. Takes the article's impact vector.
3. Computes the dot product between them.

• Companies that are structurally aligned with the positive dimensions of the news surface as tailwinds.
• Companies that are structurally exposed to the negative dimensions surface as headwinds.

From a single macro article, Swingtrader can show you:

• Which six companies stand to benefit most.
• Which six companies are most at risk.

All are ranked by how structurally exposed they are to that specific news event.

Why this matters

Retail investors are often caught on the wrong side of a news event not because they misread the headline, but because they didn't know their stock was structurally exposed to the risk the headline described.

Sensitivity vectors make that exposure visible before the move happens — not after.

By combining:

• News impact vectors (what the story is about), and
• Company sensitivity vectors (who is structurally exposed),

Swingtrader helps you understand which tickers the news truly matters for, and in which direction.