# Systematic risk

Systematic risk is the part of investment risk driven by broad market factors that affect many securities and cannot be eliminated simply by holding more stocks.

## What it means

A market-wide shock can affect many stocks at once, even when the companies have little in common. That shared exposure is systematic risk. Changes in interest rates, inflation or economic conditions can move broad parts of the market together. A company-specific event, such as a product recall, is instead an example of idiosyncratic risk.

Holding stocks from different companies can reduce company-specific risk, but it cannot remove exposure to a market-wide decline. Systematic risk is not the same as volatility: volatility measures the size of return fluctuations, including both market-wide and company-specific movements.

## How it is calculated

There is no single universal number for systematic risk. A common measure of a stock’s sensitivity to a chosen market benchmark is **beta**:

`β_i = Cov(R_i, R_m) / Var(R_m)`

- `β_i` is the stock’s beta, a unitless number.
- `R_i` is the stock’s return over each observation period, usually expressed as a decimal or percentage.
- `R_m` is the benchmark’s return over the same periods, in the same units.
- `Cov(R_i, R_m)` is the covariance of those returns, in squared return units.
- `Var(R_m)` is the variance of benchmark returns, also in squared return units.

A beta of 1 indicates that the stock has historically moved about 1% for each 1% benchmark move *on average*, within this linear model. Beta measures exposure to the **chosen** benchmark, not every source of systematic risk. Its estimate depends on the benchmark, data period and sampling frequency.

For a basket of stocks, `β_basket = Σ(w_i × β_i)`, where `w_i` is each stock’s share of the basket’s market value and the weights sum to 1. This weighted average assumes the individual betas use the same benchmark and return convention. If every stock has the same beta, spreading the basket across more names does not lower its estimated market sensitivity.

## Worked example

Suppose a hypothetical basket holds two stocks worth US$5,000 each. Both betas are estimated against the same broad stock index, using the same observation periods:

1. Each stock has a basket weight of `US$5,000 / US$10,000 = 0.50`.
2. Stock A has a hypothetical beta of 1.2; Stock B has a hypothetical beta of 0.8.
3. The basket beta is `(0.50 × 1.2) + (0.50 × 0.8) = 1.0`.

If the index fell 2% over a period, the linear beta estimate would imply an approximately 2% market-related move for the basket, or about US$200 on US$10,000. This is **not** a prediction of its actual return: company-specific moves and other factors could make the outcome different.

## Why it matters before you trade

A basket can contain many stocks yet retain substantial exposure to the same broad market. Before placing an order, a pre-trade analysis can distinguish shared market exposure from concentration in individual companies, sectors or countries. Neither a low concentration score nor a low estimated trading cost means the basket is protected from a market-wide move.

Beta offers one way to examine market sensitivity, but it is a historical estimate. A 1-day value at risk (VaR) estimate answers a different question: it summarizes potential short-horizon loss under a stated model and confidence level. It does not isolate systematic risk or describe the worst possible loss.

## How PreTrAIde uses it

PreTrAIde’s stated quantitative measures do **not** include beta or a standalone systematic-risk score. Its pre-trade analysis assesses a stock basket before an order is placed, including daily volatility, 1-day 95% VaR, and portfolio breakdowns by sector, country, region, currency and market capitalization. Those measures can help describe exposures and risk, but none should be read as a direct calculation of systematic risk.

PreTrAIde returns a GO, NO-GO or MODIFY verdict with reasoning as decision support, not an investment recommendation. It does not place orders; users trade through their own broker.

## Common mistakes

- **Assuming more stocks eliminate all risk.** Diversification can reduce company-specific risk, not a shared market shock.
- **Treating beta as a guaranteed response.** The actual return can differ substantially from a beta-based estimate.
- **Equating systematic risk with total volatility.** Volatility also includes stock-specific movements.
- **Ignoring the benchmark.** A beta against one index need not match a beta against another.

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Source: https://pretraide.com/glossary/systematic-risk