# Measuring Factor Impact

Ever wonder what level of factor exposure is “safe” for your portfolio? Combining expectations of a factor’s volatility with the factor’s exposure within the portfolio allows us to highlight potential performance impact in the form of a portfolio’s factor sensitivity.

Factor Sensitivities comes in two flavors, predicted and historical:

predicted utilizes the risk model's predicted factor volatility to provide a forward-looking expectation of factor movement. Predicted Sensitivity is only available to OP Enterprise clients.

historical relies on the realized volatility using a factor's past performance to generate an expectation of factor movement.

Specific details on methodology are detailed below.

## Utilizing Factor Sensitivities

Factor sensitivities can be used in the following workflows:

Evaluating per-asset sensitivities and contribution using Assets Review

Understanding if today's value is normal given its history using Factor Trends

Simulate trades to evaluate the impact of your decisions on the portfolio's sensitivity

Compare portfolios, rebalance experiments, and other market instruments side-by-side

Screen for new securities that have different levels of sensitivity and evaluate a security's standalone sensitivity trend

Note: all factor sensitivities displayed in the application ** default to a monthly time horizon and are calculated with respect to a 1 standard deviation (STD) factor move**. The API offers more flexibility for different time horizons or STD inputs.

By highlighting this existing data relationship between your portfolio and factors, these questions can be simply answered in seconds:

Which factors could impact my portfolio performance the most?

Based on my current factor exposure, how much could a move in the factor return impact my portfolio performance?

What level of factor exposure should I target to avoid large performance impact from factors?

#### 1. Evaluating per-asset sensitivities and contribution using Assets Review

Determine which factors have the largest potential impact to the portfolio, easily identifying the largest security contribution to the portfolio's sensitivity.

Toggle to standalone mode to understand any particular security's potential return impact in a 1 STD factor move.

Using factor sensitivity with the analyze.assets layout allows customers to make use of existing functionality, including dynamic date selection, grouping, and position segmentation. This asset review support article highlights the full set of features available.

#### 2. Analyze factor trends

Determine if a particular factor sensitivity, *with a monthly horizon*, has been constant & determine if today's sensitivity is normal for its history. Easily identify which sensitivities are over 0.5% threshold (50 bps) that can impact the portfolio's performance.

This view is also useful for researching if the portfolio responded in time to a previous factor movement, and anticipate what to do in a similar situation.

#### 3. Utilize factor sensitivities when simulating trade decisions

Utilizing factor sensitivities can provide insights to portfolio managers who may want to mitigate the risk associated with security's with higher factor sensitivities.

Using the trade simulator, PMs can sort based on securities with high sensitivities and enter trade actions to evaluate the impact of this trade decision on the security and total portfolio's sensitivity outlook.

Factor sensitivity should help PMs with making data-driven decisions that eliminate unexpected market bets.

#### 4. Compare portfolios, rebalance experiments, and other market instruments side-by-side

Evaluate any set of resources' factor sensitivity to the current portfolio, for side-by-side analysis. This includes adding any rebalance experiment to evaluate the total impact of the trade decisions.

#### 5. Screen for new securities given a particular level of factor sensitivity

Utilize Market.securities to enter search criteria based on a security's factor sensitivity.

Click on any security to access it's security profile, and evaluate the security's standalone factor sensitivity trend.

### Factor Sensitivity Methodology

Evaluating factor sensitivity is managed by taking advantage of existing data points generated from the selected risk model, combined with the security in question:

#### Predicted

**Security standalone factor sensitivity **

security standalone factor exposure * factor predicted volatility

**Contribution of security factor sensitivity to the portfolio**

security factor exposure * factor predicted volatility * security % equity

**Factor predicted volatility**

Volatility of the factor, as provided by the risk model.

The UI defaults to using a monthly horizon and 1 standard deviation for the factor volatility.

The API allows for customization of the horizon and standard deviations.

The calculation for scaling the horizon is: (annualized factor volatility / square root of 252) * number of days in horizon

#### Historical

**Security standalone factor sensitivity **

Security factor exposure * factor historical volatility

**Contribution of security factor sensitivity to the portfolio**

Security factor exposure * factor historical volatility * security % equity

**Factor historical volatility**

Volatility of the factor, calculated as the standard deviation of daily factor returns using a defined look-back period.

The UI defaults to using a 125-day look-back period, monthly horizon, and 1 standard deviation for the factor volatility.

The API allows for customization of the look-back period and horizon.

The calculation for scaling the horizon is: daily factor volatility * number of days in the horizon.

**Note: **

Historical portfolio dates may not include enough history to generate the historical factor sensitivity data. Since the UI requires a minimum of 125 days to generate the historical factor volatility value, you may see an error message. **Simply exclude the first 6 months from the UI date range to generate historical factor sensitivities. **

The API is subject to the same limitation based on the look-back period defined in the query.