Omega Point

The Omega Point application provides portfolio analysis tools that include describing how a portfolio's factor risk &amp; factor exposure have deviated from their historic values. This is described as a factor's drift and can be found in the right-hand column when using the Analyze: Risk and the Analyze: Exposure modules

Drift is categorized by the following percentile buckets: <code>Very High</code>: 95% - 100% <code>High</code>: 75% - 95% <code>Neutral</code>: 25% - 75% <code>Low</code>: 5% - 25% <code>Very Low</code>: 0% - 5%

Image of line graph displaying Style Exposures, with Apr 26, 2021 selected and showing that day's portfolio exposures for each style factor

Drift is calculated for the specified date range — when the date range value is less than 3 months, the app automatically uses as many dates as possible to get a statistically significant drift calculation.

Selecting a factor category will display historical factor exposure values [or a factor's risk decomposition values, when using Analyze: Risk].

Dynamically interact with the graph, and introspect any particular date (dashed line), then...

Display that date's weighted-average factor exposures in the right-hand column, and it's drift (red, blue arrows).

1. Drift is calculated for the specified date range — when the date range value is less than 3 months, the app automatically uses as many dates as possible to get a statistically significant drift calculation. 
2. Selecting a factor category will display historical factor exposure values [or a factor's risk decomposition values, when using Analyze: Risk].
3. Dynamically interact with the graph, and introspect any particular date (dashed line), then...
4. Display that date's weighted-average factor exposures in the right-hand column, and it's drift (red, blue arrows).

Find the mean and calculate the standard deviation over the full date range*

Find the difference from today's value from the mean, divided by the standard deviation driftValue = (todaysValue - mean) / deviation

The driftValue is then bucketed into it's respective percentile by the following categories driftValue &lt; -1.645σ = Very Low driftValue &lt; -0.674σ = Low driftValue &lt; 0.674σ = Neutral driftValue &lt; 1.645σ = High driftValue &gt; 1.645σ = Very High   The range [-0.674σ, 0.674σ] represents the range of z-scores that can be mapped for the middle 50% of any set, i.e. <code>Neutral</code>: 25% - 75% The range [0.674σ, 1.645σ] &amp; [-0.674σ, -1.645σ] corresponds to the next 20% of values falling into their respective buckets <code>High</code>: 75% - 95% <code>Low</code>: 5% - 25% And values that fall above or below +/- 1.645σ correspond to the last 5% of values <code>Very High</code>: 95% - 100% <code>Very Low</code>: 0% - 5%

1. Find the mean and calculate the standard deviation over the full date range*
2. Find the difference from today's value from the mean, divided by the standard deviation driftValue = (todaysValue - mean) / deviation 
3. The driftValue is then bucketed into it's respective percentile by the following categories driftValue &lt; -1.645σ = Very Low driftValue &lt; -0.674σ = Low driftValue &lt; 0.674σ = Neutral driftValue &lt; 1.645σ = High driftValue &gt; 1.645σ = Very High   The range [-0.674σ, 0.674σ] represents the range of z-scores that can be mapped for the middle 50% of any set, i.e. <code>Neutral</code>: 25% - 75% The range [0.674σ, 1.645σ] &amp; [-0.674σ, -1.645σ] corresponds to the next 20% of values falling into their respective buckets <code>High</code>: 75% - 95% <code>Low</code>: 5% - 25% And values that fall above or below +/- 1.645σ correspond to the last 5% of values <code>Very High</code>: 95% - 100% <code>Very Low</code>: 0% - 5%

Automatic calculations display how a factor's current risk & exposure sizes up against its historic values

Drift Methodology