Omega Point runs your portfolio through a set of machine-learning algorithms to generate proprietary insights that identify your portfolio's exposure to inflection points in factor movements. The Mitigate Factor Drift insight identifies your portfolio's factor exposures and analyzes each factor's normalized return. Based on certain threshold criteria, the insight suggests rebalances to your portfolio in order to mitigate exposure to oversold and overbought factors in the market.

What Is Normalized Factor Return?

A normalized factor return is a standardization of a factor return index that uses several Savitzky-Golay filters to provide indicators on the short-term and longer-term levels of the factor return index.  The final score is calculated by applying a standardized Z-Score on the difference between the short-term and longer-term filters. 

Overbought and Oversold Factors

A factor is defined as “overbought” or “oversold” if its normalized factor return is greater than 1 or -1, respectively.

A factor is defined as “extremely overbought” or “extremely oversold” if its normalized factor return is greater than 2 or -2, respectively.

Historically, when looking ahead to the next quarter of trading, an extremely overbought or oversold factor tends to revert to its long-term return trend more than 70% of the time.  A reversion to the long-term return trend is defined as the normalized factor return moving from its current extremely overbought or oversold levels (i.e. greater than 2 standard deviations) to within 0.5 standard deviations of the long-term trend for at least 5 trading days within the selected period.

The below example shows the recent mean reverting pattern of the Axioma US4 Medium-Term Momentum Factor.  As shown in the shaded region in Omega Point's factor profile screen:

  1. The factor hit extreme overbought levels (+2.00) on November 15, 2017 and peaked at a normalized return of +2.16 on November 21st, 2017.  
  2. Subsequently, the factor reverted to +0.5 standard deviations by December 4th, 2017.

How Does This Relate to the Mitigate Factor Drift Insight?

The “Mitigate Factor Drift” Insight does the following:

  • Identifies overbought and oversold candidate factors in your portfolio 
  • Feeds your current portfolio into a factor targeting optimization that seeks to negatively tilt your exposure to overbought factors and positively tilt your exposure to oversold factors.

Factor Targeting Optimization

A factor targeting optimization is different from a traditional mean variance optimizer that purely aims to modulate the exposures in the portfolio, rather than reduce overall risk.  For each targeted factor, the optimizer:

  • Applies a loss function that seeks to minimize the difference between the current portfolio's factor exposure and a parameterized target factor exposure.
  • Adjusts the loss function based on the underlying volatility of the factor.  For example, a technical factor such as momentum or market beta will tend to have a higher volatility than a fundamental factor such as value or growth and the optimizer will take this information into account.

For any optimization in our system, we apply a set of default constraints laid out in the Portfolio Insights article.

Optimization Output

The output of the Mitigate Factor Drift insight is delivered in our insights panel along with the Focus On Your Alpha insight and shows:

  • The risk impact to your current portfolio (left pane)
  • The historical theoretical performance impact to your portfolio (middle pane)
  • The adjustments to the portfolio required to achieve the target exposures (right pane)
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