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  Portfolio Crowding

Investment Portfolio Fund Factor Crowding Trade Analysis

Portfolio Crowding allows users to determine degrees of factor crowding within portfolios, allowing for allocators to target less crowded factors, increasing the likelihood of outperformance.

 
Aapryl’s Portfolio Crowding Module analyzes portfolio data to identify and manage “crowded trades” which can add significant risk to portfolios. Crowded trades occur when many large market participants pursue the same investment strategies, causing overlapping portfolio positions. This risk can be especially high when negative shocks hit the markets and force managers to liquidate positions. These “fire sales” may cause losses for all investors following a similar strategy and result in further liquidations, driving stock prices into a downward spiral. Our research has found that portfolio crowding can occur among factor exposures as well as specific positions. The financial crisis’ “quant meltdown” is a classic example of this in which “factor crashing” led to significant portfolio losses. We use three different methodologies to measure and predict factor crowding:
  • Pairwise correlation
  • Valuation dispersion
  • Fractal dimension
Market activity has been increasingly leveraged to certain factors, so determining the degree of factor crowding in a portfolio is essential to making informed factor allocations and managing risk.

HOW PORTFOLIO CROWDING WORKS:

START A CROWDING ANALYSIS

With Crowding, you can better understand how the factors within a portfolio are behaving in the market.

DEFINE YOUR PARAMETERS

Select a product, and provide the underlying holdings, and benchmark.

ANALYZE CROWDED RESULTS

See a detailed Crowding analysis of each factor, and how they behave individually over time.

Portfolio Crowding Highlights for Fund Factor Trade Analysis:

  • Avoid crowded trades, which occur when many large market participants pursue the same investment strategies, causing overlapping portfolio positions.
  • Measure exposure to commonly used factors including leverage, momentum, book to price, earning yield, size, and growth.
  • Identify areas of risk that may not otherwise be evident by isolating the most crowded and least crowded factors in a portfolio.
  • Improve the risk-adjusted return profile of a portfolio by allocating capital to less crowded factors which could significantly lower the probability of extreme crowding risk.
  • Construct a systematic multi-factor portfolio that could significantly lower the probability of extreme crowding risk and improve the risk-adjusted return profile of a portfolio.

For more information on how Aapryl's proprietary methodologies can be used, please contact us at info@aapryl.com.