Notes from Capital Allocators: Basil Qunibi

Link: https://capitalallocatorspodcast.com/2018/03/04/novus/

About Basil: Founder of Novus which does analytics on managers and portfolios trying to disaggregate sources of edge/skill and quantify obliques risks such as crowding and liquidity.

Early Days

Initial Research

• Early 2000s, Basil began studying underutilized data sets :
  1. Public filings (ie 13F, 13D etc) domestic and abroad
  2. Monthly exposure reports from managers
  3. Position level reports when available which provided full transparency

Meeting Resistance

• “People hear with their amygdala”
  • Amygdala is the emotional center of the brain. His analysis was perceived as a threat as opposed to being received with commensurate rationality. Often his analysis contradicted narratives or perceptions

Novus’ Start

• Initial Novus Products
  • Individual Manager Report
    • batting and slugging avg, long/short attribution, geographic/industry exposures
  • Overlap Report
    • Calculate overlap between candidate managers as well as the client allocator.
  • Aggregate/Look Thru Report
    • Analytics on an allocator’s entire portfolio of managers combined
• Novus Framework Product: aimed at distilling manager skill, positioning (based on private data on 1500 funds)
• Product aims to be “Moneyball for Allocators”

Moneyball for Allocators: Decomposing Manager Skill

Systematic Factors

Factors that depend on the broader market.

• Exposure Management: How does gross exposure variation influence return? On average detracts 200 bps/yr
  • Manager’s variation in this aspect is not persistent; deviation from mean is mostly luck
• Capital Allocation: How does exposure to capital structures or sectors contribute to performance?
  • This factor is also typically negative for most managers

Intrinsic Factors

More persistent and where the potential for alpha lies

• Security selection: items picked out of the sectors or geographies
• Sizing: This is compared to a control of equal-weighted portfolio
• Trading: Tactical trading seen in flipping positions

Using the Framework to Make Better Allocation Decisions

• If the allocation thesis for any fund is simply returns it will invariably hit a bad run. Mapping a fund’s skill to the environment is a better basis to decide whether to cut or increase exposure to the fund than simply returns.
  • For example, if the majority of a fund’s monthly alpha comes from trading but the data shows that the volume in the fund’s positions has been steadily dropping, it may indicate a lack of opportunity to capitalize on the fund’s strength.
• The framework allows an allocator to evaluate a fund based on its stated intentions. If they claim they have an edge in security selection they can be rated on that dimension.
  • This shifts the evaluation from “thinking in T to thinking in N”.
  • It doesn’t make sense to compare a fundamental value strategy vs a high-frequency strategy at the same time horizons.
  • Large sample size of trades without any single trades dominating the results is easier to evaluate than strategies that make a few concentrated bets.
• Benefit of increasing transparency also accrues to good managers since the story is about more than returns and the data can reveal that a bad run is just bad luck (ie losses coming from extrinsic non-persistent factors)

4 Measures of Crowding

1. Conviction: largest position sizes amongst managers; names reported as > 5% positions within a fund
   • Best performing factor over time
   • From Faber’s interview with fellow Novus co-founder Altshuller, they constructed a ‘Conviction Index’ with Barclays based on impressive and still persistent performance of stocks which rank high on a sort of high conviction positions by hedge funds (stock > 7.5% of portfolio concentration)
2. Concentration: How tightly held are the shares?
   • This is also a positive factor
3. Consensus: how popular is the name?
   • This factor underperforms over time
4. Crowdedness: How consensus is the name AND how much daily volume do they represent? “How crowded is the theater; how big is the exit?”
   • This is actually a factor which performs well over time but has massive skew

Scaling up as AUM Grows

How you can expect AUM growth to impact manager performance?

• Increasing number of positions
  • If the manager has skill in sizing positions this will ‘flatten’ the alpha
• Moving into higher market cap names
  • If the manager has skill in small cap, this is style drift
• Increasing current position sizes
  • This deteriorates liquidity; while adding it can be a positive feedback loop but this is a double-edged sword. This is the most dangerous form of scaling if liquidity is overestimated