Omnibus Rules Effectiveness - Broadridge

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Omnibus Rules Effectiveness - Broadridge

Omnibus Tool Effectiveness Analysis

An effort to determine optimal tool parameters

David Paletta

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Objective

Continue last year’s analysis to build out the test base of

data and tool configurations

– What rules / rule combinations catch the true violators

– What rules / rule combinations have too many false

alerts

– What rules / rule combinations have too many missed

violations

Determine the most effective settings

– How varying different parameters affect results

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Scope

Results are relative

– We are not testing individual client rules

– Focused on a spectrum of tool-parameters

– Ideally the test should have all individual account

details

• Only analyzed omnibus accounts that had underlying details

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Last year vs. This year

Category 2010 2011

Client Databases 5 5

Omnibus Tool Setups 53 314

Date Parameters 67 41

Accounts Analyzed 744,156 1,780,242

Omnibus Dollar Thresholds $5K, $15K, $25K* $10K, $25K, $50K

Individual Dollar Thresholds $15K $5K, $10K, $15K

*Pattern tool only

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What we are chasing after

Tool

Omnibus Accounts

with Underlying

Violations

Underlying

Violations*

Pattern 01: I/O 30, 5K Indv 702 4,578

Pattern 02: I/O 30, 10K Indv 0 0

Pattern 03: I/O 30, 15K Indv 0 0

Pattern 04: I/OI/O 90, 5K Indv 26 32

Pattern 05: I/O/I/O 90, 10K Indv 7 12

Pattern 06: I/OI/O 90, 15K Indv 3 3

*Many violations may be associated with one parent account

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Standard Deviation

Sigma varied from 2 to 6 by 1

Results

– Produced a lot of false alerts

– Performed better for lower threshold violators

– Only Sigma of 2 produced successful alerts

– Threshold of $10K was most effective but resulted in

only 6% success rate

– Worked most effectively for larger patterns picking

up 43% of actual violations

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Portfolio Liquidation

Varied timeframe at 30 and 90 days

Varied liquidation percent from 10% to 80%

Results

– Produced a lot of false alerts

– Performed better for lower threshold violators

– Performed only at a timeframe set to 30 days and

liquidation % set to 10%

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NAV Change

Varied NAV increase factor, trade days before and after,

and min 5 shares sold

Results

– Performed best with trade days before set to 5

– After that, trade days after and min % sold worked

best across several combinations

– Worked better at higher thresholds, $25,000 being

ideal

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Correlation

Varied Days omitted by 1 from 1 to 5

Varied Minimum correlation change from 0.1 to 0.7 by 0.2

Results

– Produced the least amount of false alerts when days

omitted set to 1

– Performed better at lower threshold violators

– Min correlation change only performed at 0.1

– Days omitted worked best at 1

– Tool performed best at $25K threshold

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Multi-rule Effectiveness

10% of violations were identified by more than one rule

For $10K thresholds, the following rules have a the

highest coverage:

– Standard deviation with 2 multiplier

– Correlation with days omitted of 2 and minimum

correlation change of 0.3

– Portfolio liquidation of 90 day timeframe and 0.1

liquidation percentage

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2011 Findings versus 2010 Findings

2010

– All Omnibus Tools (non-pattern) seem to work better at

higher dollar thresholds ($15K vs. $5K)

2011

– Some Tools worked better for higher thresholds (NAV,

Correlation)

– Others worked better for lower thresholds (Standard

Deviation)

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2011 Findings versus 2010 Findings

2010

– NAV Change works better for smaller timeframes

• 3days before and after

– NAV tool marginally performs better at higher NAV

increase factors

2011

– NAV Change worked best at 5 days before and varied

on days after

– NAV Change performs better at lower NAV increase

factors

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2011 Findings versus 2010 Findings

2010

– Standard Deviation effectiveness is better at higher

sigma multipliers

2011

– Standard Deviation only worked at a sigma

multiplier of 2

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Next Steps

Data collected from 2010 analysis coupled with current

analysis is providing a window into effectiveness of

tools

– Complete the analysis and provide results in

November to client base

– Add analysis by fund size

– Add analysis by fund volatility

• Higher beta funds provide opportunity for market timers

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