Transcript Governance, Transparency and Good Portfolio Management

Governance, Transparency
and Good Portfolio
Management with Internetbased Tools
www.mcubeit.com
Dr. Arun Muralidhar
Outline
1) Keys to Effective Portfolio Management: 5 Key Steps
2) Good Process Overcomes Challenges in Managing Funds
3) Technology Challenges: Where Web Applications Help
4) Using Web-based Technologies Effectively – Demo

Structuring portfolios; understanding risks; converting
risks into higher returns; using attribution to improve
decisions
5) Using the Internet to Empower Investors
6) Summary and Conclusions
2
Who Benefits from This Presentation?
 Country pension funds
 Central banks
 Funds-for-the-future (e.g., funds to preserve wealth
from the extraction and sale of commodities)
 Liability management organizations
IT Departments: Help make front and middle office effective
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1. Key to Success – Effective Decisions
Annual
Set
Determine
Objectives Benchmark
Daily
Outperform
Benchmark
Monthly
Monthly
Evaluate
Performance
Measure
Risk
 Good management = must make many decisions well
 Must make these decisions in an informed manner
 Process, transparency and governance are critical
 Challenge: Can technology integrate front & back office?
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1. Many Share Responsibility for a Fund
AssetLiability
Risk
Tactical &
Benchmark
Risk
Manager/
Active
Risk
Responsibility
Responsibility
Board of
Governors
Internal Staff
Outside
Managers
Monitor
Decision
Frequency
Annually
Daily/Monthly
Monthly
Manage
How to
Manage the
Risk
Strategic
Decisions –
Need Good
Reporting
Effective
Investment
Decisions – Need
IT Support
Manager
Selection
Decisions –
Good Reporting
Need good technology to track and manage all decisions
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1. Portfolio =Many Decisions
Portfolio
Asset
Allocation
Equity = 40%
Bonds = 40%
Sector/
Regional
By Market
(Local, US etc.)
By Market
(Local, US, Euro)
Style
Selection
Large Stocks/
Small Stocks
Govt./Agency etc.
Manager
Selection
Cash/
Currency = 20%
Mgr
Mgr
Mgr
Mgr
Mgr
Mgr
A
B
C
D
E
F
Bank 1
Bank 2
for
Deposits
for
Deposits
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2. Challenges in Managing Portfolios
1. Manage ongoing cash inflows and outflows
2. Evaluate and implement rebalancing strategies
3. Manager selection and allocations
4. Asset, country, style, sector or currency allocation
A portfolio is very dynamic – impacted daily.
Each decision can be a source of return or,
if badly managed, can reduce returns
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2. Challenges in Public Entities
1. Resource constrained: financial (budget) and staffing
2. “In public eye”: decisions are reviewed publicly
3. Need to demonstrate that decisions not political;
need to show financial impact of political constraints
4. Good governance and transparency critical
Challenge: Can technology empower staff, to raise
return and lower risk while maintaining control?
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3. Current Technology Challenges
 SILO SYSTEMS – Narrow Applicability:
1) Focus on only one asset (stocks/bonds) or one aspect
(e.g., risk or performance measurement; trading)
2) Multiple systems; high cost to integrate/maintain
3) Required extensive training and client IT backup
4) Not designed by people who managed funds
Senior managers are at risk – not knowing what is
impacting the fund or how to correct it
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3. Current Technology Challenges
 EXCEL based models are often used to make
investment decisions, which from a technology
perspective pose serious challenges:
 EXCEL models prone to error (not transparent)
 Key man risk (if staff leaves); create large teams as insurance
 Difficult to share ideas/analyses across organization
 Managers are at risk if the models have errors
Alternative technology must be transparent, robust,
inexpensive and easy to use!
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3. Web/ASP Model Overcomes Problems
 Enterprise system can be implemented at low cost
 Easy to use and can customize their overall fund
 Support all asset areas in one technology
 Link portfolio management, risk and performance in
one system/framework: transparent, flexible, quick
 Data management can be simplified
Senior managers are empowered – access results from
their desktop (intranet or internet)
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4. Using Web-based Technologies Effectively
A Case Study:
Integrated system that allows user to follow specific
process steps:
1. Specify a clear investment process (i.e., who makes what
decisions at what level of the fund) = GOVERNANCE
2. Understand all the risks taken by the fund = GOVERNANCE
3. Model decisions in a TRANSPARENT way (i.e., simple so
that anyone can understand/evaluate)
4. Attribute performance to improve decisions
Governance, process & transparency = better returns
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Case Study: Step 1
Articulate Responsibilities/Decisions
Portfolio
Asset
Allocation
Equity = 40%
Bonds = 40%
Sector/
Regional
By Market
(Local, US etc.)
By Market
(Local, US, Euro)
Style
Selection
Large Stocks/
Small Stocks
Govt./Agency etc.
Manager
Selection
Cash/
Currency = 20%
Mgr
Mgr
Mgr
Mgr
Mgr
Mgr
A
B
C
D
E
F
Bank 1
Bank 2
for
Deposits
for
Deposits
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Case Study: Step 2
Use Portfolio Tree to Pinpoint Risk
 Structuring risk at total fund level=1.5% (or $300 mn)

From asset allocation and style tilts (excludes managers)

From allocations to assets other than fund benchmark
 Allocation decisions have historically had big drawdowns
Pension
Risk= 1.5%
Fund
Maximum Drawdown = -7.5%
Risk = 1.5%
Risk = 3.2%
Risk = 1.8%
Maximum Drawdown = -5.5%
Maximum Drawdown = -11%
Maximum Drawdown = -6.8%
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Case Study: Step 3
Ensure Decisions Generate Returns
Total Excess = 2%
Total Portfolio
Asset allocation strategy
Equities
Bonds
Cash
1-mo LIBOR
Country allocation strategy
Local Bonds
+
Currency
Excess Return = 0.5%
+
Foreign bonds
Sector allocation strategy
Government
Bonds
Excess Return = 0.5%
Mortgage/
Corporate
Internal/External Managers
Excess Return = 0.5%
+
Manager Excess Return = 0.5%
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Case Study: Step 4
Use Attribution to Improve Decisions
Total Excess = 0%
Total Portfolio
Naïve Rebalancing
Equities
Bonds
Cash
1-mo LIBOR
Excess Return =- 0.5%
Let Portfolio Drift
Local Bonds
Excess Return = - 0.5%
Foreign bonds
Sector allocation strategy
Government
Bonds
+
Currency
Mortgage/
Corporate
Internal/External Managers
Too much focus on
manager selection
Excess Return = 0.5%
+
Manager Excess Return = 0.5%
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Case Study: Step 5
Portfolio Rules: Web = Transparency
Example:
Investment Idea: Cash vs Bonds: Apparently, the price of gold is a good indicator of whether funds should
be invested in cash or higher duration assets (bonds). Rising gold prices are good for cash relative to bonds.
Rule Criteria:
IF ((Price of Gold Today > Price of Gold a Year Ago))
THEN Allocate more to Cash
ELSEIF ((Price of Gold Today < Price of Gold a Year Ago))
THEN Allocate less to Cash
ELSE
Do Nothing
Performance Statistics:
Excess Annualized Return: 0.61%
Risk: 0.94%
Information Ratio: 0.65
Max Drawdown: -1.24% on 3/31/1999
Success Ratio: 61.1%
Confidence in Skill: 97.95%
Good decisions add returns and reduce risk
*Purely hypothetical example and not an investment recommendation
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Case Study: Step 6
Deliver Detailed Reports Through Web
Performance Measures
Benchmark
Strategy
Excess
6.47
9.25
2.78
36.79
55.64
18.85
Risk %
2.28
5.58
3.55
Return / Risk Ratio
2.84
1.66
0.78
Return %
Cum. Return %
Strategy
Excess
70
56.67
Average returns when positive %
1.51
0.86
Average returns when negative %
-1.02
-0.60
Max. consecutive periods of
positive returns
7
7
Max. consecutive periods of
negative returns
4
5
-2.54
-1.99
Success ratio of the rule %
Max. relative loss for a period %
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5. Mcube IT: Better Governance/Returns
Through Web Applications
1. Boards/Senior Managers can set fund structure and
monitor all decisions easily
2. Portfolio managers can use to make better decisions
3. Middle office can use to evaluate risks/performance
4. Web-technology for 3 Ms of Portfolio Management:
“Measure”, “Monitor” and “Manage”
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6. Summary & Conclusions
 Portfolio management = many decisions and requires many groups
to coordinate (board, front office, back office, external managers)
 Silo systems make it difficult and expensive to manage fund
 Web (internet/intranet) can overcome challenges
 Can create customized portfolio structure, analysis and reports
 Can create transparency for good governance, returns and risk
management
 AlphaEngineTM: adopt best practices quickly and easily
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Appendix
5. Converting Ideas To Rules to Give
Good Process and Add Value
Rule
Rule Description
Cash vs. Bonds, based on
Gold
Duration choice based on price of gold. If the spot price of gold is higher than it
was a year ago, overweight cash, otherwise overweight bonds
Stocks vs Bonds:
Halloween Effect
Stocks vs Bonds:
Inflation/Growth
Market Volatility
Stocks tend to underperform bonds between June and Sept - apparently works in
16 out of 18 stock markets, so underweight stocks during this period
Equities undervalued when inflation rises (Modigliani-Cohn insight); equities
favored when industrial production is increasing
Low equity volatility in a rising stock environment is bullish for equities.
Oil and Economy
Rising oil prices affect the economy and tend to depress equities.
P/E Ratio Rule
Value rule for equity (vs FI) using the S&P 500 P/E
Fed Model
When equity yield is higher than treasury yield then buy equity, else sell equity
Unemployment Rate
Buy stocks when the unemployment rate is falling (good for economy)
US/International: LIBOR
Rates
US/EAFE: Favor
Underperformer
Overweight equity market with the stronger currency (higher interest rate)
Overweight equity market which has underperformed over past year (i.e., buy
the laggard)
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Rule Performance (1998-2004)
Rule
Excess
Annualized Information Confidence Success Ratio Good
Max
Return
Ratio
in Skill
Ratio
/Bad Risk Drawdown
Cash vs. Bonds, based on Gold
0.04%
0.20
68.8%
56.4%
1.30
-0.44%
Halloween Effect
0.98%
0.88
98.0%
63.8%
1.42
-1.58%
Inflation/Growth
0.50%
0.57
93.1%
79.7%
1.07
-1.31%
Market Volatility
0.12%
0.11
67.8%
56.4%
1.41
-2.74%
Oil and Economy
0.45%
0.57
91.6%
70.5%
1.16
-0.84%
P/E Ratio Rule
0.17%
0.39
87.1%
50.0%
2.12
-0.80%
Fed Model
Unemployment Rate
0.47%
0.51%
0.50
0.61
91.8%
94.1%
61.5%
59.0%
1.43
0.99
-2.17%
US/EAFE: LIBOR Rates
US/EAFE: Favor Underperformer
0.17%
0.53%
0.43
0.95
84.7%
99.3%
55.1%
64.1%
1.07
1.33
-0.71%
-1.11%
-1.07%
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