3. 3
Team
1978-1983: Bell Labs - Demonstrated microprocessor could be
used for UNIX workstation
1983-1986: Melodian - Developed the first digital music sampling
synthesizer, credits include Stevie Wonder, “The Woman in
Red” and Bon Jovi, “Slippery When Wet”
1986-1988: Sun Investors - Developed computerized option
trading models on Amex
1988-1993: Bass Brothers - Designed option trading and risk
management system
1993-2000: Morgan Stanley - Developed and tested proprietary
trading, risk management, and derivatives trading platforms
2000-2002: Schonfeld Securities and Hold Brothers - Developed
quantitative trading systems
2003: Apogee Fund Management - Formed predecessor to
Acos with Sam Glassman
B.S. Computer Science & Electrical Engineering, University of
Pennsylvania
M.S. Computer Science, University of Pennsylvania
1992-1998: NatWest Securities - Head trader for
proprietary quantitative long-short equity portfolio
1998-2002: Schonfeld Securities/Riverside Asset
Management - Head trader for quantitative equity
long-short hedge fund
2003: Apogee Fund Management - Formed
predecessor to Acos with Harry Mendell
B.S., New York University
M.B.A., New York University
Harry Mendell
Research and Development
Sam Glassman
Head Trader
4. 4
Team
Harsh Sawant
Programmer Analyst
Mathias Lundmark
Research Analyst
2000-2002: Hold Brothers - Analyst in Mendell’s Black Box
group
2003: Apogee Fund Management - Formed predecessor to
Acos with Harry Mendell
B.A., Hawaii Pacific University, GTE Academic All-American
tennis player
M.B.A., Hawaii Pacific University
1996-2000:Worked in banking and financial domain for
various institutions such as Standard Chartered Bank,
Diners Club as a consultant.
2001-2002: Hold Brothers – Programmer Analyst in
Mendell’s Black Box group
2002-2004 : Project Leader, Siemens Information
Systems Ltd.
2004: Apogee Fund Management - Formed
predecessor to Acos with Harry Mendell
B.E., Pune University, India
5. 5
Investment Philosophy
ACOS has created a new paradigm for model-based
trading that has enabled us to outperform our peers
during out-of-sample testing in 2004.
The key to continuing this performance record is through
research, development and innovation.
Recent research has yielded an opportunity to generate
incremental returns by analyzing credit data
“The Twist” – a unique blend of
Quantitative equity market neutral
Capital Structure arbitrage
Option analysis
6. 6
The ACOS Process
Potential
Trades
Dynamic Universe
Selection
Equity Capital
Arbitrage Model
ACOS
Alpha Signals
Asset
Allocation
Optimizer &
Market Impact
Model
Computerized
Trade Execution
Portfolio
Continuous
R&D
7. 7
Process: Dynamic Universe Selection Technology
Static
Universe
Filtered
Universe
Permutation
Engine
Potential
Pairs
Potential
Trades
Filter
Engine
Statistical
Engine
Relationship
Engine
Universe = 8,000
Publicly Traded
US Equity
ADRs Market Cap
Liquidity
Options Data
Credit Data
10 Sectors
150 Stocks/Sector
110,000 Pairs
Rank Potential Pairs
• Sector Data
Market Cap
Liquidity
Options Data
Credit Data
1,500 Stocks
+
500 ADRs
• Equity Market Data
• Stock Price Data
• Corporate Relationship Data
8. 8
Process: Equity Cap Arb Model
Equity Capital Arbitrage Model
Technicals
Prices
Correlation
Options
Alpha Signals
Potential Trades
Quant L/S: “The Twist”
Fundamentals
Sectors
Earnings
Insider Trading
Corporate Action
Credit Markets
Implied
Volatility
9. 9
Process: Optimizer & Market Impact
Find the ‘right mix’ between risk vs reward in
the portfolio
Constraining position limits
Regulate the leverage
Evaluate liquidity
10. 10
Process: Trade Execution
Minimize market impact and opportunity cost
Use of trade execution algorithms to “quietly”
acquire and dispose of positions
Monitor Implementation vs Model
11. 11
Actual Performance Tracking Model Performance
D
ate
1/13/2004
1/26/20042/5/2004
2/18/20043/1/2004
3/11/2004
3/23/20044/2/2004
4/15/2004
4/27/2004
5/7/2004
5/19/20046/1/2004
6/11/2004
6/23/20047/6/2004
7/16/2004
7/28/20048/9/2004
8/19/2004
8/31/2004
9/13/2004
9/23/2004
10/5/2004
10/15/2004
10/27/2004
11/8/2004
Model
Actual Trades (2:1 Constant Lev)
12. 12
Risk Management Guidelines
Geographic:
US: 75%
Canada/UK/Europe: 25%
Diversification:
100-150 long/short pairs
Market Exposure:
Average Gross: 275%
Maximum Gross: 500%
Average Net: +/-3%
Maximum Net: +/- 10%
Industry Limit:
Average Gross: 12%
Maximum Gross: 25%
Maximum Net: +/-6%
Position Limit:
Average Size: 2%
Maximum Size: 5% cost (long or short), 7% market
Market Cap:
No positions usually less than $500 million market cap
Liquidity:
Usually no position is greater than 10% of the daily volume
30% minimum can be liquidated in 1 day (33% daily volume)
100% can be liquidated in 5 days
13. 13
Risk Management Techniques
Daily Review
Pricing algorithm to detect outliers
Factor exposure analysis
VaR analysis to limit specific volatility and draw downs
Stress Testing and Scenario Analysis
Asset Allocation Techniques
14. 14
Research & Development
Evolutionary process
Continuous commitment to keeping models
current and developing new products.
Utilize Board of Advisors and industry contacts
15. 15
R&D Example: Sample Cancellation
Based on Equity Signal, long ABK and short MTG
(3.00)
(2.00)
(1.00)
0.00
1.00
2.00
3.00
4/7/2004
4/15/2004
4/22/2004
4/29/2004
5/6/2004
5/13/2004
5/20/2004
Time
Stdev
-7.00%
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
PL
CreditZ
EquitZ
% Return
Credit Signal ---
contradicts
Equity Signal ---
Equity Signal ---
Triggers at Stdev 2
16. 16
R&D Example: Sample Confirmation
Based on strong Credit Signal, short ARM long NAV
(3.50)
(2.50)
(1.50)
(0.50)
0.50
1.50
2.50
3.50
7/20/2004
7/22/2004
7/26/2004
7/28/2004
7/30/2004
8/3/2004
8/5/2004
8/9/2004
8/11/2004
Time
Stdev
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
PL
CreditZ
EquitZ
% Return
Clear Credit Signal ---
Triggers at 2
Weak Equity Signal ---
17. 17
R&D Example: Rigorous Testing Shows Promising Results
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
1.80%
2.00%
2002-2004 Future Returns on fixed time intervals using multi dimensional data
Equity 0.15% 0.53% 0.77% 1.15%
Equity plus Bond 0.15% 0.55% 0.83% 1.49%
Equity plus Option 0.21% 0.74% 1.11% 1.74%
Equity plus Bond, Option 0.20% 0.75% 1.14% 1.96%
OneDay PL Five Day PL Ten Day PL Twenty Day PL
18. 18
R&D Example: Complete Testing Manifests Enhancement
50.00%
51.00%
52.00%
53.00%
54.00%
55.00%
56.00%
57.00%
58.00%
59.00%
60.00%
61.00%
2002-2004 Probability on fixed time intervals using multi dimensional data
Equity 53.58% 54.81% 55.10% 55.36%
Equity plus Bond 53.71% 55.14% 56.16% 57.55%
Equity plus Option 54.81% 57.34% 57.98% 58.75%
Equity plus Bond, Option 54.98% 56.87% 58.23% 60.18%
OneDay Probability Five Day Probability Ten Day Probability Twenty Day Probability
19. 19
Model Simulation
Acos Simulated MultiDimensional Trading System vs Mkt Neutral HF Indices
95
100
105
110
115
120
125
Mar-02
May-02
Jul-02
Sep-02
Nov-02
Jan-03
Mar-03
May-03
Jul-03
Sep-03
Nov-03
Jan-04
Mar-04
May-04
Jul-04
Sep-04
Acos
HF Mkt Neut Avg
Above is a comparison of Acos Simulated returns net of all fees at 2:1 leverage ($1=1 long + 1
short) versus the average of CSFB/Tremont, HFR, VanHedge, Hennessee and Hedgefund.net
Equity Market Neutral and Statistical Arbitrage indices.
21. 21
Contact Information
Acos Fund Management, LLC
535 Madison Ave-12th Fl
New York, New York 10022
212-207-4333
Sam Glassman Harry Mendell
sg@acosfm.com hm@acosfm.com
23. 23
Key Qualifications
Harry Mendell
Over 15 years of quantitative research and model development experience in options, futures,
and equity trading
Designed and implemented quantitative based trading systems for options, futures and
equities at Sun Investors, Morgan Stanley, Bass Brothers and Online Securities (Hold
Brothers)
Designed and managed the development of Morgan Stanley’s global risk management system
for Equities, Fixed Income, FX and Commodities
Created, staffed and directed the research, development and trading team that built Hold
Brother’s fully automated short-term trading system, called BlackBox
Managed the development group that created Morgan Stanley’s global risk management
system. Worked closely with the firm’s trading desks to insure a proper understanding and
evaluation of their risk factors and positions
Developed Morgan Stanley’s futures “black box” trading system
Developed and back tested systems for Morgan Stanley’s trend-based futures trading, mean
reversion equity trading, and volatility model based options trading
Consulted and developed trading systems for the Bass Brothers
24. 24
Key Qualifications
Sampson Glassman
Proven performance track record: 1994 - 2002 Avg 15% ROR and Sharpe ratio >2
12 years of trading/portfolio management experience
NatWest Securities: Founding member of team managing $500mm market neutral and risk
arbitrage portfolio with responsibility for $100mm portfolio
Instrumental in hiring/managing of 8 person technology team
Schonfeld/Riverside Asset Management: Established statistical arbitrage team with capital
from major, short term trading firm
Solely managed $100mm pairs portfolio and co-managed $300mm portfolio
Managed business operations
Managed build out of technology infrastructure for research, development & trading
for team
25. 25
Board of Advisors
Andreas Weigend, Ph.D.
Most recently was Chief Scientist, Amazon.com
International authority on data mining & time series prediction
Previously, a Chief Scientist at a market data analysis firm backed by D.E. Shaw, Deutsche Bank and others. The company created
information products based on behavioral finance, using state-of-the-art statistical techniques
Has held several full-time professorships, most recently at New York University Stern School of Business
Recently chaired the Forum on Behavioral Analytics in Brokerage & Banking & organized the Sixth International Conference on
Computational Finance
Received numerous prizes including an IBM Partnership Award & a National Science Foundation Career Award
Post Doctoral research fellow at Xerox PARC & Santa Fe Institute
Received his Ph.D. from Stanford University in physics
Completed consulting projects in finance for: Goldman Sachs, Morgan Stanley, UBS, Prediction Co., Nikko Securities, J.P.
Morgan, Grantham, Mayo, Van Otterloo & Co. LLC
Has published more than 100 scientific papers and co-authored 6 books on neural networks, computational/behavioral finance
and time series prediction
More information available on: http://www.weigend.com/
Peter Carr, Ph.D.
Head of Quantitative Financial Research at Bloomberg
Named ‘Quant of the Year’, Risk Magazine, 2003
Currently visiting professor at Courant Institute of New York University
Associate editor for 6 academic journals related to mathematical finance of derivatives
Co-inventor of the variance gamma model, inventor of static & semi static hedging
Introduced variance swaps & corridor variance swaps
Authored over 35 papers related to Option pricing and mathematical finance
Practitioner director for the Financial Management Association
Principal [Head of Equity Derivatives Research], Banc of America Securities, Jan. 1999-June 2001
VP, Morgan Stanley, Feb 1996-Dec 1998
Professor of Finance, Cornell University, July 1988-June 1996
More information available on: http://www.math.nyu.edu/research/carrp/
26. 26
Disclaimer
THIS IS NOT AN OFFERING OR THE SOLICITATION OF AN OFFER TO PURCHASE AN
INTEREST. ANY SUCH OFFER OR SOLICITATION WILL BE MADE TO QUALIFIED
INVESTORS ONLY BY MEANS OF A FINAL OFFERING MEMORANDUM AND ONLY IN
THOSE JURISDICTIONS WHERE PERMITTED BY LAW.
AN INVESTMENT IN THE FUND IS SPECULATIVE AND INVOLVES A HIGH DEGREE OF RISK.
OPPORTUNITIES FOR WITHDRAWAL/REDEMPTION AND TRANSFERABILITY OF
INTERESTS ARE RESTRICTED, SO INVESTORS MAY NOT HAVE ACCESS TO CAPITAL
WHEN IT IS NEEDED. THERE IS NO SECONDARY MARKET FOR THE INTERESTS AND
NONE IS EXPECTED TO DEVELOP. THE PORTFOLIO, WHICH IS UNDER THE SOLE
TRADING AUTHORITY OF THE INVESTMENT MANAGER, IS PRIMARILY CONCENTRATED
IN A STATISTICAL ARBITRAGE STRATEGY AND THIS LACK OF DIVERSIFICATION MAY
RESULT IN HIGHER RISK. A SUBSTANTIAL PORTION OF THE TRADES EXECUTED MAY
TAKE PLACE ON NON-U.S. EXCHANGES. LEVERAGE WILL BE EMPLOYED IN THE
PORTFOLIO, WHICH CAN MAKE INVESTMENT PERFORMANCE VOLATILE. AN INVESTOR
SHOULD NOT MAKE AN INVESTMENT, UNLESS IT IS PREPARED TO LOSE ALL OR A
SUBSTANTIAL PORTION OF ITS INVESTMENT. THE FEES AND EXPENSES CHARGED IN
CONNECTION WITH THIS INVESTMENT MAY BE HIGHER THAN THE FEES AND
EXPENSES OF OTHER INVESTMENT ALTERNATIVES AND MAY OFFSET PROFITS.
THERE IS NO GUARANTEE THAT THE INVESTMENT OBJECTIVE WILL BE ACHIEVED.
MOREOVER, THE PAST PERFORMANCE (IF ANY) OF THE INVESTMENT TEAM SHOULD NOT
BE CONSTRUED AS AN INDICATOR OF FUTURE PERFORMANCE.
