# Profoundd archive — Epstein Files
# Bates number: EFTA00583288
# Title:        303 Capital Partners Questionnaire
# Dataset:      9
# Pages:        6
# Images:       6 detected
# Tags:         epstein, doj, dataset-9, image-described
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# Doc viewer:   https://profoundd.com/epstein-docs/EFTA00583288
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#----------------------------------------------------------------------

=== SUMMARY ===
303 Capital Partners Questionnaire for Due Diligence Review EFTA00583288 303 Capital Partners Questionnaire for Due Diligence Review To navigate this document please use the tab-key or point mouse to the beginning of the input field. The size of the field will automatically adjust to the length of your input. Please complete to the best of your ability and return as soon as possible (some questions may not apply to your strategy). MANAGER INFORMATION Manager Entity/Team Name: Manager Name: Addre


=== EXTRACTED TEXT ===
303 Capital Partners Questionnaire 
for Due Diligence Review 
EFTA00583288
303 Capital Partners Questionnaire for Due Diligence Review 
To navigate this document please use the tab-key or point mouse to the beginning of the input field. The size of 
the field will automatically adjust to the length of your input. Please complete to the best of your ability and 
return as soon as possible (some questions may not apply to your strategy). 
MANAGER INFORMATION 
Manager Entity/Team Name: 
Manager Name: 
Address: 
Telephone: 
E-mail: 
Name of Primary Contact: Same as above 
Title of Primary Contact: Same as above 
Telephone of Primary Contact: Same as above 
E-mail of Primary Contact: Same as above 
PREVIOUS FIRM 
Firm Name: Tower Research Capital 
Position: Quantitative Trader 
Period of Employment: 2009-2011 
Accomplishments: Developed and ran high frequency Spot FX Strategy, 
Created Alpha Models for FX, Futures and Equities 
P&L Return on capital (Please explain all capital 
usage and leverage assumptions): Approximately 100K/Day. Average unleveraged return 
approximately 10 bps. Leverage varies by product type. 
Track record owned? (If no, then provide ways 
to verify): Track record as part of group, covered by non-disclosure 
agreement. Personal references are available (Dave 
Dugan received them). 
Reason for leaving: Resigned to run own book 
STRATEGY 
Strategy category: Statistical Arbitrage, Data Driven. 
Hedging Techniques: Broad, Portfolio Sharpe maximizing diversification. Low 
sector, industry concentration. 
Market Exposure: Low percentage exposure per name. Average Beta 20%. 
Highly liquid names (Average ADV 175MM, min ADV 
30MM) 
Asset classes / markets traded: US Equities: NASDAQ and other venues. 
Geographical focus: Currently US. Can be extended to European and Asian 
equities. 
Instruments used by percentage: 200-300 highly liquid Tape A and B securities — under 5% 
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exposure per name. 
Describe the strategy in simple terms: 
Uses combination of mean reversion and momentum 
signals. Mean reversion with respect to set of statistical 
indices resembling multiple RSIs. 
Systematic/Discretionary Fully systematic, nondiscretionary 
Who developed the strategy: Self 
Automated/Manual execution: Automated 
Avg/min/max holding period: 12 hrs/<lhr/20hrs 
Daily/monthly trading volumes: Approximate max book size 500MM with 50% daily 
turnover 
Have you encountered position limit problems? Max position is based on 3% of traded volume over 
signal duration (5 hr average) 
EDGE 
Specific Edge: Sophisticated and robust statistical analysis for broad 
security universe. Multi-horizon forecasting. Horizons 
chosen to eliminate adverse selection, maximize 
capacity while keeping reasonably high turnover. 
Background/evolution of edge: Ideas developed since early 2009 gradually increasing 
robustness and variation in time scale while retaining 
model parsimony. 
Factors/experience allowing manager to access 
edge: Extensive background in various mathematical statistics 
methods, statistical machine learning and econometrics. 
Development of successful alpha models for other 
product types. Development of optimal portfolio 
execution algorithms, market impact analysis from work 
on the agency side. 
Discuss the persistence of edge: Edge exists over hundreds of securities and in timespan 
of at least 10 years irrespectively of time of the day or 
other calendar effects. Breadth of trading universe 
provides resistance to fluke moves in individual 
securities. 
Method for monitoring ongoing effectiveness of 
edge: Consistency of Sharpe ratio and it's confidence intervals; 
mean and median returns; number of trades; overall 
market volumes; return auto-correlation profile —
appearance of streaks; return empirical PDF skewness 
and excess kurtosis. 
When is the strategy most/least effective: Most effective during higher market volumes, fewer 
number of rapid market regime changes. Least effective 
when volumes are very low and there are frequent 
market regime changes. 
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RISK 
Discuss overall risk management approach: Proactively, main idea is to reduce return adjusted 
portfolio risk, while maintaining capacity. Also keeping 
low single name and industry/sector exposure. 
Reactively, maintain stop losses and analyze model 
performance on ongoing basis. 
Proactive risk management: Broad, Portfolio Sharpe maximizing diversification 
balances risk-return tradeoff. Low sector, industry 
concentration further reduces exposure to risk factors. 
Low percentage exposure per name reduces sensitivity 
to major moves in a given name. Highly liquid names 
(Average ADV 175MM, min ADV 30MM) allows to 
quickly enter and exit positions. 
Reactive risk management: Uses stop losses by name and by portfolio on a given 
day. On a multi-day horizon, strategy edge monitoring 
identifies systemic situations when model performance 
worsens. 
Discuss maximum capital risk per trade, per day, 
per month: Maximum seen drawdown is -4% on a peak-to-trough 
basis, lowest daily draw down is -1.65%, lowest monthly 
return seen is -3.6%. 
Trade level risk management methodology: Stop losses, model predictive ability monitoring 
Portfolio level risk management methodology: Portfolio Sharpe maximization, portfolio stop loss 
Potential regulatory risks: None presently. If Congress decides to impose a 50bp 
transaction fee, that would be an issue. 
Liquidity risks: Flash crash type short term liquidity squeeze. 
How can risk management methodologies fail?: 1. In the event of large shocks even uncorrelated 
securities become concordant so covariance 
matrix based risk management will not be 
adequate. Stop losses will kick in. 
2. Stop losses may fail if there is a line or hardware 
failure. Backups should be in place. 
CAPITAL USAGE 
Discuss actual capital requirements of strategy: Once pipes are tested, real trading can start with as low as 2M 
in capital, trading all instruments to maximize Sharpe. Odd lots 
can be traded without penalties as long as they are executed on 
INET. Over the course of 6-8 months the aim will be to reach 
100MM in trading capital use. From that point strategy is 
further scalable to about 500MM in trading capital in US. Later 
on using Europe and Asia it may be possible to reach 18 in 
trading capital use. 
Address leverage usage, cross-margining, 
gearing associated with strategy: Initial leverage factor should be kept artificially lower in 3-4 
range as it scales and builds up P&L, leverage may reach 8-10 
range. When exposure exceeds 100MM leverage may be scaled 
down again. 
What is the capacity of the current strategy: 500MM 
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Can additional strategies be added to increase 
capacity: 
EXECUTION Adapting to European and Asian markets may do that. 
Specific prime broker/clearer requirements Commission under S mills is preferred. Rapid ability to borrow 
for short sales is a big plus. 
Specific trading platform and connectivity 
requirements Co-location and fast risk checks are preferred. Access to dark 
pools and all lit venues will be important for scaling up. 
Exchange membership requirements: none 
Leverage/margining requirements: Access to leverage in 6-10 range is preferred. 
INVESTMENT RESEARCH 
Describe your overall R&D Process 1. 
2. 
3. 
4. Data collection, cleaning, structuring for optimal 
access 
Security universe selection 
Model implementation in R — this takes advantage of 
cutting edge R packages. 
i. Data preprocessing, identifying data 
idiosyncrasies and transformations. 
ii. Hypothesizing about temporal relationships 
and formulating a model. 
iii. Model refinements, stability analysis, 
identifying key model metrics 
iv. Identifying execution logic for given model. 
v. Back tests using rolling training window on 
chosen data subset, finding optimal 
execution parameters. 
vi. Confirming resulting execution logic with it's 
parameters on a separate (left out) data set. 
Model implementation in C++. 
i. Developing the equivalent of the required R 
numerical methods in C++ 
ii. Matching execution logic and position 
management between R and C++. This serves 
as bug proofing for both R and C++ 
implementation. 
iii. Developing tick level simulator in C++ 
iv. Implementing API agnostic trading interface 
"black box". This way simulation and 
production strategy internals are the same. 
v. Matching C++ and R simulation results. Fixing 
discrepancies based on finding aggregated 
differences, subdividing that to find lower 
level differences and etc until a clear test case 
exhibiting difference emerges and fixing it. 
What are you R&D goals for the next 6, 12, and 1. Reduce execution costs on lit venues using known 
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24 months: pattern of actual trades and selecting optimal 
execution logic for those times. 
2. Incorporate dark venues and develop their heat maps 
3. Apply model to European and Asian markets, including 
model tweaks incorporating different cost structures 
and exchange rules. 
Describe the flow of an investment idea from 
inception to trading: Reading research papers in statistics and econometrics — they 
highlight some of the market phenomena and methods to 
identify them. Discussing things that work with other quants on 
a general and mutually beneficial basis. Synthesizing new ideas 
and thinking of ways to improve based on those inputs. Testing 
resulting ideas in It Also described above under "R&D process" 
Describe your back testing of investments: Described above under "R&D process": using sliding window 
model generation and testing, cross validation. 
Have you published or commissioned any 
research/academic papers? Yes: my early research area was in multivariate complex 
function theory. Lately I focus on combinatorial optimization 
methods. 
PERFORMANCE 
Annual P8d. Realized: 19M (first year projectedt) based on average trading 
capital of 50M (due to initial scaling). 
38%t (unleveraged) 
5.15t 
4% t Annual realized return on capital: 
Realized Sharpe ratio: 
Maximum capital drawdown: 
How can performance be verified: 
How do you expect this performance to differ 
going forward: 2013 volumes appear to be improving along with retail 
investor sentiment which is positive for the strategy 
'Mote: Realized P&L numbers based on live strategies at Tower are based on a different trade horizon 
and therefore not relevant for the current strategy. Presented numbers are based on extensive back 
test, while the model itself is proven in live trading — on a different time horizon. 
EFTA00583293



=== IMAGE DESCRIPTIONS ===
[Image 1] The image shows a document titled "360 Capital Partners Questionnaires for Due Diligence Review." The document appears to be a questionnaire or survey designed to gather information from individuals or entities associated with a company or investment opportunity. It includes sections for personal information, company information, and various questions related to the company's operations, managemen

[Image 2] The image shows a document with text, which appears to be a risk assessment or a report of some sort. The document is titled "Risk Assessment" and includes sections with headings such as "Risk Management," "Capital," and "Risk Factors." There are bullet points under each section with text that seems to discuss various aspects of risk management and capital, including the importance of risk managem

[Image 3] The image shows a document with a table and text. The table has columns with headings such as "Specification," "Description," "Specification," and "Specification." The text below the table appears to be a description or explanation related to the table's content. The document seems to be a technical or instructional piece, possibly related to a product or process. The text is too small to read in 

[Image 4] The image shows a document with a table and text. The table has headings such as "RESEARCH," "DATA COLLECTION," "DATA ANALYSIS," and "DATA INTERPRETATION." There are bullet points under each heading, suggesting a list of items or steps related to a research project or process. The text at the top of the document appears to be a title or heading, but it is not fully visible. The document seems to b

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[Image 6] The image appears to be a document, specifically a questionnaire or a form titled "Capital Partners Questionnaire for Due Diligence Review." It is designed for collecting information from capital partners. The document includes sections with questions such as "Name," "Address," "Phone," "Email," and "Date." There are also fields for "Capital Partner," "Representative," and "Date." The form is stru