Ram Ahluwalia How to Diligence a Quant Fund

1. Winged Foot Capital, LLC
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How to diligence a quant strategy
Ram Ahluwalia, CFA

2. Winged Foot Capital
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Motivation

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Agenda
 Framework
− What is the right framework for evaluating quantitative investment
strategies?
− How do you evaluate a manager that is reluctant to open the blackbox?
− Is the manager generating alpha, a risk premium, or simply lucky?
 Nuts & Bolts
− What is the anatomy of a statistical arbitrage and HFT strategy?
− What are the tools and practical tips you need to know?
 Applied Case Studies
− Volatility Trading & a High Frequency Trader

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Framework: Quant strategies are highly differentiated in sources of risk and
return
Strategy Beta Exotic Beta Factor models Market (Risk)-
Neutral
HFT
Example • 60/40 equities
and fixed income
• HF replication
• Endowment-style
investing
• Risk parity
• Long value,
short growth
• Equity market-
neutral
• Market-making
Return sources • Risk premium • Risk premiums
− Equity market
− Credit / spreads
− Currency
− Volatility
− Duration
• Factor timing
• Factor risks:
− Value
− Momentum
− Mean
reversion
− Size
− Quality
• Liquidity
provision
• Information
dissemination
• Arbitrage
• Bid-ask spread
capture
• Order anticipation
• Rebate capture
Risk • Systematic • Systematic
(diversified)
• Factor risk • Idiosyncratic • Idiosyncratic
Horizon • Many years • Quarters to years • Days to months • Days to weeks • Seconds to
intraday
Sharpe Ratios • -.3 to .3 • .5 to 1.5 • .5 to 3 • 1 to 3 • 5 to 15
Return/Trade • -50% to 50% • -25% to 25% • Up to 5% • Up to 250 bps • 10 mils to 10 bps
Capacity • High • High • Medium • Low • Very low
Ann. Turnover • < 1x • 1 to 2x • 1 to 5x • 1 to 250x • 250x & beyond
Fees • Low • Low • Medium • Medium • High

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Framework: The Fundamental Law of Active Management
Ex-Ante manager value-added is the product of the manager’s skill & breadth
 Published by Richard Grinold*
 The Fundamental Law of Active Management
relates 3 variables: Information Ratio (IR), Skill
(IC), and Breadth
 Lay interpretation: A manager’s performance is
based on of their batting average, and how
many times they get at bat
 Some manager’s may be lucky so we want to
decompose the manager’s performance using
the right-hand side of the equation
See “Active Portfolio Management”, 2nd. Edition for more information
𝐼𝑅 = 𝐼𝐶 ∗ 𝐵𝑟𝑒𝑎𝑑𝑡ℎ
 Breadth Increases
− # of independent bets
− size of the universe
− short-term horizons
− and number of
strategies
 Skill / Edge
is measured
quantitatively
& qualitatively
 Information
Ratio (IR)
measures the
manager’s
value-added.
IR > 1.0 is
considered
top-decile

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Skills can be measured qualitatively. Quants may exploit a structural inefficiency,
process information faster, or offer liquidity to distressed buyers/sellers
Qualitative Examples Description
• Time-Zone Arbitrage • Fund domiciled in one time-zone allows purchases/sales at a fixed price at end of trading day in
local market, while reference basket is still trading in foreign market
• Opening & Closing auction
inefficiencies
• Market rules and order imbalances can create small arbitrage opportunities for nimble players
• FX market making • Market maker “last look” rule offers right to reject trade rather than provide liquidity
• Internalization engines /
Dark pools
• Market makers inside internalizations engine have first-look at all order flow and can route
undesirable order flow to an exchange, or “pre-position” ahead of client orders
• Insurance underwriting • Un-economically motivated buyers & sellers willing to take on negative expected value trades
• Distressed sellers / buyers • Sellers demanding liquidity sell at discounts to fair value (ex: TARP auctions). Buyers seeking
safety pay premiums to fair value (ex: T-bills trading above par during Oct ‘08)
• Fund structure arbitrage • Fund structure mandates create inefficiencies in the “crossover” market between investment &
high-yield debt
• OAS spread capture • Firms with deep pre-payment and credit modeling capabilities can capture an option-adjusted
spread embedded in RMBS
• Embedded options • Embedded options (ex: survivor’s options in life insurance, trigger options in structured credit, or
the option-adjusted spread in RMBS) can be mispriced
• Analytics • Opaque and complex securities such as structured credit are difficult for most players to value
accurately
• Latency • Superior speed allows some nimbler firms to minimize adverse selection and market-make or
arbitrage more aggressively

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Framework: A relative value approach (i.e. hedging) is critical to controlling
exposure to risk factors and maximizing risk-adjusted returns
𝜇𝑚𝑎𝑛𝑎𝑔𝑒𝑟 − 𝑟𝑓
𝜎
Ex-post Sharpe ratio:
w ∗ ∝ − 𝑟𝑓
wΣw𝑇
Ex-ante Sharpe ratio is
maximized by solving for
weights in an optimizer
 The ex-post sharpe ratio is measured
using the manger’s realized time-series
of returns
 A quant seeks to maximize their ex-ante
Sharpe ratio based on their expectations
of security returns
 Turns out that is considerably easier to
model risk (i.e. the correlation and
volatility relationships across securities)
that it is to estimate expected returns
 Therefore minimizing the risk exposures in
a strategy is a substantially easier way to
improve the sharpe ratio
 Look for managers that have zero net
directional exposure (i.e. dollar-neutral,
beta-neutral, factor-neutral, style neutral)
w:
∝:
rf:
Σ:
weight
alpha
Risk-free rate
Risk model (covariance matrix)

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Framework: Managers that strictly control their beta exposures tend to have
muted returns without leverage
Expected
Portfolio
Return
Risk 𝜎
Manager’s
efficient frontier
without leverage
B: Manager’s
highest sharpe
A: Manager
without risk
hedging
Key Points
 Beta’s such as the equity risk
premium, credit risk, volatility risk,
or interest rate offer high long-run
expected returns for passive
investors
 Betas are difficult to predict and
cheap to own (why pay 2/20 for
exposure to them?)
 Manager can choose point A which
has higher returns for taking
uncontrolled systematic risks
 Manager can choose point B which
hedges out residual beta risks and
has a high risk-adjusted
performance, however, their returns
are relatively muted
 Manager can choose to operate
higher on the efficient frontier but
they sacrifice risk-adjusted
performance

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Framework: Leverage allows the manager to operate on a new efficient frontier
that efficiently trades-off risk and return
Expected
Portfolio
Return
Risk 𝜎
Manager’s new
efficient frontier
with leverage
B: Manager’s
highest sharpe
A: Manager
without risk
hedging
Key Points
 Leverage allows a manager to
maximize both risk-adjusted
returns and calibrate to an
investor’s desired level of risk
 All points on the new efficient
frontier dominate the efficient
frontier without leverage
 Leverage obviates the need for the
manager to take on uncontrolled
beta risks to increase returns
 All points on the new efficient
frontier efficiently trade-off risk and
return

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Framework: Sourcing managers based on principles of edge, breadth, and
relative value reduces selection bias
Criteria Rationale
 Sustainable edge  Produces a positive expectancy (high information coefficient)
 Market inefficiency  Capacity-constrained strategy, untrafficked markets, structural
inefficiencies with un-economic buyers and sellers
 Large universe size  Increases breadth and scope for diversification
 High turnover  Implies greater breadth, liquidity, lessens MTM issues, and easier
to measure statistical significance
 Relative value / Strict
hedging
 Rewards alpha, avoids systematic risk. Minimize net directional
exposure
 Small position sizes  Risk profile consists of small (uncorrelated) idiosyncratic risks
Edge
Relative
Value
Breadth
Results in managers with attractive risk-adjusted and
uncorrelated returns

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Anatomy of a Statistical Arbitrage Model
The core components are the alpha signal, risk model, and portfolio construction
Hypothesis
Generation
Data
Prep
&
Analysis
Model
/
Rules
Specification
Refresh
Model
Parameters
Generate
Alpha
Signals
Refine
Signals
Signal
Weighting
Optimized
Portfolio
Goals &
Constraints
Risk Model
Implement
Portfolio
Existing
Positions
Security
Returns
Alpha Research Generate Signals Construct Portfolio Report
Active
Risk
Performance
Model Quality
Trade List
• What are the data inputs
into the model?
• What is the on-going
research process?
• How is the data cleansed?
• Outlier treatment?
• Is the model rules-based or is it a forecast?
• What is the functional form of the model(s)?
• What is the half-life of the model?
• What is the sampling procedure for the model?
• What is the signal weighting process?
• What is the utility function that is
optimized?
• How are transaction costs minimized?
• How is beta exposure minimized?
• What are the sector and risk exposure
over time?
•Return
attribution?
•Risk
attribution?
•Sample
reporting?
Marginal Risk
cut-offs
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Systems

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Anatomy of a Statistical Arbitrage Model
Take an evidence-based approach to assessing the manager
Outside the Blackbox
• Is the strategy based on faster information processing, liquidity provision, or risk premium
harvesting?
• What is the evidence showing the relationship between the inputs and the dependent
variable? Is the relationship time-varying? Why does the relationship exist?
• What environments does the model do best in (is it related to volatility, cross-sectional
dispersion, shocks, etc.)
Inside the Blackbox
• What is the choice of dependent variable and independent variables? What is the
functional form of the model (multiple regression, logistic, non-parametric, etc.)
• How is look-ahead bias avoided in the model generation process (i.e. walk-forwards, hold-
out population, etc.)
• What are the model performance statistics (R2, spearman correlation coefficient, hit-rate,
gains charts, etc.)?

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How HFT Works – 1. Liquidity provision (i.e. Bid-Ask spread & Rebate Capture)
HFT relies on low latency, queue positioning, and order routing to offer liquidity
Price
Nasdaq
T=1
Price
Nasdaq
T=2
Market sell orders
arrives (2 shares)
Price
• Orange has best
queue position in
inside bid, and 2nd
queue position on
inside ask
• A new market
order to sell 2
shares arrived
• Orange is filled
• A new market order
to buy 4 shares
arrives
• Orange is 2nd in
queue but still filled
Nasdaq
T=3
Market buy orders
arrives (4 shares)
Most HFTs run a market-
making strategy
They compete on time & price
to provide liquidity
As long as their buys and sells
are well simultaneously
matched, the HFT profit is :
(# of shares traded) * (ASKPrice – BidPrice)
The HFT bears inventory risk
while waiting to for both legs
of the trade to complete
HFTs experience adverse
selection (stuck holding
inventory that is moving
against them)
Strategy

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How HFT Works – 2. Arbitrage of prices across different trading venues
• HFT may also exploit mispricings across exchanges and dark pools, or differences in cash vs. futures markets
• Mispricings are caused by differing rates of information dissemination, as well as aggressive non-HFT trading
Nasdaq ARCA BATS-Y Edge-X
Price
Price
Price
Price

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How HFT Works – 3. Order Anticipation
• An HFT can exploit a poorly
designed execution algorithm
such as TWAP or VWAP
A large institutional buy
order divided is divided
into child orders to
minimize market impact (i.e.
TWAP algo)
100
shares
100
shares
100
shares
100
shares
100
shares
100
shares
100
shares
. . . . .
3:30:00 3:30:30 3:31:00 3:31:30 3:32:00 3:32:30 4:00:00
• The HFT will rapidly
cancel standing sell-limit
orders to avoid the
inventory risk
• The HFT can compete
aggressively and take liquidity
or “pre-position” ahead of
orders

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HFT Due Diligence – Check that the manager has appropriate portfolio & trade-
level risk checks to avoid “fat finger” mistakes
• Pre-trade risk validations
• Max buying power per symbol
• Max notional value per order
• Max loss per account
• Auto cancel thresholds
• Maximum order size
• Maximum position size
• P&L loss thresholds
• Short checking
• Odd lot allowances / restrictions

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Due Diligence Checklist - Organize your quant diligence efforts around edge,
breadth, and risk in addition to your standard diligence procedures
Edge
• How latency sensitive is the strategy?
• Is the average return-per-trade compressing as a function of time? Has the alpha model decayed over time?
• Return attribution – liquidity providing vs. taking? Rebate capture? Losses to slippage?
• What technology is driving the latency advantage? How much cap-ex is required?
• Co-location, FPGA, Dark-fiber, Microwave, Laser
Breadth / Universe
• Number of strategies?
• Number of symbols traded? Trades per day? Turnover? Average holding period?
Risk / Hedging
• What portfolio & trade-level risk checks are in place?
• What hedging procedures are used to minimize market exposure?
• Analyze anomalies in the daily returns provided by an independent administrator
• Is there any auto-correlation in the daily or monthly returns?
• Do trade statements confirm the stated strategy?
• What failover processes are in place in case the software, hardware, or network layer fail?
• Performance during key events (Aug 2007, Lehman failure, Oct ’11 Knight incident, flash crash, etc.)
• How does the strategy perform over various environments (levels of VIX, financial distress, etc.)?
• Drawdown analysis
• Overnight risk?
• How is leverage managed?

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Quantitative analysis - Request the following information from the manager (in
addition to your traditional due diligence request)
Risk Reporting
• Time-series of daily returns furnished by administrator
• Broker-provided trade statements for select periods showing rebates & transaction details
• Return attribution – by strategy, asset class, long/short, sector
• Risk decomposition
• Time-series reports: sector weights, net directional exposure, rolling analysis
• Correlation analyses (up/down capture days, scatter plots vs. risk index, etc.)
Rolling Sharpe Ratio analysis
Over-under Analysis vs. Primary Risk Index

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Quantitative analysis - Daily returns can generate a wealth of reporting and
reveal how the strategy performs during periods of market stress
Distribution of Returns
Plot of manager returns vs. Benchmark sorting from worst-to-best
Benchmark
Manager
Reporting
Control charts
Risk statistics
VaR / CVaR analysis
Performance statistics
CAPM statistics
Up-down capture ratios
Distribution of returns
Leverage over time
Sector-weights over time
Rolling sharpe ratio analysis
Drawdown recovery speeds
Conditional correlation test

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Qualitative analysis - strategy descriptions in the manager’s marketing materials
can reveal strengths or points of concern
Manager Statements Consistent w/ Framework Manager Statements Inconsistent w/ Framework
Manager A – Global Macro
• “…models monitor global financial markets in
search of securities with absolute or relative prices
that appear inconsistent with their fundamental
value”
• “Dislocations tend to arise as large, institutional
flows dominate price action… flows are often driven
by non-economic motivations such as differing
laws, regulations, and investment objectives”
Manager C – L/S Equity and credit
• “Search for asset classes and to identify attractive
value investment that are consistent with our
economic outlook”
• “Concentrate capital – good ideas are hard to
find – make them meaningful”
• “Downside protection – buy portfolio and tail
insurance to protect against systemic risk when
risk is mispriced”
Manager B – CTA
• “Our short-term mean reversion market neutral
model trades spreads rather than the outright
markets for two reasons - a) they possess a lot
more mean-reversion than their individual
constituents, and b) they have “better” risk
properties (less tail risk)”
• “Trading frequency is from minutes to several
days”
Manager D – Systematic Options Trader
• “Strategy designed to profit from implied vol
changes and/or implied vs. realized vol
differentials”
• “Common trades include long straddles /
strangles, long calendar spreads, skew trades”

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Case-Study: Quantitative Option Volatility Trader
Would you invest in this manager?
Strategy
− Edge: Net seller of option premium - more than
80% of options expires worthless and there are
no natural sellers
− Breadth: 50 most liquid global options (liquid
front-month contracts) on equity indices,
commodities, interest rates, and FX
− Relative value: Long/short volatility
Risk management
− Delta-neutral
− Each position < 2%. Stop loss at 15% drawdown
− Diversified by asset class, geography, option
maturity, and option strike
Summary
% Q1 Q2 Q3 Q4 YTD +/- S&P
2005 6.47 4.85 3.05 -.37 14.7 8.0
2006 4.95 -3.21 3.71 6.61 12.06 3.95
2007 .93 6.46 -.25 5.17 12.31 12.34
Performance
Trades positive = 80%
Months positive = 85%
Max drawdown = 7%
Correlation to SPX: .28
Correlation to Vix: (.26)
Sharpe ratio > 2.2
High turnover = 12x
Out-performs S&P 500 each year since inception
(2004)

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Case-Study: Quantitative Option Volatility Trader
The manager experienced severe losses in 2008
Summary
% Q1 Q2 Q3 Q4 YTD +/- S&P
2005 6.47 4.85 3.05 -.37 14.7 8.0
2006 4.95 -3.21 3.71 6.61 12.06 3.95
2007 .93 6.46 -.25 5.17 12.31 12.34
Performance
Trades positive = 80%
Months positive = 85%
Max drawdown = 7%
Correlation to SPX: .28
Correlation to Vix: (.26)
Sharpe ratio > 2.2
High turnover = 12x
Out-performs S&P 500 each year
Daily returns show
strategy has a
conditional correlation to
S&P
Strategy
− Edge: Net seller of option premium - more than
80% of options expires worthless and there are
no natural sellers
− Breadth: 50 most liquid global options (liquid
front-month contracts) on equity indices,
commodities, interest rates, and FX
− Relative value: Long/short volatility
Risk management
− Delta-neutral
− Each position < 2%. Stop loss at 15% drawdown
− Diversified by asset class, geography, option
maturity, and option strike

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Case-Study: High-Frequency trading strategy
Would you invest in this manager?
 Strategy
− Edge: 25 factors include price, volume, sentiment,
option activity, cash flow, quality. Each factor has
a T-statistic of 3.0 and Sharpe ratio > 1
− Average return .6 bps (< .01 / share)
 Breadth:
− Universe of 3,200 equities. Multiple strategies
(index re-balancing, corporate actions,
momentum, etc.)
− 500 to 600 positions at a time. Holding period 0 to
25 days / 700 trades per day
 RV & Risk control: Market-neutral, positions < .5%
Summary Performance
Days positive = 68%, Months positive = 92%
Max drawdown = 2%
Sharpe ratio ranges from 4 to 8
Extremely high turnover
Due Diligence notes
− Co-located at exchange
− Uses risk model to hedge out systematic risk
− Performs better during higher-volatility
% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD
2011 16.1 22.6 8.6 2.8 4.0 5.8 7.4 7.7 6.1 9.8 1.6 -1.2 91.2
2012 -.8 5.9 4.0 2.4 1.4 1.6 .6 3.3 3.4 2.2 4.6 2.8 31.4
Monthly Returns
Returns appear to
be declining – is it
the strategy or
environment?

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Portfolio Construction
Low-Vol
environment
Risk-Off Risk-On
 Some equity hedged strategies underperform
during financial distress (ex: ‘merger arbitrage’).
These and other strategies are implicitly short
volatility
− Rationale: The spread between take-over
price and traded price widens as the cost-
of-capital increases, and leverage available
decreases
 Other strategies are implicitly long volatility
– they perform better when there is dislocation
and panic (i.e. HFT, time-zone arbitrage, global
macro, etc.)
 Successful portfolio construction requires
blending managers in a balanced way
 Avoid mean-variance optimization since
correlations are time-varying
 Consider risk-based weighting or robust
optimization so that no single manager
dominates the return signal
Key Points

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Lessons Learned
 The quants universe is highly differentiated
 The Fundamental Law of Active Management provides a framework for assessing a manager’s
value-added in terms of edge & breadth. This also avoids selection bias by starting with first
principles rather than merely screening for high sharpe ratio strategies
 Quants have a variety of sustainable edges – structural inefficiencies, faster information
processing, or transacting with distressed buyers/sellers
 A relative value approach where systematic risk is hedged out, the manager has a positive
expectancy, and no single trade dominates produces attractive risk-adjusted returns
 The core components to evaluate in a statistical arbitrage model are the alpha model, risk model,
and portfolio construction process
 HFTs generate returns thru market-making, arbitraging across trading venues, and order
anticipation
 Combine quantitative & qualitative analysis to build a portfolio of high-performing managers

27. Winged Foot Capital
Winged Foot Capital
- 26 -
Q & A

28. Winged Foot Capital
Winged Foot Capital
- 27 -
Market Timing – A Choose Your Own Adventure game
• A nuclear
state
(Russia) will
never
default
• We are in
a Dotcom
Bubble
• We are in
a New
Economy
• Bet on
housing
boom
• Short dollar
• “Subprime is
contained”
• Buy
legacy
CDOs
• BAC is too
big to fail (but
not LEH)
• We are in a
central bank
driven
reflationary
cycle
• We are in a
deleveraging
cycle
• Emerging
markets
have
contagion
risk
• Short
Japanese
bonds
?
?
• We are in a
“New
Normal”
?