Compliance and Risk Management 

in 21st Century

Erozan Kurtas

The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private 

publication or statements by any of its employees. The views expressed herein are those of the author and 

do not necessarily reflect the views of the Commission or of the author's colleagues upon the staff of the 

Commission.
Convergence of disciplines: Rise of the Algorithms

Algorithmic/Computerized 

Trading

Computing Power Storage Capacity

Internet

Broadband

Smart Routers

High speed Networks

GLOBALIZATION

Connectivity of the markets

24/7 News

Global social networks

Computer Science

Statistics

Data Mining

Artificial Intelligence

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Econometrics

Financial Engineering

Behavioral Economics 

ECN/ATS

DMA

Electronic Market Making

Dark Pools

Tick Sizes

Co-Location

Erozan Kurtas
Algorithms come in many fashion

UHFT

HFT

Execution Tactics

VWAP, TWAP

Alpha Capture

Analyst surveys

News Aggregators

Technical 

Fundamental 

Event Based 

Market Making 

Statistical Arbitrage 

Time series Analysis 

Neural Networks 

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Latency Arbitrage

Fees/Rebates

Maker/Taker

Adaptive/Learning Systems

Pattern Recognition

Erozan Kurtas
Alpha Capture Systems

• Started with Marshall Wace in 2001 in UK

• Web based systems to capture trading 

ideas coming from contributors: sales 

desks, analysts

• More than buy or sell ideas

• Becoming more and more common place

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Trade Idea Flow: Old Model

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SELL SIDE BUY SIDE

IDEAS

• Decentralized

• Minimal Computational Power

• Minimal use of Historical Data

• No Statistical Analysis

Erozan Kurtas
Alpha Capture Systems: New Model

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Computation Power

Real Time

Historical Data

Centralized

Sophisticated Statistics

Erozan Kurtas
A Few of The Risk Factors for Alpha Capture

• Material non-public information

• Compliance requirements both at the contributing and receiving firms

• Compensation Structure

• Difficulty in establishing cause-effect relationship between contributor 

ideas and executed trades

• Regulation arbitrage: lack of strong controls in overseas markets

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High Frequency Trading

Ultra High Frequency 1 millisecond to 1 second

High Frequency 1 second to minutes

“Slow” Minutes to hours

Research By Tabb Group

US 21% in 2005 56% in 2010

Europe 9% in 2007 25-35% in 2010

Asia 10-30% in 2010

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High Frequency Trading: Good or Bad?

STRATEGIES

•Liquidity Providing

• Trading the Tape 

• Statistical Trading

• Market Making

• Momentum Trading 

• Technical Trading 

•OTHER:? ? ? 

SUPPORTERS

•Increases Liquidity

• Makes markets more efficient

• Decreases Volatility 

• Natural evolution of markets

• Cancellations are natural

• Does not need more regulation 

OPPONENTS

• Liquidity Fleeting: not real, not there when needed

• Distorts the markets: self similarity, correlation

• Increases Volatility 

• Privileged access to markets, data

• Huge number of cancellations to manipulate markets

• Significant Number of ODD LOT trading

• Needs more regulation 

• ACADEMIC RESEARCH

• J. Brogaard, July 2010: HFT and its impact on market quality. 

• J. Hasbrouck and G. Saar, Oct 2010: Low-Latency Trading

• R. Smith, June 2010: Is HFT inducing changes in market microstructure and dynamics?

• R. Cont, September 2011: Statistical Modeling of High-Frequency Financial Data

• M. O’Hara, July 2011: What’s Not There: The Odd-Lot Bias in TAQ Data

JURY IS OUT ON HFT

HFT IMPACT IS REAL

RESEARCH INCONCLUSIVE SO FAR

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Out On The Field

What We Hear: Nice and Orderly

Data Sources

Data Normalization

Forecasting 

Models

Risk Models

Portfolio 

Generator

Order 

Management 

and Execution 

Tactics

Exchanges

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REALITY IS DIFFERENT: What We See

Land of many languages: Lost in Translation

Alpha $$$

Risk Metrics, VaR, Stress 

Tests, Cross Business 

Impact

C++, Python, Java, 

Matlab, FPGA

Optimization, 

Information Ratio, 

Covariance Matrix

Dodd-Frank

Cross Trades, wash trades, 

churning, layering ...

Micro second latency

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Interaction of Algorithms

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Compliance and Risk Management 

Challenges

• Proper documentation of models, systems and processes

• Proper testing of the models and systems:

– Can they start or accelerate market events?

– How do they behave individually and in aggregate under stress environments?

– How do the changes in models tracked? 

• Most compliance personnel do not have the background to understand, monitor 

or test the models

• Models and Systems evolve faster than Risk or Compliance processes

• In my opinion, traditional compliance need to become Quantitative 

Compliance: Financial Engineering requires Compliance 

Engineering.

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A Robust Risk and Compliance Process

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DATA SOURCES

TECHNOLOGY

RISK TARGETING

QUANTITATIVE

ANALYSIS

FINANCIAL

ANALYSIS

LEGAL/COMPLIANCE

ANALYSIS

RESULT

DATA

T

E

C

H

E

N

A

B

L

E

R

S