Changing Notions of Risk Management in Financial Markets

QuantInsti
Quantitative Learning
Pvt. Ltd.

Rajib Ranjan Borah
Co-Founder & Director,
QuantInsti Quantitative Learning Pvt Ltd
&
iRageCapital Advisory Pvt Ltd
Changing Notions of Risk
Management in Financial
Markets –
Impact of Proliferation of Automated Trading Systems and
Technology on Financial Markets

Table of Contents
• Changing Trends in Trading
• Major Automated Trading Risk Failures
• Changing Trends in Trading Risk Management
• Regulatory requirements
• Q & A

Table of Contents
• Regulatory requirements
• Q & A
Changing Trends in Trading → Major Failures → Changing Trends in Risk Mgmt → Regulations → QA

Trading in the markets
If you have a profitable trading
strategy, then …

strategy, then …
• do it as frequently
(don’t miss any opportunity)
• scale it up
(trade as many financial instruments)
• don’t let emotions affect
(greed & fear: traders’ biggest enemies)

strategy, then …
• scale it up
Computers:
• always at their seats
• respond to opportunities in
microseconds

strategy, then …
• scale it up
Human eye can monitor 10-
15 stocks.
Computers can track
thousands simultaneously

strategy, then …
• scale it up
Computers have no
emotions

strategy, then …
• scale it up
Trading is all about computations and computers do calculations faster

Trading Today
Inevitably, machines have taken
over human beings

Trading shifted from pits …

…to computers

…and even more computers

Trading Landscape changes
This revolution has been fast

Effect of algo-trading
… and this growth has been across
asset classes
Options
FX
Equity

Options
FX
Equity
… and this growth has been across
asset classes

Unfortunately, …. computers don’t
think

Pros and Cons
Trading algorithmically is generally
more profitable

Pros and Cons
more profitable
• Less downtime
• No emotions (Greed & Fear)
• React faster
• Higher scalability
• Accurate and faster calculations

Pros and Cons
more profitable
But …
Systems are getting
more complicated
Traditional trading system

Pros and Cons
more profitable
But…
Systems are getting
more complicated
Increasing
likelihood of errors
Automated trading system

Pros and Cons
Trading algorithmically is more
profitable …
… and more riskier

Major algorithmic trading incidents - I
• Credit Suisse, Nov 2007
– Incident:
• Hundreds of thousands of cancel orders sent to the
exchange
• Orders clogged NYSE and affected trading of 975 stocks
– Reasons:
• Trader implemented code which could change parameters
on clicking on spin button
(without any need for confirmation)
• With each click, orders were cancelled and resent
– Fine/ Losses:
• $150,000 fine

• Infinium Capital, Feb 2010
– Incident:
• 4612 trades on crude oil futures in 24 seconds
– Reasons:
• Strategy was designed to trade energy ETFs on the basis of
crude prices
• Trader configured crude oil futures on the basis of energy
ETFs
• Moreover, RMS was designed on the basis of ETF prices, not
crude prices
– Fine/ Losses:
• $850,000 fine by CME
Major algorithmic trading incidents - II

• Deutsche Bank, June 2010
– Incident:
• Sent orders for 1.24 million Nikkei 225 Futures & 4.82
million Nikkei 225 mini-futures in first few minutes
• More than 10 times normal volume
• Market dropped 1% on orders
– Reasons:
• Pair trade strategy used value of Nikkei ETF to quote Nikkei.
At start of day, there was no price information in Nikkei ETF
(because of a configuration change)
• Error recognized immediately, 99.7% orders cancelled
– Fine/ Losses:
• Forced to close Algorithmic trading desk in Tokyo
Major algorithmic trading incidents - III

• BATS listing, Mar 2012
– Incident:
• On the day of listing, stock price dropped 99%
– Reasons:
• Software bug in newly installed exchange matching
engine - orders placed during auction session became
inaccessible for stocks whose ticker symbols began
with letters A to BFZZZ
– Fine/ Losses:
• IPO withdrawn
Major algorithmic trading incidents - IV

• Knight Capital, Aug 2012
– Incident:
• Traded 154 stocks at bizarre prices (4 million trades for 397
million shares in 45 minutes): alternately bought at higher
prices and sold at lower prices
– Reasons:
• Accidentally installed test software which incorporated an
old piece of code designed 9 years ago
• In one out of 8 production servers, new code was not
installed by a technician
• No process for second technician to review
– Fine/ Losses:
• Trading loss of $460 million in 45 minutes. Fine of $12
million
• Knight Capital had to be rescued by Getco
Major algorithmic trading incidents - V

• Goldman Sachs, Aug 2013
– Incident:
• Traded stock options at very erroneous prices at the
exchange
– Reasons:
• Indication of interests were sent as actual orders to the
exchange
– Fine/ Losses:
• Trading loss of $100 million
Major algorithmic trading incidents - VI

• Tel Aviv Stock Exchange, Aug 2013
– Incident:
• Shares of Israel Corp. country's largest holding
company fell sharply from 167,200 Israeli Shekels to
210 Shekels.
– Reasons:
• Trader wrongly entered Israeli Corp as scrip name
instead of some other firm
– Fine/ Losses:
• All trades cancelled
Major algorithmic trading incidents - VII

• Everbright Securities, Aug 2013
– Incident:
• Rogue algorithm kept buying – index moved up 6%
intraday
• Did not inform regulators, shorted the artificial bubble
– banned from prop trading forever for insider trading
– Reasons:
• Algorithm did not check position limits and kept
sending orders
– Fine/ Losses:
• Banned from prop trading forever for insider trading
Major algorithmic trading incidents -VIII

• HanMag Securities, Dec 2013
– Incident:
• HanMag exercised wrong call and put options
• 36,100 trades in a few minutes
– Reasons:
• Error in automated profit taking trade program
(interchanged puts with calls)
– Fine/ Losses:
• Some firms returned money back to HanMag (Optiver
returned $600k trading profits)
• Eventual loss of 57 billion Korean Won
Major algorithmic trading incidents - IX

• United Airlines mini-flash crash
– Incident:
• On Sep. 7, 2008 United Airlines had a downward price spike
– Reasons:
• Google’s newsbots picked up an old 2002 story about
United Airlines possibly filing for bankruptcy
• News Analytics based automated traders reacted to it
Major algorithmic trading incidents - X

• Dow Jones mini-flash crash
– Incident:
• On Apr 23, 2013 Markets dropped 0.8% momentarily
– Reasons:
• Twitter account of news publisher hacked – false news
of White house explosion
• News Analytics based automated traders reacted to it
Major algorithmic trading incidents - XI

Table of Contents
• Regulatory framework in India
• Q & A

Traditional Risks
Traditionally trading operations
focused on following risks …
• Market Risk
• Credit / Counter-party Risk
• Financial Risk
• Liquidity Risk
• Regulatory Risk

Automated Trading Risks
Automated trading requires
additional focus on
• Market Risk
• Credit / Counter-party Risk
• Financial Risk
• Liquidity Risk
• Operational Risk
• System Risk
• Greater focus on Natural Disaster Risk
• Regulatory Risk (Automated Trading related)

Issues with Algo-Trading
• Orders flow without human control
– Higher reliance on technology
– GIGO (Garbage Input → Garbage Output)
• Before a human can realize (and then respond)
→ tremendous damage would happen already
• Trades happen at such a fast pace
→ positions could become huge in no time
– Real-time monitor of positions, exposures,
regulation checks

Algo-trading system risks
• System and Operational Risks specific to
automated trading can be classified into
the following categories:
– Access
– Consistency
– Quality
– Algorithm
– Technology
– Scalability

• Such System and Operational risks have to
be handled pre-order
– Within the application
– Before generating an order in the Order
Management System
• Moreover, it is pertinent that the trader
understands the internal working of the
black-box

Automated trading platform – system architecture

RISK
Risk
Handled in Methodology
App OM
Access Connectivity to an exchange
goes down
Y Y
Heart-beats
Exchange disconnects you Y Heart-beats
Network issue Y Hardware, Operating System
Consistency Market Data is stale Y Y Time-stamp
Analytics are running in real-
time (huge processing time)
Y
Time-stamp
OM adaptor is responding in
real time
Y
Time-stamp
Quality Market - data is garbled Y Common RMS rule
Loss of liquidity during high-
volatility
Y
Common RMS rule

RISK
Risk
Handled in Methodology
App OM
Algorithmic Margin breached Y Common RMS rule
Exposure limit set by
exchange
Y
Common RMS rule
Risk limits exceeded
Y
Check for acknowledgements
before sending order
Incorrect strategy setting
leading to continual
mistrades
Y Y
PnL fluctuation check
-do- Y Order throttle rate
-do- Y Fat finger settings check
-do- Y Max Value Traded
Incorrect order generation Y Y Price range check
Order throttle Y Exchange reject limit

RISK
Risk
Handled in
Methodology
App OM
Technology Hard disk gets full Independent check
Virus /Trojan Firewall, Anti-virus
System Crash Operating System
Application crash
Y
Heart-beat to check application
Protocol Mismatch Third-party software
compatibility check

RISK
Handled in
Methodology
App OM
Scalability Number of applications &
portfolios that can be handled Y Y
Number of exchanges that can
be connected Y
Number of symbols that can
be handled Y Y
Order of complexity of
computations Y

• Half-yearly system audit conducted only for algorithmic trading
facility
• Members are required to provide following information on NSE-
ENIT:
– details of all algorithmic strategies in the template provided
– auditor certificate
• Audit provides following reports:
– Summary report: Ratings of ‘Strong’, ‘Medium’ or ‘Weak’ on each
broad areas (which is to be submitted to exchange via NSE-ENIT)
– Detailed report
• In case audit report has a rating of Weak, the member is
required to submit an ATR (Action Taken Report) to exchange
• Auditors to provide report on their letter heads:
– List of all strategies approved
Audit Process & Requirements

SEBI’s broad guidelines on Algorithmic Trading
(Circular CIR/MRD/DP/09/2012 dated 30 Mar 2012):
Guideline for exchanges:
• The stock exchange shall have arrangements, procedures
and system capability to manage the load on their
systems in such a manner so as to achieve consistent
response time to all stock brokers. The stock exchange
shall continuously study the performance of its systems
and, if necessary, undertake system up gradation,
including periodic up gradation of its surveillance system,
in order to keep pace with the speed of trade and volume
of data that may arise through algorithmic trading.
SEBI guidelines

• In order to ensure maintenance of orderly trading in the
market, stock exchange shall put in place effective
economic disincentives with regard to high daily order-to-
trade ratio of algorithmic trading orders of the stock
broker. Further, the stock exchange shall put in place
monitoring systems to identify and initiate measures to
impede any possible instances of order flooding by
algorithms.
• The stock exchange may seek details of trading strategies
implemented through algorithmic trading for such
purposes viz. inquiry, surveillance, investigation, etc.
SEBI guidelines

• The stock exchange shall include a report on algorithmic
trading on the stock exchange in the Monthly
Development Report (MDR) submitted to SEBI inter-alia
incorporating turnover details of algorithmic trading,
algorithmic trading as percentage of total trading, number
of stock brokers / clients using algorithmic trading, action
taken in respect of dysfunctional algorithms, status of
grievances, if any, received and processed, etc.
• The stock exchange shall synchronize its system clock
with the atomic clock before the start of market such that
its clock has precision of atleast one microsecond and
accuracy of atleast +/- one millisecond.
SEBI guidelines

• Stock exchange shall ensure that the stock broker shall
provide the facility of algorithmic trading only upon the
prior permission of the stock exchange. Stock exchange
shall subject the systems of the stock broker to initial
conformance tests to ensure that the checks mentioned
below are in place and that the stock broker’s system
facilitate orderly trading and integrity of the securities
market. Further, the stock exchange shall suitably
schedule such conformance tests and thereafter, convey
the outcome of the test to the stock broker.
SEBI guidelines

Guideline to brokers:
• The stock broker, desirous of placing orders generated
using algorithms, shall submit to the respective stock
exchange an undertaking that -
– The stock broker has proper procedures, systems and technical
capability to carry out trading through the use of algorithms.
– The stock broker has procedures and arrangements to safeguard
algorithms from misuse or unauthorized access.
– The stock broker has real-time monitoring systems to identify
algorithms that may not behave as expected. Stock broker shall keep
stock exchange informed of such incidents immediately.
– The stock broker shall maintain logs of all trading activities to
facilitate audit trail. The stock broker shall maintain record of control
parameters, orders, trades and data points emanating from trades
executed through algorithm trading.
– The stock broker shall inform the stock exchange on any
modification or change to the approved algorithms or systems used
for algorithms.
SEBI guidelines

SEBI later laid out additional guidelines pertaining to Audit
(Circular CIR/MRD/DP/16/2013 dated 31 May 2013):
• The stock brokers/ trading members that provide the facility of
algorithmic trading shall subject their algorithmic trading system to a
system audit every six months in order to ensure that the
requirements prescribed by SEBI / stock exchanges with regard to
algorithmic trading are effectively implemented
• Such system audit of algorithmic trading system shall be undertaken
by a system auditor who possesses any of the following
certifications:
– CISA (Certified Information System Auditors) from ISACA;
– DISA (Post Qualification Certification in Information Systems Audit)
from Institute of Chartered Accountants of India (ICAI);
– CISM (Certified Information Securities Manager) from ISACA;
– CISSP (Certified Information Systems Security Professional) from
International Information Systems Security Certification Consortium,
commonly known as (ISC)
SEBI guidelines

• Deficiencies or issues identified during the process of
system audit of trading algorithm / software shall be
reported by the stock broker / trading member to the
stock exchange immediately on completion of the system
audit.
• In case of serious deficiencies / issues or failure of the
stock broker / trading member to take satisfactory
corrective action, the stock exchange shall not allow the
stock broker/ trading member to use the trading software
till deficiencies / issues with the trading software are
rectified and a satisfactory system audit report is
submitted to the stock exchange. Stock exchanges may
also consider imposing suitable penalties in case of failure
of the stock broker/ trading member to take satisfactory
corrective action to its system within the time-period
specified by the stock exchanges.
SEBI guidelines

• The audit process shall broadly cover the following aspects:
– Approved features and system parameters implemented in the
trading system
– Adequacy of input, processing and output controls should be
tested
– Adequacy of the application security should be audited
– Event logging and system monitoring
– Robust Password management standards
– Network management and controls
– Backup systems and procedures
– Business continuity and disaster recovery plan
– Proper Documentation for system processes
– Security features such as access control, network firewalls and
virus protection should be actively managed
Audits

• The stock broker, desirous of placing orders generated using
algorithms, shall satisfy the stock exchange with regard to the
implementation of the following minimum levels of risk controls
at its end -
– Price check
– Quantity check
– Order Value check
– Cumulative Open Order Value check
– Automated Execution check - an algorithm shall account for all
executed, un-executed and unconfirmed orders, placed by it
before releasing further order(s)
– Pre-defined parameters for automatic stoppage in the event of a
runaway situation / execution in a loop
– All algorithmic orders are tagged with a unique identifier provided
by the stock exchange in order to establish audit trail
Audits

• System compliance requirement for CTCL on annual basis:
– Members to submit to the exchange the system audit report every
year (for the year ended Mar 31) after getting the CTCL trading
facility audited from any qualified auditor
– Report to be submitted through NSE-ENIT by April 30
• System compliance requirement for Algorithmic Trading
Facility on half yearly basis:
– Members to submit the System Audit Report for the half year
ended March 31 (i.e. for the period from October 01 to March 31)
and September 30 (i.e. for the period April 01 to September 30),
after getting the Algorithmic trading facility audited from any
qualified auditor
Audit Timelines

• Algorithm to be executed in Mock Trading environment –
logs to be certified by auditor
• Algorithm to be executed in Test market at NSE – logs to
be certified by auditor
• Apply to exchange for strategy demonstration date with
following documents:
– Strategy document
– Risk Management document
– Network Architecture
– Auditor certificates (both Mock market and Test market)
– Application form (signed by director/senior management)
• Algorithm to be demonstrated with exchange
Strategy Approval Process

• After approval from exchange, member applies for trading
ids (NEAT ids)
• NEAT ids converted to CTCL ids for particular vendor.
Vendor of software intimated about ids and confirmation
obtained
• Member uploads location code details (12 digits) along
with dealer details under CTCL ID before commencing
trading
• Member can trade as either PRO or on behalf of CLIENTS.
– For PRO trading, PRO Undertaking, PRO Location
Undertaking must be submitted. PRO enablement should
also be done for the particular trading id.
Strategy Approval Process

RMS for strategy approval
RMS Description
Manual Trading
disabled
Manual orders are disabled for auto-trading systems
Trade Price
Protection Limit
Order should be within x% of last price
Quantity Freeze
Limit
For each instrument an order size freeze limit is set
Price Range Check Order should not breach the circuit limit (daily price range) of an
instrument
FII restricted list FIIs cannot trade in a select set of stocks (RBI directed)
Market Wide
Protection Limit
Cannot trade derivatives to increase Open Interest beyond a threshold
Shares available
for selling
Overnight long position that is available per share for selling
Automated
Trading enabled
Automated trading to be enabled for a select list of instruments only
Index change
check
Cannot send buy orders if Index moves up beyond a point. Likewise for
sell orders

RMS Description
Client Position
Limit
Maximum position that a client can have in a particular stock
Margin Limit If a threshold of the available margin is reached, then the application
should not send orders to increase the position further
Position Value
Check
Net Position value per instrument
Order Value Max Order Value
RMS for strategy approval

Table of Contents
• Regulatory framework in India
• Q & A

Contacts
For 4-month Executive Program in Algorithmic Trading:
contact@quantinsti.com
E-PAT: 4 month weekend online program (3hrs every Sat + Sun)
• Statistics
• Quant Strategies
• Technology (programming on algorithmic trading platform)
For algorithmic trading advisory: contact@iragecapital.com
To reach me directly: rajib.borah@iragecapital.com

E-PAT
Statistics and
Econometrics
Financial Computing &
Technology
Algorithmic &
Quantitative Trading
QI’s E-PAT course

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
E-PAT course structure - module I
Basic Statistics
Advanced Statistics
Time Series Analysis
 Probability and Distribution
 Statistical Inference
 Linear Regression
 Correlation vs. Co-integration
 ARIMA, ARCH-GARCH Models
 Multiple Regression
 Stochastic Math
 Causality
 Forecasting

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
E-PAT course structure - module II
Programming
Technology for Algorithmic
Trading
Statistical Tools
 Intro to Programming
Language(s)
 Programming on Algorithmic
Trading Platforms
 System Architecture
 Understanding an Algorithmic
Trading Platform
 Handling HFT Data
 Excel & VBA
 Financial Modeling using R
 Using R & Excel for Back-testing

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
E-PAT course structure - module III
Trading Strategies
Derivatives & Market
Microstructure
Managing Algo Operations
 Statistical Arbitrage
 Market Making Strategies
 Execution Strategies
 Forecasting & AI Based Strategies
 Pair Trading Strategies
 Trend following Strategies
 Option Pricing Model
 Dispersion Trading
 Risk Management using Higher
Order Greeks
 Option Portfolio Management
 Order Book Dynamics
 Market Microstructure
 Hardware & Network
 Regulatory Framework
 Exchange Infrastructure &
Financial Planning (Costing)
 Risk Management in Automated
systems
 Performance Evaluation &
Portfolio Management

E-PAT
Statistics and
Econometrics
Technology
Algorithmic &
Project work
E-PAT course structure - project

Copyright © 2013 by QuantInsti Quantitative Learning Private Limited.
Although great care has been taken to ensure accuracy of the information
in this presentation – however the author (and QuantInsti) accepts no
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consequences of any actions taken on the basis of the information
provided.
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Prior approval from QuantInsti is necessary before usage of this
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Disclaimer

Risk Management Process
• Phase 1: Setting risk management
structure & policies
• Dedicated risk department
• Completely cut off from trading
department
• Full autonomy & powers to risk
department
• Approval process for each new
product and operation introduced
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Phase 2: Identifying sources of risk
• Market Risks
• Credit / Counter-party Risks
• Financing Risks
• Operational Risks (Systems,
Mechanical, Criminal)
• Regulatory Risks
• Liquidity Risks (Exogenous &
endogenous)
• Natural disasters, political,
terrorism, etc
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Phase 3: Evaluating risk components
• Market Risks :
• Sensitivity Analysis
• Total Greeks, Dividend, Currency
exposures
• What-if scenario analyses
• VaR analysis
• Stress tests
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Basel II IRB method
(Internal Rating Based Method)
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Financing Risk
Probability of downgrade * interest
rate hike * Size of portfolio
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Regulatory Risk
Probabilities of new Regulations- Is
estimated from News Analysis &
Historical Data
Examples…
• Short Selling Ban
• Margin Increase
• Taxes Introduced
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

Mechanical, Criminal)
• Robustness of a System
• System Load handling capacity
• Maximum order flow before
system detects failure
• Maximum leeway in error while
setting parameters
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Liquidity Risks
• Liquidity adjusted VaR
L-VaR = VaR + Liquidty Adjusted
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Natural Disaster, Political Risk,
Terrorism
• Risk v/s Uncertainty
• News Analysis
Have the potential to wipeout
portfolios
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Phase 4: Setting risk limits
• Market Risks :
• Total cash exposure
• Exposure to geography
• Exposure to sector
• Exposure to asset class
• Exposure to assignment /
delivery risks (settlement risks)
• Settlement Type (future vs
cash)
• Exposure to interest rates
• Exposure to exchange rates
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Maximum exposure to any
counter-party
• Maximum exposure per credit
rating level
• Financing Risks
• Maximum amount borrowed per
counter-party
• Repayment period for loans
• Rho exposure
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

Mechanical)
• Max exposure per strategy
• Max orders per second
• Max orders in a day
• Max exposure per application
• PnL fluctuation per application
• Price Range check
• Max order size
• Max Value Traded
• Net Value of portfolio
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Operational Risks (Criminal/Fraud/
Theft, etc)
• Access Control
• Transparency of operations
• Rotation of team members
• Audit (internal & external)
• Centralized PnL reconciliation
• Independent verification of
price to pricing models
• Online Infiltration & Virus
Protection
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Liquidity Risks
• Maximum exposure per
instruments of each liquidity
category
• Total exposure per liquidity
category
• Natural disasters
• Score-card approach
• Similar to one used By
Insurance/ Actuaries
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

• Phase 5: Designing systems with
strict adherence to risk controls
• Centralized system which
summarizes net position &
exposure
• Asset classes, Interest rates,
Exchange rates, Volatility,
Dividends, Counter parties
• What if Analysis
• Centralized control of all trading
operation
• Pre trade controls
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5

Changing Notions of Risk Management in Financial Markets

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