Changing Notions of Risk Management in Financial Markets

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

© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
Table of Contents
• Changing Trends in Trading
• Major Automated Trading Risk Failures
• Changing Trends in Trading Risk Management
• Regulatory framework in India
• Risk Management Process

Trading in the markets
If you have a profitable trading strategy, then …
1) Do it as frequently
Don’t miss any opportunity
Computers are always at their seats
Respond to opportunities in microseconds
3) Don’t let emotions
affect
Greed & fear are traders’ biggest enemies
Computers have no emotions
2) Scale it up
Trade as many financial instruments
Human eye can monitor 10-15 stocks
Computers can track thousands
simultaneously
Trading is all about
computations and computers
do calculations faster.

Trading Today
… and even more computers
Trading shifted from pits …
… to computers
Inevitably, machines have taken
over human beings
1 2
43

Effect of Algo-Trading
Options
FX
Equity

India woke up recently
Algorithmic trading is in nascent stage;
and is picking steam super-fast

Pros and Cons
Trading algorithmically is generally more profitable
Pros
• Less downtime
• No emotions (Greed & Fear)
• React faster
• Higher scalability
• Accurate and faster
calculations
Cons
• Complicated systems
• Increasing errors
• 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 over 900 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 mini-Nikkei to quote
Nikkei. At start of day, there was no liquidity in mini-Nikkei
• 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:
• Trader wrongly entered Israeli Corp as scrip name
instead of some other firm
– 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
– 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

• United Airlines
– 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

Trading Risks
Traditionally trading
operations focused
on following risks
• Market Risk
• Credit / Counter-party Risk
• Financial Risk
• Liquidity Risk
• Regulatory Risk
Automated trading
requires additional
focus on
• 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

• 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.
• 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.
• 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

Table of Contents
• Changing Trends in Trading
• Changing Trends in Trading Risk Management
• Major Automated Trading Risk Failures
• Regulatory framework in India
• Risk Management Process

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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