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





• 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 …




• 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)







Computers:

• always at their seats

• respond to opportunities in

microseconds







Human eye can monitor 10-

15 stocks.

Computers can track

thousands simultaneously







Computers have no

emotions







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


• Less downtime

• No emotions (Greed & Fear)

• React faster

• Higher scalability

• Accurate and faster calculations


Pros and Cons


But …

Systems are getting

more complicated

Traditional trading system


Pros and Cons


But…

Systems are getting

more complicated

Increasing

likelihood of errors

Automated trading system


Pros and Cons

Trading algorithmically is more profitable …

… and more riskier


Table of Contents





• Q & A


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


Algo-trading system risks • 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


Algo-trading system risks

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



Table of Contents





• Q & A


• 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:

[email protected]

E-PAT: 4 month weekend online program (3hrs every Sat + Sun)

• Statistics

• Quant Strategies

• Technology (programming on algorithmic trading platform)

For algorithmic trading advisory: [email protected]

To reach me directly: [email protected]


mailto:[email protected]



E-PAT

Statistics and Econometrics

Financial Computing & Technology

Algorithmic & Quantitative Trading

QI’s E-PAT course


E-PAT




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




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




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




Project work

E-PAT course structure - project


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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

presentation for educational and (or) commercial purposes.

This document provides an outline of a presentation and is incomplete

without the accompanying oral commentary and discussion.

Disclaimer


Addendum

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



• Operational Risks (Systems, 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



• Operational Risks (Systems, 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

Economy & Finance