EXTENT-2015: Prognoz Market Surveillance

Prognoz Market Surveillance

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PROGNOZ TODAY

1 5 A wide range of standard products for public, corporate, and financial sectors

Offices in 7 countries, including USA, Canada, Belgium, China, CIS

Own training center with strong methodological support of key projects

Support of partner professional community around the world

More than 20 years experience in the IT and business analytics market

More than 1,500 successful implementations for 550 customers in 70 countries worldwide

Leading company in international ratings related to business analytics and custom software development

In-house unique software – the Prognoz Platform

62

73

84

years’ experiencein the IT market

successful implementations highly qualified programmers, analysts, and economists

customers around the world

countries where our offices are located

countries we delivered projects to

70+550+20+

71500+ 1500+

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GARTNER MAGIC QUADRANTS

Business Analytics Advanced Analytics

Gartner included Prognoz in the 2015 Magic Quadrant for Business Intelligence and Analytical Platforms and 2015 Magic Quadrant for Advanced Analytics Platforms

KEY CUSTOMERS IN FINANCIAL SECTOR

PROGNOZ MARKET SURVEILLANCE (TIMELINE)

Functionality

Features

Product Specification

Market abuse patterns recognition (insider trading, pre-arranged wash trades, matched orders, non-competitive trading, market price manipulation, price control etc.)

HFT abusive strategies detection (front-running, quote staffing, quote smoking, layering/ spoofing, price fade, momentum ignition)

Statistical detection of deviations High-frequency data visualization engine Tradebook and orderbook replay

Market abuse detection: Insider trading and wash salesMarket price manipulation Trading data interactive visualizationCase managementRegulatory compliance

Front-end: 2-tier application Data sources: Stock exchanges data (clients trades and orders, tradelogs, orderlogs)References: This solution is in use in Central Bank of Russia

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BUILD-IN DETECTION MODELS

1. Market abuse patterns recognition:― insider trading― pre-arranged wash trades― matched orders― non-competitive trading― market price manipulation― price control

2. HFT abusive strategies detection ― quote staffing ― quote smoking― layering / spoofing― price fade

3. Statistical detection of price deviations

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END-OF-DAY TO INTRADAY DRILLDOWN

Drilldown into intraday data:― sorting and filtering data― news and event labels― sorting and filtering event labels― zooming function

― intraday market activities monitoring― cross-transactions in group― key statistics by trader or group

03.11.2011

Intraday 03.11.2011

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INTRADAY DATA VISUALIZATION

Convenient instruments for intraday data analysis:

Intraday deals

Traders

Net position of selected trader

Deals

HIGHLIGHTING DEALS

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Buy deals(green points)Sell deals

(red points)

Visualization of intraday dynamics:― Labels of deals and events on the timeline― Net position for trader or group― Drilldown to orders and counterparty for each deal

Cross deals (orange points)

ORDER BOOK VISUALIZATION AND REPLAY

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Visualization of order book:― Bid-ask spread and orders of selected traders over historical period― Order book visualization at the selected moment― Order book replay: tick-by-tick or second-by-second― Drilldown to list of orders for each price level

Historical period

Order book

Order list

Traders

EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 60 SEC

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Orders of selected trader

Historical period 60 sec. Current time = 16:48:45.000084

Volume by price levels of selected

tradesPlay mode navigator

Bid-ask spread

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EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 1 SEC

16 ms

Historical period 1 sec

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PROGNOZ.SITUATION CENTER

1. On-line markets and news monitoring2. High level market health indicators3. Interactive drilling down into the detailed trading information4. Early warnings

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ALGORITHMS CONFIGURATOR

Algorithms configurator1. High level objective language available for users2. Binary compliable code (not interpreter)

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DMZ Enterprise Network

ProjectProject

HOW IT WORKS

Project

Historical DatabaseOracle

API

Cache Cache

Prognoz.Situation Center

Prognoz.TimeLine

ProjectProjectDistributed Calculation Engine t

Alerts & Statistics

Algorithm configurator

Architecture benefits for brokers and regulators1. Sophisticated market abuse patterns recognition 2. Configurable algorithms by users3. Having isolated DMZ is the stringent info security requirement of many

financial institutions 4. Insignificant (for surveillance) latency dramatically decreases costs of

solution

Batch FilesReserved channel

1/3/2012

4/3/2012

7/3/2012

10/3/2012

1/3/2013

4/3/2013

7/3/2013

10/3/2013100000

1000000

10000000

Number of trades Number of orders

Time

Coun

t

Number of orders: ~ 10 M per day (median)~ 37 M per day (in peak)

Number of trades: ~ 700 K per day (median)

ACTIVITY OF EQUITY MARKET

*CFTC Technology Advisory Committee, 2012

HIGH FREQUENCY TRADING (HFT)

“Coscia was accused of entering large orders into futures markets in 2011 that he never intended to execute. His goal, prosecutors said, was to lure other traders to markets by creating an illusion of demand so that he could make money on smaller trades, a practice known as spoofing. Prosecutors said he illegally earned $1.4m (£900,000) in less than three months in 2011 through spoofing.”, The Guardian, HFT layering, November 2015

“A unit of hedge fund Citadel LLC was fined $800,000 by U.S. regulators in June for failing to prevent erroneous orders from being sent to several stock exchanges over a nearly three-year period”, Reuters, HFT stuffing, 2014

“Athena is the regulator’s first market manipulation case against a firm engaged in high-frequency trading, an industry besieged by accusations that it cheats slower investors” , Bloomberg Business, HFT manipulations, 2014

“Navinder Singh Sarao, 36, is fighting extradition to the US where he is facing 22 charges ranging from wire fraud to commodities manipulation, which carry sentences totalling a maximum of 380 years. Mr Sarao is alleged by US prosecutors to have made $40m over four years by spoofing the Chicago futures market. The trader’s activities include making a $900,000 profit on May 6, 2010, when a trading frenzy known as the flash crash saw one of the most spectacular falls ever seen in the equity markets .” , Financial Times, 22 October 2015

MANIPULATIONS

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TIMELINE: BUILD-IN DETECTION MODELS




Quote smoking - practice of putting a large number of quotes (creation new bids and offers) and then immediately canceling them

Best bid

Best ask

69 ms

QUOTE SMOKING

Agent’s asks

Possible criteria:

• Number of orders per time interval (sec, min, hour)

• Median lifetime of order

• Best price ratio = count “best price” orders / count agent’s orders

• Median minimum distance between agent’s price orders and best prices (best ask/best bid)

QUOTE SMOKING

Quote stuffing - practice of putting a large number of orders (thousands of messages) and then immediately canceling them for creation delay in other participants.

QUOTE STUFFING

Possible criteria:

• Number of orders per time interval (sec, min, hour)

• Order-to-trade ratio

• Median lifetime of orders

• Range of order’s price

QUOTE STUFFING

Layering - practice of creation selling/buying pressure in order to make naive investor to move the price.

Best ask

Best bid

Cancellationsof orders

Agent’s bid

Agent’s asks

Trade

LAYERING/SPOOFING

Possible criteria:

• High level of order imbalance = count of buy orders / all orders

• High number of orders in visible part of order book

• Ratio of agent’s volume to visible volume of order book

• Ratio of agent’s orders to count of orders in visible part of order book

• Median lifetime of orders in each part of order book

LAYERING/SPOOFING

Price Fade - practice of orders cancellation immediately after the trade on the same venue.

Best ask

Best bid

Cancellationsof orders

Agent’s asks

Trade

PRICE FADE

Possible criteria:

• Number of cancelled orders at the same time

• Range of order’s price

• Number of orders cancelled before trade (in interval x sec)

PRICE FADE

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TIMELINE: BUILD-IN DETECTION MODELS




CIS, Eastern EuropeMoscow

+7 495 995 80 76

Western EuropeBrussels

+32 2 217 19 50AsiaBeijing

+86 10 6566 5337

North and South America, Canada, Australia, AfricaWashington

+1 202 955 55 20

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CONTACTS