test.asp and click where it says Register to lmod $150 discount on … The Data.pdf · 2015. 6. 8. · earnings estimates) and aggregators (e.g. FactSet aligning Compustat with IDC

© Ed Flynn & Learning Modules Page 1 These notes have been prepared by Ed Flynn, an FISD recognised trainer. (updated 10 March 2014.)

Dear FIA Candidate Welcome to the course notes for the Data Section of the FISD FIA Syllabus. This module is designed to prepare you to take the FISD FIA exam. By using a combination of the videos and course notes - and working through the quiz - you should be ready and feel confident to pass the exam. (One without the other will not work effectively). You have 60 days to view the videos as many times as you wish and you can also complete the quiz as many times as you wish during this period too. It is however advised that you download and save this pdf so that you can refer back to it should you need to after the 60 days have expired. We have structured the course notes to match the order of our videos and have referred them back to the FISD syllabus. There are however many occasions when the sequencing of the videos is different to the order of the FISD syllabus - but it is all covered. The course is divided into four main sections: Section 1: The Markets Section 2: The Data (covered in this module) Section 3: The Technology Section 4: The Industry

To book your FISD FIA exam, please visit http://siia.net/fisdpc/test.asp and click where it says “Register to

take the FIA exam”. When you pay for the exam please use discount code lmod to receive a $150 discount on the exam fee. If you have any problems please contact the FISD co-ordinator David Anderson at email: [email protected] Good luck! Best wishes

Tracey Huggett Finance Modules PS: We welcome your feedback and comments. Email us at [email protected]

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SECTION 2: THE DATA

FISD general overview for the section THE DATA: (FISD syllabus Section 2) A candidate must understand the different types of data used in the market, where that data comes from originally, who delivers it to customers, the many different ways that data is used and broadly what are the types of commercial model deployed for charging for it.


Finance Modules video: The Data – Module 1

Sources of Market Data Time: 5 min 13 sec

SOURCES OF DATA (FISD Syllabus 2.1) FISD syllabus requires you to: Thoroughly understand the different sources of data and the implications of what that means for the use the data.

VENDOR GENERATED DATA (FISD Syllabus 2.1.3)

• What types of data do vendors generate • Where does news and commentary come from • How do vendors add value to data from other sources • What does the phrase “aggregator” mean

FINANCE MODULES COURSE NOTES: Vendor Generated Data Vendor generated data is generally considered to be sourced from originators (e.g. Thomson Reuters creating earnings estimates) and aggregators (e.g. FactSet aligning Compustat with IDC data), where originators create or directly collect data and aggregators generally co-mingle sets of third party data. Data sources are broad in nature and may include: • Publicly available data (such as data provided to regulators by publicly traded companies) • Exchange based trading price data (quote data) • Or numerous other categories of market data, such as: – Historic data used for modeling illiquid instruments that provide clues on possible future trade prices based on past performance (such as price “evaluations”) – News (based on fact) and commentary (based on opinion) can provide data for investment research ideas and advice Most vendors seek to add value to their content regardless of whether they are an originator or aggregator. They do this by providing expertise in data collection, delivery, support, quality control, timeliness and coverage. (An example would be where they “normalise” global company data from multiple accounting standards.) Additionally, vendors may seek to provide value by integrating data into applications or a platform to make using the data easier, included would be managing the delivery and updating of data. Public content on the internet • Comment on web – free can be worth it, but reliability can be an issue. Also because of data piracy issues, data that appears as free on the internet generally can only be used for personal use and not commercial use. • Quote data on free sites does not meet trading needs, trade price only – Brokerage and trading functions must provide BID and ASK to TRADE


EXCHANGE GENERATED DATA (and similar e.g. MTF, ATS AND IDB etc.) (FISD syllabus 2.1.2)

FISD syllabus requires you to: • What is exchange data • Why do exchanges (and other similar entities) distribute data • What types of data do exchanges generate and distribute • How do exchanges distribute their data • What are the differences between “direct” and “indirect” distribution • What is co-location and proximity hosting

FINANCE MODULES COURSE NOTES: Exchange Generated Data Exchange generated data is primarily that of “quote data”, that is – bid, ask or offer, trade, order size. Level 1 data is the best available bid and offer, also known as NBBO (National Best Bid and Offer) in the US. Level 2 data is additional quotes, higher asking prices and lower bid prices, with order sizes, which collectively shows the “depth of the market”. For illustration, if 100 shares are offered at $25.00 and the bid is $24.875 then depth of market data would show additional quotes such as 1,000 shares bid at $24.00 and an additional 5,000 shares bid at $23.50. On the offer side, similarly there may be 5,000 shares offered at $25.25 and 2,500 shares offered at $26.00. Level 2 data would provide these additional quotes. How exchanges distribute their data: • Users can connect directly to the exchange or indirectly through a vendor or distributor • Consolidated vendors collect quote data from multiple (global) exchanges and integrate it into a single feed or platform (such as Bloomberg or Thomson Reuters) • A direct feed is where a firm connects directly to each exchange to reduce delay of processing time (“latency”) from a consolidated data provider • An increasingly important method of eliminating latency is to be physically located at the exchange’s data centre, termed “co-location”, or to obtain the same access by using a provider that has “co-lo” or “proximity hosting” services. Some people use the phrases proximity hosting and co-location to mean the same thing, however, if one is a purist, co-location is data accessed directly at the exchange and proximity hosting is very close to the exchange but not in the same location. • Direct Market Access (DMA) is a concept whereby a firm that is not a “member” of any exchange can process orders on that exchange via another brokerage house who is a member of that exchange. This is often done with either “low touch” i.e. minimal advice from the member brokers house - or 'no touch' where no advice is given.


CONTRIBUTED DATA (FISD Syllabus 2.1.1)

FISD syllabus requires you to: Understand: • What is contributed data • Who, typically are the contributors of data and why do they contribute • How do organisations contribute data • What types of data are typically contributed • What is the debate surrounding who owns contributed data

FINANCE MODULES COURSE NOTES: Contributor Generated Data Contributed data is generally third party “broker dealer” data offered by broker dealers who publish their inventory for sale whether a primary issue (new issuance or raising capital) or secondary markets trading (resale of existing securities). Contributors publish their inventory on major market data platforms as a method to communicate with buy-side institutions and other sell-side firms. The inventory published includes: • Indications of interest or pricing from brokerage firms • Market makers soliciting for trading • The third party provides data up on vendor terminals or order management systems enabling counterparties to see a broad range of data on a single workstation The owner of contributed data is generally perceived to be the originator. However, when the data is provided by a third party data supplier, particularly when it has been comingled with other data, the issue of “who owns the data” – the originator or the market data vendor - can become more complicated.



Service Providers Time: 4 min 23 sec

VENDORS (PROVIDERS) (FISD Syllabus 2.2)

FISD syllabus requires you to: Be familiar with the global vendors and also be familiar with the range of regional and more specialist vendors. You should have a broad understanding of: • The range of services provided and core competencies • Their methods of delivery and display • Their broad organisational structure • Their broad approach to commercials e.g. pricing and contracts

FINANCE MODULES COURSE NOTES:

Primary List of Vendors: Bloomberg A major terminal platform provider for trading and analytics (Bloomberg Terminal). Also provides pricing evaluations (B-Val), reference data (Data License), quote data feeds (B-Pipe), news and a trading ATS. (High Level Summary – product categories include: desktop workstation, analytics, trading, research) Thomson Reuters A major terminal platform provider for trading and analytics (Eikon, T1). Also provides pricing and reference data (DataScope), quote data feeds (Elektron hosted and managed solutions), news, FX trading and a broad set of diverse data ranging from earnings estimates to trading systems and other investment research services and data. (High Level Summary – product categories include: desktop workstation, perceived to have the greatest breadth of broad research services via acquisitions) Interactive Data Best known for its pricing and reference data, it is in the datafeed space providing real time data delivery and low latency hosting services. (High Level Summary – product categories include: End of Day accounting – Fixed income evaluations, real time capabilities) SIX Financial (formerly Telekurs) Best known for its global reference data, it is also in the real time pricing datafeed delivery business (High Level Summary – product categories include: global reference data, real time capabilities) Dow Jones Primarily a news provider (publisher of the Wall Street Journal and DJ Newswires) as well as a major index participant of the Dow Jones Indices (divested to S&P in 2012, however branding is retained as Dow Jones Indices)


FactSet Originally an aggregator of other vendor content and added value in co-mingling and managing disparate data sets to integrate across databases and applications. Currently FactSet is both an aggregator, originator and provider of a terminal platform for real time data. (High Level Summary – product categories include: securities research data – “aggregator of third party content”) MSCI/Barra Both an index provider (the MSCI family of indices) and a quantitative modeling platform (Barra), with additional acquisitions (Risk Metrics, ISS, CFRA) used in risk and compliance and investment research. (High Level Summary – product categories include: indices, quantitative risk, attribution & optimisation models) Morningstar Founded as a mutual fund analytics company where it retains its primary focus but it has recently moved more diversely across other market data content sets (fundamentals, real time) as well. (High Level Summary – product categories include: mutual fund data) Other major market data vendors include: • Standard & Poor’s – a major provider of ratings data, index data, pricing & reference data and fundamental data and research services, which includes S&P Compustat and CapitalIQ. • Markit – a pricing data provider and research data provider (primarily through its acquisition of Wall Street on Demand, now Markit on Demand) • Moody’s Analytics – a credit ratings provider and fixed income analytics service • News providers – Financial Times, Associated Press and news aggregators such as Acquire Media • Economic data providers – IHS Global Insight, Decision Economics, Haver Economics and others • Index providers – Russell Indices, Wilshire, FTSE and index aggregators such as RIMES • Market data management providers – The Roberts Group, MDSL, and BST’s FinOffice and others • Other providers – Fitch (Ratings) and pricing services that include Superderivatives, VWM Group and IRESS


The Vendors Company Term Quote EOD Ref C/Act Funds Filing Esti

mate

Performance

Index Res Identifier

Rating

Risk Econ News

History

Ind/ Sector

Bloomberg x x x x x x x x x x x x x x x

Dow Jones x

Factset Research Systems

x x x x x x x x x x x x x

Fitch Ratings x

FTSE x

Interactive Data

x x x x x x x x

Markit x x x x

Mergent x x x x x

Moody’s Investor Services

x

Morningstar x x x x x x x x x

MSCI/Barra x x x x x

Netik x x x

Northfield Information Service

x x x x x

Rimes Technology

x x x x x x x x x x

Six Financial x x x x x x x x

Standard & Poors / McGraw Hill

x x x x x x x x x x x x

SunGard Market Data Services (FAME)

x x

Thomson Reuters

x x x x x x x x x x x x x x x x x

Thomson Reuters - Lipper

x x x x


Below is a fuller list of the products sold by main market data vendors. For business it is important to have a broad awareness of the product names but you will not be tested on the specifics in the certification. You will however need to know where a company operates. The listing of vendors and services are provided for illustration and can be considered to evolve and change over time. Therefore please visit the vendors’ websites to check the products and services offered by each firm.

Thomson Reuters Vendor Type Category Product Name Original/Acquired

Company

Thomson Reuters General Data Administrative Tool DACs Reuters

Thomson Reuters General Data Announcements

StreetEvents CCBN/Thomson

Thomson Reuters General Data Announcements StreetSmarts (retired

into StreetEvents) StreetSmarts/Thomson

Thomson Reuters General Data Economic EcoWin ECO Win/Reuters

Thomson Reuters General Data Economic IFR Briefing IFR/Reuters

Thomson Reuters General Data Estimates First Call First Call/Thomson

Thomson Reuters General Data Estimates I/B/E/S I/B/E/S / Primark/Thomson

Thomson Reuters General Data Estimates Multex - Estimates Multex/Reuters

Thomson Reuters General Data Estimates Starmine Starmine/Reuters

Thomson Reuters General Data Fixed Income Data SDC Securities Data Corp/Thomson

Thomson Reuters Exchange Fixed Income Trading

TradeWeb TradeWeb/Thomson

Thomson Reuters General Data Fundamental Multex - Fundamentals

Market Guide/Reuters

Thomson Reuters General Data Fundamental DataStream DataStream/Thomson

Thomson Reuters General Data Fundamental Nelsons Nelsons/Thomson

Thomson Reuters General Data Fundamental Worldscope Worldscope/Primark/Thomson

Thomson Reuters General Data Fundamental - Application

Baseline Baseline/Primark/Thomson

Thomson Reuters General Data Fundamental - Application

StockVal StockVal/Reuters

Thomson Reuters General Data FX Rates WM Company Joint Venture with WM / Reuters

Thomson Reuters General Data Investment Research

Thomson Business Intelligences

Thomson

Thomson Reuters General Data Investment Research

Thomson One Analytics

Thomson

Thomson Reuters General Data Municipal Bond Data

TM3 Municipal Markets Monitor/Thomson

Thomson Reuters General Data Mutual Fund Data Lipper Lipper / Reuters

Thomson Reuters General Data News Reuters News Reuters

Thomson Reuters General Data Ownership Data CDA CDA Investnet/Thomson

Thomson Reuters General Data Pricing/Reference Data

DataScope EJV (Equity Joint Venture)/Reuters

Thomson Reuters General Data Pricing Data – Bank Loan Funds

LPC Loan Pricing Corp/Reuters

Thomson Reuters General Data Quantitative Models

Market QA Quantitative Analysis/Reuters

Thomson Reuters General Data Real Time Quote Data

Bridge (retired into RDF/Workstation)

Bridge Data/Reuters


Thomson Reuters General Data Real Time Quote Data

Elecktron Reuters

Thomson Reuters General Data Terminal Eikon Reuters

Thomson Reuters General Data Terminal Reuters 3000 XTRA Reuters

Thomson Reuters General Data Terminal Reuters Station Quotron/Reuters

Thomson Reuters General Data Terminal Telerate Telerate/Dow Jones/Bridge/MoneyLine/ Reuters

Thomson Reuters Technology Datafeed Technology

RDF Reuters

Thomson Reuters Technology Middleware Software

KOBRA (retired into RMDS)

Reuters


RMDS TIBCo (share)/Reuters


Triarch (retired into RMDS)

Reuters

Thomson Reuters Technology OMS Contributed Data

Gissing Mighter Gissing/Reuters

Thomson Reuters Technology OMS Trading Software

Autex Autex/Thomson

Thomson Reuters Technology OMS Trading Software

Omgeo Omgeo/Thomson

Thomson Reuters Technology Statistical Software MathWorks MatLabs/Thomson

Thomson Reuters Technology Tick Data Management

VhaYu VhaYu/Reuters

Bloomberg Vendor Type Category Product Name Original/Acquired Company

Bloomberg General Data Terminal Bloomberg Professional

Bloomberg

Bloomberg Exchange ATS-Equities TradeBook Bloomberg Bloomberg General Data Real Time Quote

Data B-Pipe Bloomberg

Bloomberg General Data Price Evaluation Data

B-Val Bloomberg

Bloomberg General Data Pricing & Reference Data

Data License Bloomberg

Bloomberg General Data Reference Data Management Software

Polar Lake Polar Lake

Bloomberg General Data News Bloomberg News Bloomberg

Bloomberg General Data News Bloomberg Business Week

McGraw-Hill


Interactive Data (IDC) Interactive Data Corporation

General Data Fixed Income Analytics

CMS BondEdges CMS BondEdge

Interactive Data Corporation

General Data Price Evaluation Data

Interactive Data Pricing

FT Interactive Data Corp




Muller Data Corp




Bear Stearns Pricing Service


General Data Real Time Quote Data

eSignal Real Time Data

eSignal


General Data Real Time Quote Data

Interactive Data Real Time

S&P Comstock


General Data Reference Data SIRS (Security Information Retrieval Service)

Standard & Poors / McGraw-Hill Vendor Type Category Product Name Original/Acquired

Company

Standard & Poors/McGraw-Hill

General Data

Credit Ratings S&P Credit Ratings

Standard & Poors


General Data

Energy Platts Platts


General Data

Fundamental-Application

Capital IQ Capital IQ


General Data

Fundamental Data Compustat Standard & Poors


General Data

Index Data S&P Indices Standard & Poors


General Data

Index Data Dow Jones Indices

Dow Jones/CME Group


General Data

Investment Research

S&P Advisor Standard & Poors


General Data

Municipal Bond Data

Blue List Standard & Poors


General Data

Price Evaluation Data

S&P Kenny JJ Kenny


General Data

Pricing Data – Bank Loan Funds

S& P PMD Portfolio Market Data Group


General Data

Security Numbering System

CUSIP Bureau American Bankers Association


Morningstar Vendor Type Category Product Name Original/Acquired Company

Morningstar General Data Fundamental Hemscott Bigdough.com

Morningstar General Data Fundamental Hemscott Hemscott

Morningstar General Data Mutual Fund Data Morningstar Morningstar

Morningstar General Data Mutual Fund Data Morningstar S&P Micropal

Morningstar General Data Performance & Returns

Ibbotson Ibbotson Associates

Morningstar General Data Quantitative Models

LIM Logical Information Machines

Morningstar General Data Real Time Quote Data

QuoteSpeed Tenfore Systems

MSCI/Barra Vendor Type Category Product Name Original/Acquired Company

MSCI/Barra General Data Fundamental-Application

Holt Holt Value Associates

MSCI/Barra General Data Index Data MSCI Indices MSCI (Morgan Stanley Capital International)

MSCI/Barra General Data Ownership Data ISS Institutional Shareholder Services

MSCI/Barra General Data Quantitative Models

BARRA BARRA

MSCI/Barra General Data Risk Management Data

Risk Metrics Risk Metrics

Dow Jones Vendor Type Category Product Name Original/Acquired Company

Dow Jones/News Corporation

General Data Index Data Divesture to Standard & Poors

Dow Jones


General Data News Dow Jones Newswire

Dow Jones


General Data News MarketWatch CBS MarketWatch


General Data News Factiva – Archived News

Factiva


General Data News Wall Street Journal Dow Jones

Financial Times/Pearson Vendor Type Category Product Name Original/Acquired Company

Financial Times/Pearson

General Data News Financial Times Financial Times


General Data News FundFire.com Money Media


General Data News Ignites.com MoneyMedia


Acquire Media Vendor Type Category Product Name Original/Acquired Company

Acquire Media General Data News Acquire Media Acquire Media

Acquire Media General Data News Desktop Data Desktop Data

Acquire Media General Data News NewsEdge Corp NewsEdge Corp

HSI Global Insight Vendor Type Category Product Name Original/Acquired Company

HIS Global Insight

General Data Economic HIS Global Insight DRI/Primark

HIS Global Insight

General Data Energy HIS CERA CERA

Markit Vendor Type Category Product Name Original/Acquired Company

Markit Corporation

General Data Fundamental -Application

Wall Street on Demand

Wall Street on Demand

Markit Corporation

General Data Index Data IBOXX IBOXX

Markit Corporation


Markit Markit

Moody’s Investor Service Vendor Type Category Product Name Original/Acquired Company

Moody’s Investor Service

General Data Credit Ratings Interfax Credit Ratings

Interfax Russian Credit Service


General Data Credit Ratings Moody’s KMV KMV Corporation


General Data Credit Ratings Moody’s Ratings Moody’s Investor Service


General Data Investment Research

Moody’s Analytics



Other Vendors Vendor Type Category Product Name Original/Acqui

red Company

BST (Finoffice) General Data Administrative Tools Fin Office

MDSL General Data Administrative Tools MDSL

Screen Consultants

General Data Administrative Tools Screen Consultants

The Roberts Group General Data Administrative Tools FITS (Financial Information Tracking System)

Depository Trust Clearing Corp

General Data Clearing Services DTCC

Fitch General Data Credit Rating Fitch

Factset General Data Fundamental – Application

Factset

RIMES General Data Fundamental – Application

RIMES

FTSE General Data Index Data FTSE

Russell Index Data Services

General Data Index Data Russell

Associated Press General Data News Associated Press

Superderivatives General Data Price Evaluation Data Superderivatives

VWD General Data Price Evaluation Data VWD

IRESS General Data Pricing & Reference Data

IRESS

SIX Financial General Data Pricing & Reference Data

Six Financial Formerly Telekurs



Exchanges Time: 1 min 41 sec

Exchanges and MTF/ATS and IDB (FISD Syllabus 2.2.1)

FISD syllabus requires you to: Understand the role that exchanges, MTFs/ATSs and IDBs play in generating, distributing and marketing data and the types of data that they generate. (Note: MTSs/ATSs and IDBs are covered in Video: Data Module 3 and Video: Markets 7)

FINANCE MODULES COURSE NOTES: Exchanges compete for trading volume and derive revenue from trade transactions. Distribution of quotes to market participants is an important component of price publication - exchanges publish quote data to facilitate price transparency and send it to all participants to draw in “liquidity” to their exchange. The more shares available to transact, the more likely buyers and sellers can transact efficiently without creating significant market impact (i.e. the price change up or down due to there not being enough buyers/sellers to match an order is minimised). Sufficiency of liquidity is termed “pools of liquidity”. Participants will gravitate toward the largest pools of liquidity where they are most likely to transact efficiently. Exchange trends include: • Growth in the importance of revenues attributed to the distribution of real time quote data as opposed to revenues from trading volumes (average daily volumes circa 2012 ran at approximately 6 billion per day whereas in 2008 daily volumes ran significantly higher around 10 billion shares per day). With trading volume down and generating less revenue, market data fees have grown in importance as a revenue source. • High speed trading and direct feeds (connectivity direct to an exchange) has continued with the increased importance on reducing latency, which has led to co-location, machine (computerised) decision making and trading rules known as algorithmic trading (algo trading) and trading strategies based on speed or High Frequency Trading (HFT). • Exchanges have faced commercial challenges where individual “paying” traders are fewer in number as computers and servers replace traditional traders. • Exchange fees are generally based on data delivered in real time whereas data that has been delayed at some interval (typically 15 minutes or longer) has not been subject to fees. • Additional exchange issues centre on the “tick rate”, where the volume of quotes has continued to grow exponentially and requires greater capacity in the management and delivery of exchange quote data. Sample list of exchanges include: NYSE/Euronext, NASDAQ/OMX, Deutsche Börse, CME Group, ICAP, LSE, ASX, BM&F Bovespa, TMX, RTS Exchange, HKEx, OPRA, BATS, SIX Swiss Exchange and approx. 160 additional exchanges globally. Exchange Appendix – major exchanges It is important to have a broad awareness of the product names but you will not be tested on the specifics in the certification. You will however need to know where a company operates. The listing of vendors and services is provided for illustration and can be considered to evolve and change over time. Therefore please visit the exchanges’ websites to check the products and services offered by each firm.


NYSE Euronext

Exchange Type Category Product Name Original/Acquired Company

NYSE Euronext Exchange Exchange NYSE Euronext New York Stock Exchange

NYSE Euronext Exchange Exchange AMEX American Stock Exchange

NYSE Euronext Exchange ATS-Equities ARCA Archpelago ECN

NYSE Euronext Exchange Exchange NYSE Euronext EuroNext Exchange

NYSE Euronext Exchange Exchange NYSE Euronext Amsterdam Stock Exchange

NYSE Euronext Exchange Exchange NYSE Euronext Brussels Stock Exchange

NYSE Euronext Exchange Exchange NYSE Euronext Lisbon Stock Exchange

NYSE Euronext Exchange Exchange NYSE Euronext Paris Stock Exchange

NYSE Euronext Exchange Exchange NYSE Euronext LIFFE (London International Financial Futures & Options Exchange)

NYSE Euronext Exchange Commodities Exchange

NYSE Euronext Amsterdam Futures & Options Exchange

NYSE Euronext General Data Depth of Market NYSE OpenBook New York Stock Exchange

NYSE Technologies Technology DMA Exchange Software

NYSE Technologies Wombat Technologies

NASDAQ OMX


NASDAQ OMX Exchange Exchange NASDAQ

NASDAQ OMX Exchange ATS-Equities Brut BRUT ECN

NASDAQ OMX Exchange ATS-Equities Instinet Instinet ECN

NASDAQ OMX Exchange ATS-Equities Island Island ECN

NASDAQ OMX Exchange Exchange BSE Boston Stock Exchange

NASDAQ OMX Exchange Exchange PHLX Philadelphia Stock Exchange

NASDAQ OMX Exchange Commodities Exchange

NASDAQ OMX Futures Exchange

Philadelphia Board of Trade

NASDAQ OMX Exchange Exchange Denmark / Nordic Copenhagen Stock Exchange

NASDAQ OMX Exchange Exchange Finland / Nordic Helsinki Stock Exchange


Finland / Nordic Helsinki Futures & Options Exchange

NASDAQ OMX Exchange Exchange Sweden / Nordic Stockholm Stock Exchange


Sweden / Nordic Stockholm Options Markets


OMLX OMLX (Swedish Index & Equities Options)


NASDAQ OMX Exchange Exchange Iceland / OMX Iceland Stock Exchange

NASDAQ OMX Exchange Exchange Estonia / Baltic Tallin Exchange

NASDAQ OMX Exchange Exchange Latvia / Baltic Riga Exchange

NASDAQ OMX Exchange Exchange Lithuania / Baltic Vilnius Exchange

NASDAQ OMX General Data Depth of Market TotalView NASDAQ

Deutsche Börse Group


Deutsche Börse General Data Index Data STOXX Indices Dow Jones

Deutsche Börse General Data News Market News International

Market News International

Deutsche Börse Exchange Exchange Deutsche Börse Group

Deutsche Börse

Deutsche Börse Exchange Exchange International Securities Exchange

ISE

Deutsche Börse Exchange Exchange ISE/SEAQ ISE

Deutsche Börse Exchange Exchange XETRA XESTRA

Deutsche Börse General Data Clearing Services ClearStream ClearStream

CME Group


CME Group Exchange Commodities Exchange

Chicago Board of Trade

Chicago Board of Trade


Chicago Mercantile Exchange

Chicago Mercantile Exchange


COMEX COMEX


New York Commodity Exchange

New York Commodity Exchange


New York Mercantile Exchange

New York Mercantile Exchange

ICAP (EBS)


ICAP (EBS) General Data Real Time Treasury Quotes

GovPX GovPX

ICAP (EBS) Exchange Inter Broker Dealer BrokerTec – ICAP Electronic Broking LLC

BrokerTec – CAP Electronic Broking LLC

ICAP (EBS) Exchange Inter Broker Dealer Brokertec USA Brokertec USA

ICAP (EBS) Exchange Inter Broker Dealer ICAP (EBS) ICAP (EBS)

ICAP (EBS) Exchange Commodities Exchange

ICE Group ICE Intercontinental Exchange

ICAP (EBS) Exchange Commodities Exchange

ICE Group New York Board of Trade


Other Exchanges


ASX Australian Securities Exchange

Exchange Australian Stock Exchange

ASX Australian Securities Exchange

BOLSAS Y MERCADOS ESPAÑOLES

Exchange Madrid Stock Exchange

BOLSAS Y MERCADOS ESPAÑOLES

HKEx Exchange Exchange Hong Kong Stock Exchange

London Stock Exchange

Exchange Exchange London Stock Exchange

Oslo Exchange Exchange Norway Exchange

RTS Exchange Exchange Exchange Russian Trading System

SGX Singapore Exchange Exchange Singapore Stock Exchange

SIX Swiss Exchange Exchange Exchange Swiss Stock Exchange

TMX Group Exchange Exchange Toronto Stock Exchange

Tokyo Stock Exchange

Exchange Exchange Tokyo Stock Exchange

Vienna Börse Exchange Exchange Vienna Börse

ISE International Securities Exchange

Exchange Options Exchange International Securities Exchange

OPRA Exchange Options Exchange Options Price Reporting Authority (US)

Chi-X / BATS Exchange ATS - Equities BATS Bridge ATS

Chi-X / BATS Exchange MTF - Securities Chi-X Chi-X

Currenex Exchange ATS - FX Currenex State Street Bank

FX-All Exchange ATS - FX FX-All

GFI Group Exchange Inter Dealer Broker

GFI Group

Tullet Prebon Information

Exchange Inter Dealer Broker

Tullet Prebon Information



Exchange Data & Fundamental Data Time: 2 min 59 sec

TYPES OF DATA (FISD Syllabus 2.3) FISD syllabus requires you to: Have a broad understanding of the different types of data.

FUNDAMENTAL AND ECONOMETRIC DATA (FISD Syllabus 2.3.2) A candidate should understand what the phrases “fundamental data” and “econometric data” refer to. • What are economic fundamentals • What are company fundamentals • What format(s) is Fundamental Data typically provided in • Be able to give examples of vendors

FINANCE MODULES COURSE NOTES: Exchange Data: Traditional market data is narrowly defined as “the market” and is primarily real time quote data. However market data can now be considered as all information related to the investment process. Key market data terminology (both as quote and real time data) • Common data elements: – Bid – the price a buyer is willing to pay for a security – Ask – the price a seller is willing to sell at (also known as the “offer” price, as in “offering for sale”) – Last – the last price transacted – High – the highest transaction price in a trading session (day, 52 week, etc.) – Low – the lowest transaction price in a trading session (day, 52 week, etc.) – Open – the expected transaction price at market open, generally driven from matching pre-open bids and offers – Close – the price of the last transaction at a designated price a buyer is willing to pay for a security market closing time (e.g. last traded price at 4:00pm) – Volume - the number of shares traded (maybe by each individual trade transaction, or the total shares traded in a given day for an individual security, but most commonly reported as total share volume on a given day for a particular market (e.g. NYSE volume for April 12th) • Common data classification: Level 1 and Level 2 data (all available orders are known as “the full order book”) – Level 1 data, also termed “Top of Book” is the very best bid price to buy and offer price to sell (known as NBBO in the US) – Level 2 data is both the NBBO (Level 1) and the listing of nearby bids and offers along with volume (size) to provide detail on how liquid the market is in a particular security. That is, whether or not there are many shares available at a similar price to the NBBO (in which case it’s highly liquid) or very few (which is a sign of an illiquid market). • The concept of the highest bid and the lowest asking price or offer is the “National Best Bid and Offer” (NBBO) – National Best Bid and Offer (NBBO) is the term applied to Level 1 data in the US when a security can be quoted and traded on multiple exchange venues. It is governed by principles of Reg NMS (Regulation National


Market System) where the NBBO must be shown to direct the next available trade to the exchange venue with the best price. • Real-time – refers to data being as up to date as technically possible. An actionable/executable price must be in real time, but a real time price is not necessarily actionable. However, there are also things called 'indicative' prices that can also be delivered in real time. Reg NMS

Reg NMS is a US regulation that runs under the auspices of the US SEC (securities exchange commission).

Regulation National Market System is the full name. It was established in 2007 having been proposed in 2005.

The key rules are:

- Order Protection (or Trade Through) Rule - provides intermarket price priority for quotations that are immediately and automatically accessible (Rule 611)

- Access Rule - addresses access to market data such as quotations (Rule 610) - Sub-Penny Rule - establishes minimum pricing increments (Rule 612) - Market Data Rules:

o a) Allocation amendment – institutes a new Market Data Revenue Allocation Formula, o b) Governance amendment – creates advisory committees, c) Distribution and Display Rules

– governing market data (Rule 600, 601 & 603).

The overall intent was to provide for a fairer and more transparent market for equities in the US. Best

Execution is key element of the overall intent. With REG NMS the trade through rule provides quite a

prescriptive approach. This can be contrasted with MIFID which is more principles based.

MIFID

The EU Markets in Financial Instruments Directive.

MiFID I is wide-ranging and seeks to promote a single market for wholesale and retail transactions in financial

instruments. MiFID II widens both the scope of investment services requiring authorisation by member states

and the range of investments falling within the ambit of regulation. MiFID II aims to further strengthen the

single market and ensure its resilience.

MIFID1 can into force on November 1st 2007.

MIFID is a ‘Directive’ MIFIR is a ‘Regulation’

A directive is a legislative act of the European Union, which requires member states to achieve a particular

result without dictating the means of achieving that result. It can be distinguished from regulations which are

self-executing and do not require any implementing measures. Directives normally leave member states with a

certain amount of leeway as to the exact rules to be adopted. Directives can be adopted by means of a variety

of legislative procedures depending on their subject matter.

MIFID I aimed to provide fairness and transparency across the European Equities markets. It also aimed to

create a single standardised market across Europe. Hence one element was to remove what were referred to

as ‘Concentration rules’ where a national stock exchange could make it illegal to trade certain ‘national stocks’

off that national exchange.


Best Execution was another key element but under MIFID brokers etc only had to document what their best

execution policy was.

Transparency was another element – it became a requirement that all transactions must be published even if

they took place off a ‘Regulated Market’ (i.e. away from fully regulated stock exchange.).

Different market participants were described and defined. ‘Regulated Markets’ were fully regulated stock

exchanges. MTFs(Multilateral Trading Facilities) act very much like exchanges but have a lower degree of

regulatory oversight. ‘Systematic Internalisers’ are entities like large broker dealers who might cross (i.e.

internalise) client orders between two of their clients i.e. one who sells and another who buys.

In practice since 2007 MIFID has actually created a large degree of fragmentation in the European equities

markets.

In MIFID I there was no requirement to set up a European Consolidated Tape that is part of MIFID II.

Types of exchange market data • Delayed – quote data with delay factor (e.g. 15 min delay) - generally free and often used on public websites • Snapshot – snapped in real time or delay, but as single quote, EOD (end of day) • Streaming data – the opposite of snapshot data in that is continually updating • Full tick – all quote data, each individual bid, ask and trade, generally in a data streaming broadcast • Other real time data terms – VWAP – Volume Weighted Average Price – measure of trade execution and price/volume impact – Conflated – Parsing quotes to reduce tick volume to reduce size and volume of quote stream – Update rates - generally measured in ticks per second/constrained by bandwidth & processing speed – Latency – delay from the exchange order matching engine quote until it is available to a participant • Other terms important to market data managers of real time data: – The need for redundancy or backup – if exchange data is unavailable, then a trading floor cannot function – Issues around symbology – identifier schema can be problematic if traders trade on tickers but securities settle and have record keeping on other symbology (CUSIP, ISIN, SEDOL, etc.). The need for accurate symbology is critical to ensure that the precise instrument is properly identified. Fundamental Data Fundamental data is data about a company’s financial statement or “the fundamentals” about its financial condition and operating results. This data is extracted from the balance sheet, income statement and statement of cash flows. Some include economic fundamentals as fundamental data (the FIA certification does). However this type of data can also be econometric data because it includes economic macro level data such as inflation rate, gross domestic product, growth of economy. Format for financial modelling in Excel. Vendors include: • FactSet, S&P Compustat, S&P Capital IQ, Bloomberg data licence provides, Thomson Reuters business unit formerly Market Guide, Multex, DataStream, WorldScope


Finance Modules Video: The Data – Module 5

Historical Data & Valuations Data Time: 4 min 21 sec

HISTORICAL AND TIME SERIES DATA (FISD Syllabus 2.3.3) FISD syllabus requires you to: Have a broad understanding of: • What is historical/time series data • How is historical data used • The recent growth in the importance of historical data • How is historical data typically supplied and/or created • What do phrases like “intraday”, “interday” and “EOD” mean (Note: this is covered more in The Markets – Module 6 video) • What factors affect time series • Be able to give examples of vendors

FINANCE MODULES COURSE NOTES: Historical/Time Series Data Historical data can also be termed time series data. It is used primarily for securities research and commonly used for back testing strategies and to support technical analysis – typically from charting data over time. It is also used by those building algo-trading models and also sometimes by regulators to observe market and price behaviour. Observations of time series can be correlated to a range of criteria such as peers, economic environment, etc. Generally, historical/time series data is used for back testing is end of day (EOD). However trading strategies may use high frequency, “tick by tick”, data to develop applications for algo or High Frequency Trading (HFT). “Tick by tick” data (sometimes referred to as “every tick”) includes all bid, ask and trade information rather than taking a snapshot every minute/every 5 minutes etc., as some time series data does. For certain algos, the high frequency of “tick by tick”/“every tick” data is essential. The data may be charted for visualisation, but it is generally elementised: • Other periodicities: – Intraday (bar charts) to observe trading price sensitivity – Interday may be weekly, monthly or quarterly Historical/time series data has numerous challenges to maintain accuracy: • Data gets dirty – Splits are difficult to “adjust” historically for capital changes • Mergers & Acquisitions – What happens to the old company - “reconstruct” history? • Weekends and holidays – What does “not expected” data do to statistical analysis? • Predecessor and successor identifiers – How do you map the issue if identifiers change?


• Extinction and survivor bias – You should consider whether there is a bias built into a time series if you start with the universe of securities available today rather than what was available in the time frame during which your analysis is conducted. You lose the behaviours of companies that failed (extinct) and you concentrate on companies available over the analysis time frame (survivors) Example vendors: FactSet, IDC, SunGard FAME, Thomson Reuters, Center for Research in Securities Prices (CRSP)

2.3.4 VALUATIONS DATA (FISD Syllabus 2.3.4) FISD syllabus requires you to: Understand the overall concept of evaluated pricing in the context of hard to value instruments that trade very infrequently. • What do phrases like “mark-to-market” and “mark-to-model” mean • What is “fair value” • Be able to give examples of vendors

FINANCE MODULES NOTES Valuations Data Valuations are generally end-of-day and used for accounting operations. They are generally provided for fixed income instruments that do not actively trade. • Mark to market is the value if traded today in the market versus historical cost based on similar instruments that trade in the marketplace to be representative of “where the security would trade” – hence its valuation • Mark to model is a more controversial method of using financial modeling to determine a valuation, particularly for hybrid or illiquid securities that do not actively trade • Fair value is modeling to determine what a close price may be in a market that is closed whilst others are trading to fairly value the instrument at one point in time. An example would be a Hong Kong instrument being valued as a holding in a London based portfolio. Example vendors: • Interactive Data, Thomson Reuters (EJV unit), Bloomberg (B-Val), SIX Financial, S&P and specialty vendors



Credit Ratings and Index Data Time: 3 min 1 sec

CREDIT RATINGS (FISD Syllabus 2.3.5) FISD syllabus requires you to: Have an understanding of: • What a credit rating is • What a credit rating agency is. • What data is provided by the credit rating agencies • What financial products are credit ratings used for • How are credit ratings used in the context of structured finance • Be able to give examples of vendors

FINANCE MODULES COURSE NOTES: Credit Ratings • A credit rating evaluates the credit worthiness of a debtor, especially a business (company) or a government. It is an evaluation made by a credit rating agency of the debtor's ability to pay back the debt and the likelihood of default. • A credit rating agency is a company that assigns credit ratings for issuers of certain types of debt obligations as well as the debt instruments themselves. In some cases, the servicers of the underlying debt are also given ratings. • Credit ratings are applied primarily to debt issues at an instrument level, they may also be applied universally to a company (e.g. Ford debt is downgraded to BBB) • Credit rating agencies are approved by the SEC in the US as a “Nationally Recognised Statistical Rating Organisation“ (NRSRO) • Ratings are generally provided at a bond’s issuance – the rating will typically drive the interest rate (coupon) of the bond. Issuers and broker dealers underwriting a new bond issue will use the credit rating to set the marketable interest rate. • Over time, ratings agencies review and update ratings with potential upgrades or downgrades as a security’s credit characteristics change over time. A change in a credit rating will generally trigger a material change in the value of the instrument. • Ratings are typically provided in a scale form that ranges from investment grade instruments to non-investment grade (often referred to as “hi yield” or colloquially as “junk bonds”) • Ratings determine risk characteristics, foster coupon rates and determine whether or not instruments are investment grade • Major credit rating agencies include Standard & Poor’s, Moody’s and Fitch


INDICES (FISD Syllabus 2.3.6) FISD syllabus requires you to: Understand: • What an index is • What an index’s constituents are • What is an index’s constituent weighting • How are indices maintained • Give examples of companies which produce benchmark indices • What is the “Global Industry Classification Standard” (GICS) • How are indices paid for by customers – How does index licensing work – What IPR (or Intellectual Property Rights) considerations apply – What types of customer would pay money to an index provider and what different elements would they be required to pay for

FINANCE MODULES COURSE NOTES: Index Data An index is a basket of securities that is representative of an underlying benchmark, such as a country’s overall economy (S&P500, Dow Jones Industrials, FTSE 100). Each single member (typically a stock or a company) listed on the exchange is known as a “constituent”. An index’s “weighting” means how it is priced. For example: • Dow Jones Industrials is “price weighted” – this means that the price of each component stock is the only consideration when determining the value of the index. • S&P500 is market capitalisation weighted – the size of the companies listed is taken into consideration when pricing the value of the index • Rydex S&P500 EWI is equal weighted - the smallest companies are given equal weight to the largest companies Indices are maintained by periodically re-balancing and replacing constituents that may have been acquired or no longer meet the criteria for index membership. GICS (Global Industry Classification Standard) The Global Industry Classification Standard (GICS) is an industry classification developed by MSCI and Standard & Poor's (S&P) for use by the global financial community. The GICS structure consists of 10 sectors, 24 industry groups, 68 industries and 154 sub-industries into which S&P has categorised all major public companies. "GICS" is a registered trademark of McGraw-Hill and is currently assigned to S&P. It is similar to the Industry Classification Benchmark (ICB) which is a classification structure maintained by Dow Jones Indexes and FTSE Group. How are indices paid for by customers • Exchange data licenses can be for EOD (end of day) data, real-time data, customised solutions, data distribution, or for product creation • There is a lack of IPR laws and practices especially with free streaming data. There is an ongoing problem with some consumers of data not admitting to using certain fee liable data and hence not paying for it. This is one thing that has prompted many data providers to conduct “audits” where they physically visit a consumer and manually check what data that user is consuming. This misreporting of data usage (i.e. data piracy) can be on an individual basis and a firm by firm basis – in some cases concerns have been raised about the attitudes and culture of an entire country. • Customers of exchanges would pay for the data provided by the exchange – the fee structures vary depending on the data required (for example fees would usually be higher for real-time data as opposed to end of day data).


• Indices are used for benchmarking and the creation of tradable instruments. The fees to use the index data vary according to usage. Major index providers: • Standard & Poor's, Dow Jones, FTSE, MSCI, Russell, Wilshire, CRSP, NYSE


News & Commentary + Reference Data Time: 5 min 22 sec

NEWS AND COMMENTARY (FISD Syllabus 2.3.7) FISD syllabus requires you to: Understand: • The role of news and commentary (i.e. text) as it sits alongside numeric data • What is the difference between “news” and “commentary” • Examples of financial news providers • How news is being used in algo trading • What is a news sentiment feed

FINANCE MODULES COURSE NOTES: News & Commentary News is objective and factual, whereas commentary includes opinion and advice. News is a major component driving a broad range of investment decision making (such as sell on bad news, buy on good news). News and commentary are primarily text based as opposed to being purely numeric. This is important because computer programs like the structured format of numbers. News and commentary is sometimes categorised using the phrase “unstructured data” because it has not generally been in a machine readable form. There has been a growing trend to provide elementised news to facilitate machine readable news. Elementised news is becoming a more important input within algorithmic (automated) trading. News sentiment is elementised to capture trends and other measures of interest in news to alert users to developing investment opportunities. News is generally delivered in a: • Streaming broadcast • Pushed update • Snapshot – request/retrieval Major providers: • Dow Jones, Reuters, Bloomberg, Associated Press, PR Newswire, newspapers, internet & blogs and aggregators such as Acquire Media.


REFERENCE DATA (FISD Syllabus 2.3.8) FISD syllabus requires you to: Understand what the phrase “Reference Data” means in common usage within the industry and why it is most commonly seen as static data. However, you should also understand why, over recent years, some reference data is seen to be less “static”. You should understand the key differences between: • Instrument reference data • Entity reference data You should understand the meaning, origination (history) and context of the following: • Securities master file • Enterprise data management (EDM) – examples of who the EDM system and service providers are • Golden copy • Securities identifier – What is a securities identifier – What are CUSIP, ISIN, VALOREN and SEDOL numbers – What are numbering agencies – What proprietary identifiers are provided in the market (sometimes referred to as ‘symbology’) • A candidate should understand what is meant by : – Security instrument “terms & conditions” – Financial instrument prospectus – what is the basic data found there – A data model (both in terms of physical and logical) • With respect to entity data, you should understand: – Corporate hierarchies – What is the “ultimate parent” – The concept of issuer – The concept of counterparty – The broad concept of KYC (Know Your Client) – The broad concept of AML (Anti Money Laundering) • A candidate should understand what “corporate actions” are – What are the different kinds – How do they impact time series data – Examples of providers • A candidate should understand why calendars are important in reference data

FINANCE MODULES COURSE NOTES: Reference Data Reference data is the data elements that describe the key attributes about a security. Reference data is generally static in nature. “Static” describes data made up of elements that rarely change over the life of an instrument. Securities descriptive data may be about the issue itself (instrument reference data) or about the security’s issuer (entity reference data). The data elements are different depending on the type of security. For example for bonds, the reference data elements are: • Coupon - a coupon payment on a bond is a periodic interest payment that the bondholder receives during the time between when the bond is issued and when it matures. • Maturity - refers to the specific future date on which the investor's principal will be repaid. Bond maturities generally range from one day up to 30 years. In some cases, bonds have been issued for terms of up to 100 years. Maturity ranges are often categorised as follows: – Short-term notes: maturities of up to 5 years


– Intermediate notes/bonds: maturities of 5 to 12 years – Long-term bonds: maturities of 12 or more years • Dated date – the bond issue date • Callable – a callable bond can be redeemed by the issuer prior to its maturity. Usually a premium is paid to the bond owner when the bond is called. It can also be known as a "redeemable bond". • Call schedule - the list of dates on which a specified bond can be called is shown in a call schedule. • Sinking fund provision – a characteristic of a bond whereby portions of the debt must be paid periodically before the maturity of the bond. • Sinking fund schedule – the dates and amount of debt that must be paid by the issuer prior to the maturity of the instrument. • Yield – the return you get on a bond investment. • Yield to maturity (YTM) – the total return you will receive if you hold the bond to maturity. • Yield to call (YTC) – the return if you were to buy and hold the security until the call date. This yield is valid only if the security is called prior to maturity. The calculation of yield to call is based on the coupon rate, the length of time to the call date and the market price. • Yield to worse (YTW) – the lowest potential return that can be received on a bond without the issuer actually defaulting. For an option, the reference data elements are: • Call - a call (call option) is a financial contract between the buyer and the seller of this type of option. – The buyer of the call option has the right, but not the obligation, to buy an agreed quantity of a particular commodity or financial instrument (the underlying) from the seller of the option at a certain time (the expiration date) for a certain price (the strike price). – The seller (or "writer") is obligated to sell the commodity or financial instrument should the buyer so decide. The buyer pays a fee (called a premium) for this right. • Put - a put (put option) is a contract between two parties to exchange an asset (the underlying) at a specified price (the strike) by a predetermined date (the expiry or maturity). – The buyer of the put has the right, but not the obligation, to re-sell the asset at the strike price by the future date. – The seller of the put has the obligation to repurchase the asset at the strike price if the buyer exercises the option. Securities Master File A securities master file is simply the list or database of instruments of interest or holdings. Historically, there could be multiple security master files in a single firm, each one unique and specific to particular departments and functions. Multiple security master files would often contain disparities and there was a need for more accurate and complete data - a “golden copy” - which in turn spawned the concept of enterprise data management or “EDM”. Enterprise data management has led to an entire industry of suppliers. Securities Identifiers Historically, securities identifiers have been issued and managed often at “local markets levels” (e.g. by country or instrument type) and have created challenges for market participants, particularly as markets have become more globalised. The challenge is primarily around the management of disparate security identifiers. The potential to introduce trading, settlement and operational issues with securities exists if the proper instrument is incorrectly identified. This is often due to the lack of or use of multiple forms of securities identifier schemas. Legal Entity Identifier One of the outcomes of the Dodd Frank Act & European Central Bank is the emerging changes and standards for a global identifier - LEI or Legal Entity Identifier.


Terms and conditions A component of security master data (e.g. is the bond callable?). • If the bond can be called (pre-paid by the issuer), at what time intervals and at what price may it be pre-paid (e.g. the “call schedule") • If the bond interest rate is variable, when does it reset – what benchmark does it reset to? • A prospectus is a document that includes the full legal definition of the financial instrument. Hierarchy and counterparty Exposure related to (a) single issuer and (b) single obligor. KYC (Know Your Client) Know your client (or know your customer) is tied to banking regulations ranging from knowing customer exposures to politically exposed individuals to anti-terrorism funding. • Large fund transactions must be reported with name (KYC) of the transacting parties AML (Anti-Money Laundering) Anti-money laundering, also tied to KYC, was designed to prevent illegal sources of money being converted into seemingly legitimate or legal sources of money. Corporate actions Primarily events that include payments or changes to a security, such as a dividend payment. Additional forms of corporate actions are stock splits, mergers & acquisitions, spinoffs, divestitures, tender offers and other related events that may cause the underlying security or “issued capital”, “bond amount outstanding” or “shares outstanding” to materially change. • Impact on time series – corporate actions create difficulty linking past financial statements to the survivor. An example of this would be in the event of, say, a major restructuring (merger, bankruptcy). – The treatment of a time series data over the course of multiple stock splits can be error prone (remedy is effective use of adjustment factors) Calendars and time series data Event dates to manage corporate actions, such as resets for variable coupons, interest payment dates, bond maturity dates and option expiration dates.



Data Summary Time: 8 min 44 sec

2.3.1 MARKET DATA (FISD Syllabus 2.3.1) FISD syllabus requires you to: Know: • What the phrase “market data” typically means • What the main constituent elements of “price data” are • What fields like “bid”, “offer”, “last”, “high”, “low”, “volume” etc. mean – You should understand a broad range of field types – How do different field types relate to asset classes • What phrases like “Level 1” and “Level 2” typically mean (Note: covered in Finance Modules Markets 6 & Data 6) • What an order book is and how it relates to data e.g. the phrase “full order book” (Note: covered in Finance Modules Markets 6

& Data 6) • What “best bid and offer” (e.g. NBBO) means and why it is important (Note: covered in Finance Modules Markets 6 & Data 6) The meaning, relevance and inter relationships of the following: • Real time – data made available as close to the instance it was created (e.g. a bid, offer or trade upon receipt at an exchange) • Delayed – the term applied to quote data made available after a time delay (e.g. 15 minutes) that lessens the perceived value of the data, typically seen as “non-actionable” and therefore of less commercial value to the provider. Most exchanges provide delay data free of charge as a result. • Snapshot (static) – an extract of data, typically a quote (bid, ask or trade) at a single point in time, which may be real time, but defined as a single set of data. • Full tick – the entire broadcast of all data (bid, ask, trade) available from the trading activity at an exchange or other trading venue – or set of venues across the market. • VWAP (Volume Weighted Average Price) – a measure of price related to the associated volume of shares. Used as a metric to determine price impact and effective trade execution. • Conflated – a limitation on the amount of a data stream managed or supplied to reduce the quantity of data, for example, provision of updates in real time, but updating at intervals to limit and restrict the full volume The broad meaning and significance of: • Update rates (traffic/throughput) • Latency • Redundancy • Symbology (Note: covered in Finance Modules Data 4 & 7) You should have a broad understanding of major proprietary symbologies being used in markets today such as the longstanding Thomson Reuters RIC and the developments recently introduced by Bloomberg.



Data Category Data Types General Use Example Vendors

Exchange Quote Data Real Time / Delay / Snapshot

Trading / Analysis Direct to Exchange / Thomson Reuters / Bloomberg

End of Day Pricing Data Exchange Close / Composite

Accounting Interactive Data / Thomson Reuters / Markit

End of Day Valuations Evaluation Data / Fair Value

Accounting Interactive Data / ITG Group

Reference Data Securities Descriptive Data

Security Master Management

SIX Financial / Interactive Data / Thomson Reuters

Corporate Actions Split / Dividends / M&A / Reorganisation

Accounting / Security Master

SIX Financial / Interactive Data / DTCC

Mutual Fund / UIT Net Asset Value / Style Box

Accounting / Research NAV-Interactive Data / Thomson Reuters / Bloomberg / Style-Morningstar / Lipper

ETF Data Constituents / Pricing Trading / Analysis Direct to Exchange / Thomson Reuters / Bloomberg

Regulatory Filings 10k / 3(f) / 13 (d) / Insider / Own

Risk Management / Investment Research

Edgar Online / Thomson Reuters / Bloomberg / Regulator’s sites and niche players

Earnings Estimates Mean Estimate / Standard Deviation /Analysts

Investment Research Thomson Reuters “First Call” / “Multex” / “IBES” / Street

Performance Total Return / Sector Return

Attribution / Marketing Morningstar / Lipper / Barra

Index Data Index Value / Constituents

Benchmarking / Performance / Attribution / Structured Products

Standard & Poors / MSCI / FTSE / Russell / Dow Jones / Wilshire / Exchange & Brokerage

Investment Advice Street Research / Specialty

Investment Research Brokerage Firms / Boutique Firms

Securities Identifiers CUSIP / ISIN / Valor / RIC Basic Security Identification

Standard & Poors CUSIP / Thomson Reuters “RIC” / SIXTKS-Valor

Credit Ratings Rating / Upgrade-Downgrade

Fixed Income Investment Research / Risk

Moodys / Standard & Poors / Fitch

Risk / Optimization Exposure-Weighting Portfolio Optimisation / Risk Management

MSCI Barra / Northfield / Thomson Reuters “Vestek”

Counterparty Issuer and Obligor Data / LEI

Risk Management to single entity

Standard & Poors – Dun & Bradstreet (joint venture) / DTCC / Swift

Economic CPI / Time Series Investment Research Decision Economics / HIS Global Insight

News & Commentary News / Option / Analysis Investment Research / Trading

Dow Jones / Reuters / Acquire Media / FT

Historic Time Series Price Data (daily, weekly, monthly, quarterly…)

Investment Research SunGard “FAME” / Interactive Data / Thomson Reuters

Industry / Sector Data GICs / ICB / Classification / SIC

Investment Research / Performance

S&P-MSCI “GICs” / FTSE-DJ “ICB” / DoC



Standards Time: 1 min 59 sec

STANDARDS (FISD Syllabus 2.4) FISD syllabus requires you to: Understand the broad concepts of standards. • What the ISO is • “De Jure” contrasted with “De Facto” standards Describe at a high level what certain standards are and why they have been introduced (Please note: candidates will not be required to remember specific ISO numbers). • Standards for messages in securities trading (e.g. ISO 15022 and ISO 20022) • Bank identifier code “BIC” (ISO 9362) • Market identifier code “MIC” (ISO 10383) • Classification of financial instruments “CFI” (ISO 10962) • International Securities Identification Number “ISIN” (ISO 6166) • ISO currency standards • FIX

FINANCE MODULES COURSE NOTES: De Jure (based on laws) versus De Facto (based on practice), or the legal standard vs customary (practised) standards. SWIFT (Society for Worldwide Interbank Telecommunications) provides secure interbank messaging. The ISO (International Standard Organisation) sets uniform standards, some of which are applicable to market data: • ISIN for identifier schema • Yield important results for commonality across global markets • Others include BIC, MIC, CFI FIX protocol (Financial Information eXchange) – created by industry participants to standardise trading processes between participants MDDL – Initiative to create “market data definition language” under FISD (mddl.org)



Delivery & Functional Use Time: 4 min 19 sec

DELIVERY AND DISPLAY (FISD Syllabus 2.5) FISD syllabus requires you to: Understand how data is delivered to both organisations and individual users. (Finance Module Note: this is covered in more detail in the Technology section)

TERMINALS/WORKSTATIONS (FISD Syllabus 2.5.1) FISD syllabus requires you to: Understand the main functionality of display workstations. • How price data is displayed • How news is displayed • How charts are presented • How additional functionality is provided e.g. Excel • How workstation functionality is different on and off the trading floor • How data from the internet is provided, presented and used

FINANCE MODULES COURSE NOTES: Delivery by data type Price data is delivered in real time or at a delay (data: bid, ask, trade). Generally the display flashes green for up-ticks and red for down-ticks. Pricing data for end of day is generally in a static data file of “a row of data for each security record” (data: high, low, close, volume). News is generally delivered through streaming or request/retrieve. Charting data is generally delivered visually, with interactive time periodicity. There are multiple instruments for technical analysis. Workstation (or “terminal”) data delivery is linked to its functionality – same workstation, different function. • On the trading floor – may directly cross trades with counterparty • Off the trading floor – generally investment research, asset lookup function • Excel add-in (linking into workstations and mid-office data with DDE links) Internet – general websites and content/data searching. Examples of finance sites are Fidelity.com, eTrade, Ameritrade, Google and Yahoo finance.


BATCH DOWNLOADS (FISD Syllabus 2.5.3) FISD syllabus requires you to: Understand what types of data are accessed through a batch download from a database and broadly why.

FINANCE MODULE COURSE NOTES: Batch downloads of data files are generally for EOD (end of day) type pricing files • Generally for accounting operations and similar datafeeds • Characterised as a block of data vs “updating data” • Traditional data files are often delivered overnight when systems resources would be available • Used for large scale data processing such as EOD pricing files, corporate actions and reference data

STREAMING DATAFEEDS (FISD Syllabus 2.5.2) FISD syllabus requires you to: Understand: • What streaming datafeeds are and how they differ from terminal only solutions • What the different types of datafeed are and why they might be deployed in different use case scenarios

FINANCE MODULE COURSE NOTES:

Streaming Datafeeds Streaming data is typically seen as a “middle office” system that feeds trading floor applications. It is used to distribute data (eg. streaming quotes for high volume of retail brokerage customers) where a “direct vendor product” would be inefficient and expensive as a distribution method. Streaming data is characterised by being “broadcast” and “un-interpreted”, and: • Is generally high speed, high volume • Has specialised head-end and back-end systems to manage flow (e.g. feed handlers) Streaming data is typically exchange quote data and, to a lesser extent, news. Streaming datafeeds differ from terminal solutions that are “sole recipients” of a data stream. • The term “datafeed” implies receipt into a middleware system (rather than a single end user)



Use by Firm Type & User Time: 2 min 34 sec

USES OF DATA (FISD Syllabus 2.6)

INDIVIDUAL USERS (FISD Syllabus 2.6.1) FISD syllabus requires you to: Broadly understand the different uses that individuals make of data and the different types of data different users may require: • Asset classes that use data: – Equities – Fixed Income – Foreign Exchange – Money Markets – Commodities – Energy • Job functions that use data: – Trader – Sales – Research – Compliance – Portfolio management – Clearing and settlement • Types of firm that use data: – Brokerage – Investment bank – Long only asset management – Hedge funds

FINANCE MODULES COURSE NOTES: Data use by asset class: • Equities – Investments and trading in stocks, primarily listed and highly liquid – growth of capital • Fixed income – Bond investment and trading, generally (but not always) – preservation of capital • Foreign exchange – Global business, speculation and hedging • Money markets – Cash management, short term treasury use of funds • Commodities – Speculation and hedging • Energy – Speculation and hedging Data use by job function: • Trader – Quotes and news • Sales – Quotes, news, research, CRM • Research – Fundamental data, technical data, quotes and news • Compliance – Entity data, internal holdings/positions data, returns and performance data • Portfolio management – Same as research • Clearing and settlement – Reference data, trade confirms, end of day pricing, corporate actions data


Data use by type of firm: • Brokerage – Use and distribution of data supporting trading functions (quotes, news) • Investment bank – Same as brokerage, add research data to support valuation function • Asset management – Primarily investment research data and news, quotes to manage trading • Hedge funds – Same as asset management, include strategies for long-short and arbitrage

LOCAL APPLICATIONS (FISD Syllabus 2.6.2) FISD syllabus requires you to: Broadly understand how data is used within local applications. For example within: • Microsoft Excel - including real time adapters • Bespoke in house software created for a specific user • Product software purchased for a particular purpose


Local applications: • Excel for user modelling, investment research, trader desk support. Traders particularly leverage Excel’s DDE (Dynamic Data Exchange) “real time adapters” • Bespoke or custom applications - deployed to a single user/desktop • Product software purchased for a particular purpose, or “shrink-wrap software”. An example would be statistical packages such as MS Office, characterised as commercial standard software applications


CENTRAL (SHARED) APPLICATIONS (FISD Syllabus 2.6.3) FISD syllabus requires you to: Understand how data is used within central applications which are often shared by many individuals, including: • Pricing engines risk assessment & management • Trading systems • Portfolio management • Clearing and settlement • Data storage • Back testing

FINANCE MODULES COURSE NOTES: • Pricing engines – generally applications that use real time or other inputs to value instruments for trading purposes • Risk assessment & management – applications which monitor holdings and exposures and may update in real time to set alerts to identify the securities or holdings approaching a risk tolerance boundary • Trading systems – a range of applications providing market data, order management, customer account information and trade routing type services to support trading and best execution • Portfolio management – generally providing holdings and watch lists of securities with relevant data (e.g. price or total value) displayed throughout the course of the trading day • Clearing and settlement – applications to process and perform operational functions for trade transactions • Data storage – data management and storage, often designed to accommodate time series data and data structured financial markets data (e.g. time periodicities of 5 trading days, followed by 2 weekend non-trading days, etc.) • Back testing – generally model based to run strategies and ideas forward from a historical timeframe to test results had the strategy been in place



Commercial Terms & Unit of Count Time: 6 min 16 sec

COMMERCIALS - PRICING AND CONTRACTUAL TERMS (FISD Syllabus 2.7) FISD syllabus requires you to: Have a broad grasp of commercial issues including: • Enterprise deals • Volume discounts • Price benchmarking • Global (i.e. cross border) deals • Alternative pricing models e.g. AUM (Assets under Management) based • Non Display Usage • “Most Favoured Nation” • Derived data (including “new original works”)

FINANCE MODULES COURSE NOTES: Commercials (pricing and contractual terms): • Enterprise deals Enterprise deals are typically a licence for “unlimited use” that does not restrict data use, lowering the administrative burden of licence compliance, reporting and entitlement controls. The challenge of an enterprise licence is that it is typically priced at a premium, often making it uneconomical in a downturn for a consumer in spite of the licensing benefits. It could also potentially be uneconomical for a supplier because they might be limiting future growth based on increased levels of use and value. • Volume discounts Volume discounts generally leverage scale (unit costs are lowered as a result of high volume use). • Price benchmarking Benchmarking, or a comparison to what other firms pay, is limited in part due to confidentiality and the unwillingness of suppliers and consumers to share price information. It should be noted that companies that operate as a monopoly have a duty to have transparent pricing and treat all clients alike. • Alternative pricing models Pricing by asset size or “Assets Under Management” (AUM) changes the commercials of data from a “raw material supply” to that of pricing by the value of the data being applied to a greater scale (a firm’s asset size as the metric of scale). • Non display usage Non display usage is the use of data by machines as opposed to “user viewed” or “view only” data. “User viewed”/”view only” data was typical of most trades in the past where a trader was an individual person – but now trading has changed in great volumes to machines (aka not being displayed on a screen). • Most Favoured Nation Most Favoured Nation is the principle of best price, as in: “my price cannot exceed the lowest (most favoured) price available to any of your customers”.


• Derived data Derived data (or “resultant data”) is when data is combined to create a new data set. This is usually where licensing issues can occur. Ownership is not transferred from supplier to consumer, unless the data is “non-reverse engineer-able”.

UNITS OF COUNT (FISD Syllabus 2.7.1) FISD syllabus requires you to: Understand the meaning of, and the differences between, pricing based on “units of count”: • Per user, user ID, application, application instance, machine, instrument • The relevance of MISU(Multiple Instance Single User) in this context

FINANCE MODULES COURSE NOTES Unit of Count • Counting by an individual user or “per user” Each user is charged for the data, often whether or not they use it • Licensing by “user ID” Data is charged for per individual login and password - often whether or not used (Note: user IDs and passwords are sometimes allowed to be shared) • Licensing by “application” Pricing for the software use licence by any number of sizing metrics • Licensing by “application instance” Pricing by copy of software • Licensing by “machine” Pricing by server or core-processors • Licensing by “instrument” Counting securities accessed or data hits and billing as a unit cost rate MISU – Mutiple Instance, Single User The principle of one user accessing the same data via multiple sources is commonly associated with the NSYE Euronext, which coined the term “MISU” (Multiple Instance, Single User). MISU allows a user to be “counted once” for payment purposes, even if accessing data on multiple platforms or from multiple suppliers.


DATAFEED PRICING MODELS (FISD Syllabus 2.7.2) FISD syllabus requires you to: Understand how pricing for datafeeds can be constructed. • Per site charges • Application charges • “Watchlist” or “cache”

FINANCE MODULES COURSE NOTES Datafeed pricing Licensing by “datafeed pricing” poses use and compliance challenges. Datafeeds imply digital copies of data delivered to a consuming application, which then potentially controls further use and distribution where the supplier’s control becomes limited. Licensing “per site” brings a geographical or departmental component to fee structures. This may or may not be rational: • It appears irrational to small firms with a diverse geographical footprint • But it appears rational to suppliers providing data to large, global firms with multiple sites and regional dominant businesses Application charges mean linking fees to how the data is used. The vendor’s rationale for this type of charging is that if the customer is extracting considerably higher value from some applications than others, then they should pay more. However this may seem slightly unfair because if you took a motor industry example, it would be like Porsche paying higher prices for steel because they build premium cars. Licensing by “watchlist” or “cache” is metered volume and is similar to instrument based pricing whereby you often “pay by the volume available in your systems”.



Contracts, Compliance & Audits Time: 1 min 38 sec

CONTRACTS, COMPLIANCE AND AUDITS (FISD Syllabus 2.7.3) FISD syllabus requires you to: Understand contract concepts such as cancellation dates, rollover dates and notice periods, and the broad concepts of compliance and how and why data audits take place. FINANCE MODULES COURSE NOTES: Cancellation dates • The date ending a term or service, also generally the trigger date for an automatic rollover. Rollover dates • The contract anniversary dates triggering an extended term. Notice periods • The prior, advance notice to terminate an agreement (e.g. 30 days, 90 days). Compliance • Generally applies to consumer adherence to commercial terms of vendor agreement and ability to prove such adherence. Audits • Audits are the typical enforcement control imposed by data suppliers (vendors, exchanges) to ensure compliant licensing with the supplier’s content when control of the content has passed from the supplier to the consumer.



Inventory Management Time: 2 min 21 sec

INVENTORY MANAGEMENT, PERMISSIONING, USAGE REPORTING – AND ASSOCIATED TOOLS (FISD Syllabus 2.7.4)

FISD syllabus requires you to: Understand the broad principles of how to set up and maintain an inventory management system for market and reference data. You should be familiar with the main product solutions. You should broadly understand the concept and the process of permissioning/entitlements and usage reporting. You should be familiar with how usage reports can be generated through permissioning and entitlement systems and why they are needed.


Market data inventory management systems are generally used to manage the administrative function of market data contracts, users, fees, and invoice reconciliation. They however do not typically control access or entitlement to data. Most exchange data fee models are one of “if you can see the data, you have to pay, whether or not you used it”. Inventory management systems primarily support billing administration by mapping users to the listing of services or data subscriptions to invoicing and cost data. Inventory management systems allow market data managers to track users, manage renewal triggers, inform business units and users of their market data consumption and reconcile invoicing. Permissioning and entitlement controls are generally software based utilities to manage access to data on internal data distribution systems, characterised as “enabling or preventing access to data” at both the user and application level. Most permissioning systems additionally create an audit trail and reporting capability that allows the system to demonstrate controls of data and amount of use or users consuming any particular metric of data access or use. Examples of Permissioning/Entitlement or Usage Reporting Systems DACS (Data Access Control System), a Thomson Reuters product, historically running in middleware (TIBCo and RMDS – now TREP “Thomson Reuters Enterprise Platform”), is one of the most commonly used permissioning systems. Other forms of data access control are generally internal or bespoke applications that allow for control over user access and the ability to prove out use and control of data distribution. DADS (Data Access Declaration Statement) represents an “honesty statement” on usage. DART (Data Access and Reporting Tool) is an NYSE Technologies service that reports actual usage.



Market Data Job Functions Time: 3 min 41 sec

MARKET AND REFERENCE DATA JOB FUNCTIONS (FISD Syllabus 2.8) FISD syllabus requires you to: Understand the broad characteristics of various generic roles within the market and reference data sectors.

CONSUMER FIRMS (FISD Syllabus 2.8.1) FISD requires you to understand: Commercial roles: • Business analysts • Procurement and vendor relationship management • Performance management • Administration, including billing/invoicing • Financial analyst • Inventory management • Contracts management • Compliance exchange management Technical/IT (Engineering) • Selection • Implementation • Support and maintenance • Software/systems development


Consumer Firms - market data job functions on the commercial side of the business: • Business analysts Tasked with liaising with end users to determine requirements • Procurement and vendor relationship management Tasked with negotiating the best deals with a vendor and maintaining collaborative information exchange • Performance management Tasked with measuring the performance of services supplied • Administration, including billing/invoicing Tasked with processing contracts and invoices • Financial analyst Tasked with more detailed financial analysis of ongoing and future purchasing • Inventory management Tasked with keeping track of which services are being used and by whom


• Contracts management A specialised task of keeping track of contracts, terms and conditions • Compliance exchange management A specialised task of staying on top of the complex rules that exchanges impose on the use of their data Consumer Firms - technical/IT functions in the business: • Selection Tasked with reviewing technical choices and making a selection of IT and system related products and services • Implementation Tasked with physically deploying new software and systems • Support and maintenance Tasked with looking after existing software and systems • Software/systems development Tasked with creating new software and systems (as opposed to buying them from a supplier)

VENDOR FIRMS (INCLUDING EXCHANGES AND SOFTWARE/TECHNOLOGY SUPPLIERS) (FISD Syllabus 2.8.2)

FISD syllabus requires you to: Understand the broad roles in vendor firms, including: Customer Facing • Trainers • Customer Support • Account Manager (sales) • New Business (sales) • Administration and Billing Office – cross customer • Product management • Product development • Marketing communications

FINANCE MODULES COURSE NOTES: Vendors firms - Customer facing market data job functions • Trainers Tasked with educating users on the functionality and content of services • Customer support Tasked with responding to specific problems or requests from customers, often via a telephone helpline. • Account manager (sales) Tasked with managing the relationship with the customer which would include either increasing revenues or maintaining them


• New business (sales) Tasked with generating new business revenues from new customers or new departments within existing customer groups • Administration and billing Tasked with generating invoices and collecting payment Vendor firms - other market data job functions • Product management Tasked with managing a product or service as a business. This would include collecting and recording market requirements as well as understanding the P&L economics of a given product/service • Product development Tasked with actually developing a product/service (as probably defined by product management) • Marketing communications Tasked with such things as PR, advertising and the production of collateral such as brochures etc.

PEER GROUPS (FISD Syllabus 2.9) FISD syllabus requires you to: Have a general understanding of the role that peer groups such as customer lobby groups and industry trade associations play in the market and reference data industry. You should be familiar with the major peer group organisations.

FINANCE MODULES COURSE NOTES Peer Groups Due to the market data industry maturing and becoming more complex, there is a growing trend for peer groups to work on industry issues across all participants – consumers, exchanges, vendors – which have become an increasingly effective method of sharing interests and resolving common issues. Issues range from commercial policy and regulatory compliance to data and technology challenges. Trade lobby groups e.g. SIFMA (Securities Information and Financial Markets Association) will act as a voice to represent industry interests to regulators and other constituencies (e.g. exchanges, settlement standards) Industry constituency groups e.g. FISD, FIF, IPUG (See appendix - market data acronyms)


You have now reached the end of the Data Section.

Feel free to watch the videos again, study the notes and research some of the terminology if you need to.

You can also work through the quiz as a “mock exam” to test your knowledge as many times as you like.

The other sections that the FISD FIA Syllabus covers are:

• The Markets • The Technology

• Industry Issues & Trends

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