The Data

Through a combination of videos, course notes and a quiz, you will be guided through everything you are expected to know under the "Industry Issues and Trends" section of the FIA Syllabus.

THE MODULE CONSISTS OF:

• Videos: 2 hours and 40 minutes of information.
• Course notes: 51 pages of downloadable summary notes. The notes are clearly marked and follow each of the topics listed on the FIA syllabus. The notes should be worked through in conjunction with the videos to ensure you are fully prepared to take the exam. We recommend you print these off and make notes as you watch the videos.
• Quiz to test your knowledge: Complete the quiz after you have finished studying the videos and course notes. It is recommended that you do the quiz without referring to the notes so that you gain a better understanding of the areas you may need to focus on. You will be provided with your score at the end.

HOW LONG DO I HAVE ACCESS FOR?

You have access to the course for 365 days from the date of purchase.

HAPPY LEARNING!


TO BOOK YOUR EXAM PLEASE DO SO VIA THE FISD WEBSITE
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FISD FIA SYLLABUS v4.0
THE DATA - MODULE 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.

2.1. SOURCES
The different sources of data and the implications of what that means for the use of the data.

2.1.1 Contributed data
• What is contributed data?
• Who typically are the contributors of data and why do they contribute?
• How do organizations contribute data?
• Internal versus external contribution
• What types of data are typically contributed?
• What is the debate surrounding who owns contributed data?
• How could market manipulation occur?
• Difference between 'indicative' and 'executable' quotes

2.1.2 Exchange (and similar eg MTF, ATS and IDB etc) generated data
• What is exchange data?
• Why do exchanges (and other similar entities) distribute data?
• What types of data do exchanges generate and distribute?
• Multiple quotes for the same stock from different venues eg. exchange and OTF
• How do exchanges distribute their data?
• What are the differences between “direct” and “indirect” distribution?
• What is “co-location” and “proximity hosting”?

2.1.3 IDB (Inter Dealer Broker) data
• Difference betwen voice and electronic broking
• Main asset classes covered by IDBs

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


2.2 DATA TYPES

2.2.1 Market data
• What does the phrase “market data” typically mean?
• What are the main constituent elements of “price data”?
• What do fields like “bid,” “offer,” “last,” “high,” “low,” “volume,” etc., mean?

• A candidate should understand a broad range of field types
• How do different field types relate to asset classes?

• What do phrases like level 1 and level 2 typically mean?
• What is an order book and how does it relate to data? e.g. the phrase “full order book”
• What does “best bid and offer” (e.g. NBBO – as in national best bid and offer) mean and why is it important?

The meaning, relevance and inter relationships of the following:
• Real-time
• Delayed
• Snapshot (static)
• Full tick
• VWAP
• Conflated
• Evaluated
• After midnight

The broad meaning and significance of:
• Update rates (traffic/throughput)
• Latency
• Redundancy
• Symbology

The major proprietary symbologies being used in the markets today and the issues and debate surrounding them.

2.2.2 Fundamental and econometric data
What the phrases “fundamental data” and “econometric data” refers 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
• Understand the difference between 'fundamental analysis' and 'technical analysis'

2.2.3 Historical and time series data
A candidate should have a broad understanding of:
• Historical/time series data
• How is historical data used?
• The importance of historical data
• How is historical data typically supplied and/or created?
• What do phrases like “intraday,” “interday“ and “EOD“ (end of day) mean?
• What factors affect time series?
• Be able to give examples of vendors

2.2.4 Valuations Data
The overall concept of “evaluated pricing” in the context of hard to value instruments that trade very infrequently (i.e. that are illiquid).
• What do phrases like “mark to market” and “mark to model” mean?
• What is “fair value”?
• Be able to give examples of vendors

2.2.5 Credit Ratings
A candidate should have a broad understanding of:
• A credit rating
• A credit rating agency
• Data provided by the credit rating agencies
• Financial products a credit rating is used for
• How credit ratings are used in the context of structured finance
• Be able to give examples of vendors

2.2.6 Indices
• What is an index
• An index's constituents
• An index's constituent weighting
• How indices are maintained
• How to give examples of companies which produce benchmark indices
• What industry classification standards exist ( e.g. GICS, ICB, NAICS)
• The role of index benchmarks in client investment mandates
• How indices are paid for by customers
• How does index licensing work?
• What IPR considerations apply?
• What types of customer would pay money to an Index provider and what different elements would they be required to pay for?
• Difference between standard vs. customized indices and why organisations use customized indices

2.2.7 ESG (Environmental, social and governance) data

• The broad concept
• ESG Ratings
• ESG reporting
• Examples of vendors

2.2.8 News and commentary
• The role of news and commentary (i.e. text) as it sits alongside numeric data
• The difference between “news“ and “commentary“
• What are some examples of financial news providers?
• How is news being used in Algo Trading?
• What is a News Sentiment feed?
• How has social media impacted this segment? e.g. Twitter
• The meaning of the phrase “unstructured data”

2.2.9 Reference data
What the phrase “reference data” means in common usage within the industry.
A candidate should understand why reference data was, in the past, commonly seen as static data. However, a candidate should understand and be able to explain why over recent years much reference data is seen to be less “static.”

The key differences between:
• Instrument reference data
• Entity reference data
• Issue and issuer

The meaning, origination (history) and context of the following:
• Securities master file
• Enterprise Data Management (EDM) and examples of who the EDM system and service providers are
• Golden copy
• Data governance
• Data lineage

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”)?

What is a data model (both in terms of physical and logical)

With respect to entity data:

• 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)
• The use of the phrase “Client Onboarding”
• The background to and use of the Legal Entity Identifier (LEI)

What “corporate actions” are:

• What are the different kinds?
• How they impact time series data?
• Name some examples of providers

Why calendars are important in reference data
Be able to give examples of vendors

2.2.10 Social media
Social media is growing in overall relevance to society. Candidates should understand the relevance of social media
in the context of the data industry:
• Twitter
• Facebook

2.2.11 Alternative data
• The broad concept of 'alternative data'
• Examples of alternative data
• Organisations providing services in this context

2.3 STANDARDS
The broad concepts of standards.
• What the ISO is – The International Organization for Standardization;
• “De Jure” contrasted with “De Facto” standards.

Be able to 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)

• Bank identifier code “BIC” (ISO 9362)
• Classification of financial instruments “CFI” (ISO 10962)
• FIX
• financial instrument global identifier (FIGI)
• LEI (legal entity identifier)Market identifier code “MIC” (ISO 10383)International securities identification number
“ISIN” (ISO 6166)
• ISO currency standards
• Standards for messages in securities trading (e.g. ISO 15022 and ISO 20022)

2.4 DELIVERY AND DISPLAY
The way that data is delivered to both organizations and individual users. (NB. this will be covered in more detail under the Technology section).

2.4.1 Terminals/workstations
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 and desktop APIs
• How workstation functionality is different for off the trading floor
• How data from the Internet is provided, presented and used

2.4.2 Streaming datafeeds
• 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.
• What is a Server API
• Give examples of vendors

2.4.3 Batch downloads
What types of data are accessed through a batch download from a database and broadly why.

2.5 USES OF DATA

2.5.1 Individual users
The different uses that individuals make of data and the different types of data different users may require:
• Asset class:

• Equities
• Fixed income
• Foreign exchange
• Money markets
• Commodities
• Energy

• Individuals job function:

• Trader
• Sales
• Research
• Compliance
• Portfolio manager
• Clearing and settlement
• Risk management
• Wealth manager
• Retail broker

• Type of firm:

• Brokerage
• Investment banking
• Long only asset management
• Hedge funds
• Retail brokerage
• Online brokerage

How individuals can access data via online brokerage

2.5.2 Local applications
How data is sometimes 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
• Desktop containers eg OpenFin

2.5.3 Central (shared) applications
How data is used within central applications which are often shared by many individuals, including:
• Pricing engines
• Risk assessment and management
• Trading systems
• Portfolio management
• Clearing and settlement
• Data storage
• Back testing

2.6 COMMERCIALS (PRICING AND CONTRACTUAL TERMS)
A candidate should 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 Favored Nation”
• Derived data (including “new original works”)
• Redistribution
• Audit - practices and issues
• Professional vs. non-professional users

2.6.1 Units of count
The meaning of, and the differences between, pricing based on “units of count”:
• Per - user, user id, application, application instance, machine, instrument, location, assets under management
(AUM), query/quote
• The relevance of MISU (Multiple Instance/Installation Single User) in this context.

2.6.2 Datafeed pricing models
The commercial challenges presented by datafeeds and how pricing for datafeeds can be constructed:
• Per site and per department charges
• Per application type charges
• “watchlist” or “cache”
• “Use case"

2.6.3 Inventory management, permissioning, usage reporting and associated tools
The broad principles of how to set up and maintain an inventory management system for market and reference data, including cost allocation reporting and cost analysis.
Candidates should be familiar with the main product solutions.
The concept and the process of permissioning/entitlements and usage reporting; including dynamic entitlements.
How usage reports can be generated through permissioning and entitlement systems and why they are needed.

2.6.4 Contracts, compliance and audits
A candidate should understand contract concepts such as cancellations dates, rollover dates, and notice periods and the broad concepts of compliance and how and why data audits take place.