Finance

Reference Data in Trading

Reference Data in Trading

Trading, here, refers to the transfer of a stock or security from the seller to the buyer. There is a number of risks involved in trading such as Liquidity Risk, Market Risk, Credit Risk, Interest rate risk, and so on. Along with all the risks mentioned, consideration of Operational Risk is also of so much importance. Operational risk refers to uncertainties and hazards faced in performing day-to-day or routine activities. — Change it 

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Faulty or incorrect reference data is the major component of operational risk. To manage operational risk, it is important to improve data quality and accuracy. Reference data is used at all levels including front office, middle office, and back-office level. 70% of all the data used in financial transactions is reference data.

What is meant by Reference Data:

Reference data refers to the information about Financial instruments involved in trading, and parties involved in the security trading. It involves information about Type of security, Price of security, Counterparties, Parties involved in the financial supply chain such as brokers, clearinghouses, Credit rating, Expiration date or Maturity of financial security, and more.

What important information does it provide?:– Elaborate

It provides information about three important things:

  1. Financial Product Information: It includes information about the type of security, its symbolic identifiers, maturity date, and so on. Thus, it includes all the important information related to the financial instrument used in trading.
  1. Entity Information: Reference data provides important information about the parties involved in trading the financial instrument such as counterparties, financial supply chain.
  1. Pricing Information: This data also provides information about the pricing of financial instruments and how they will be valued differently at different time periods.
  1. Used in Managing Operational Risk:

Types of Reference Data: Elaborate

It is categorized into two types:

  1. Static Data:

As the name suggests, Static data is a type of data that remains static or constant and does not alter throughout the transaction. It includes information about the name of the counterparties and parties involved in the financial supply chain, type of financial instrument, maturity date of the instrument, etc.

  1. Dynamic Data:

As the name suggests, dynamic data is a type of data that is dynamic in nature i.e. changes throughout the transaction. This is the information about credit rating, price of the security at different times, the closing price of a security, etc.

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Examples:– Take from pdf 

Reference data can be categorized in various data categories according to different data items.

Type of Security: Stock, preferred stock, bond, swap, futures, etc.

Calendar information: Holiday, expiration of the contract

Terms & Conditions: Maturity date, conversion date, and rate sinking fund provision, deliverable equivalent, and reset date.

Problems associated : — Elaborate

  1. Duplication of Reference Data:  Duplication of reference data is a common problem because firms source data from third-party vendors. These vendors have their own data definitions and data structure. The firm’s data structure and definitions are also different from outside vendors. This mismatch may lead to erroneous financial transactions. It may lead to system failure.
  1. The problem of Standardisation: New regulations like FRTB and MiFID II have added further complications by doubling down on standards for the identification of counterparties, trading venues, and financial securities throughout the trade.

Conclusion:

The importance of clear and accurate reference data in a financial transaction can’t be understated. Financial firms are now working towards a centralized, industry-wide business model which will provide consistent reference data to solve the problem of standardization.

Author: Hetvi Shah

About the Author: Hetvi is a BBA(Finance) graduate. She is currently pursuing an MBA with Finance specialization. She has a keen interest in Financial Market, Financial Management, and Financial Analysis.

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