2016年-FCA英国金融行为监管局_ms15_1_2_annex_2_15页_307kb

Summary of MS15/1.2: Annex 2 - Data Collection and Analysis

Core Content

This document outlines the data collection and analysis process undertaken as part of the FCA's market study on the investment and corporate banking sector. It focuses on the data gathered from banks and advisers, the challenges faced during collection, and the methods used to clean, standardise, and utilise the data for further analysis.

Main Points

• Data Sources: The market study collected data from a variety of sources, including banks, advisers, buy-side investors, and databases such as Dealogic and Orbis.
• Banks and Advisers: 81 banks and advisers were approached for information, with 70 responding. Of these, 63 provided complete revenue data for 2012–2014, and 60 completed the transactional data request.
• Data Types Requested:
  • Quantitative data: Annual revenues, unsuccessful bids, order and allocation books.
  • Qualitative data: Value proposition ratings, mandate awarding methods, ancillary services, and client engagement details.
• Transactional Data Scope:
  • Covered ECM (IPOs, follow-on offerings, other ECM), DCM, M&A, and corporate lending.
  • The data request was tailored to reduce the burden on banks, especially for DCM, lending, and M&A, which had high transaction volumes.
• Geographic Scope:
  • The study focused on UK operations, regardless of the location of the client or legal entity.
  • Some non-UK transactions were included if they were reported by banks as being within the scope of their UK operations.

Key Information

1. Data Collected from Banks and Advisers

• Revenue Data:

  • 63 responses provided annual revenue breakdowns for UK wholesale operations (2012–2014), with some extending to 2015.
  • Revenues were split by service type: corporate banking, investment banking (ECM, DCM, M&A, acquisition finance), and other wholesale operations.

• Unsuccessful Bids:

  • 27 responses for IPOs and 19 for other ECM services were received, but the data was incomplete and not suitable for analysis.

• Order and Allocation Books:

  • 32 banks provided details on 801 books for 410 IPOs.
  • Banks also provided revenue data from investors in their allocation books and a list of meetings with issuers and investors.

• Value Proposition Ratings:

  • 66 banks and advisers responded to the question on what they considered most important when setting out their value proposition.
  • This data was used in Chapter 6 and Annex 4 to assess selection criteria.

• Transactional Data:

  • Banks were asked to provide transaction-specific data for each deal, including 60 pre-populated fields from Dealogic and 31 additional fields.
  • For IPOs, an additional four fields were included.

2. Challenges in Data Collection

• Data Accessibility: Many banks did not have the requested data easily accessible and had to perform manual data collection.
• Incomplete Responses: Some responses were not completed as per the instructions, leading to inconsistencies and gaps.
• Diverse Terminology: Banks used different terms for the same concepts, which required significant data cleaning and standardisation.
• Currency Conversion: Data submitted in different currencies had to be converted to USD using ECB exchange rates.

3. Data Cleaning and Standardisation

• Data Cleaning Process:
  • Included follow-up queries, data aggregation, and standardisation across banks.
  • Addressed inconsistencies, conflicts, and duplicate entries.
• Standardisation:
  • Responses were recoded to align with predefined options.
  • Categories were created for unlisted responses.
• Currency Conversion:
  • All data was converted to USD for consistency.

4. Representativeness of Transactional Data

• Syndicate Coverage:
  • The data covered at least three quarters of syndicate members for around half of transactions.
  • Coverage varied by transaction type (e.g., 50% for IPOs, 60% for ECM follow-ons).
• Comparison with Dealogic:
  • The study's dataset was compared with Dealogic EMEA data for 2014 and 2015.
  • The sample size was smaller than Dealogic's due to the focus on UK operations and the exclusion of certain transactions.

5. Categorisation of Clients and Providers

• Client Categorisation:
  • Corporates were classified into large, medium, and small based on market capitalisation, total assets, and operating revenue.
  • If market capitalisation data was missing, other criteria were used.
• Provider Categorisation:
  • Banks and advisers were classified into small, medium, and large based on their annual investment banking revenues.
  • The thresholds for classification are outlined in Table 10.

Conclusion

The data collection and analysis process was designed to be proportionate and representative, with a focus on UK operations. Despite challenges in data completeness and consistency, the FCA conducted a thorough data cleaning process to ensure the dataset was suitable for analysis. The final dataset included transactional data from 60 banks and advisers, and it was used to examine various aspects of the market, including value proposition ratings and client engagement.