Asset4 ESG dataset & scores

The corporate social responsibility dataset Asset4 ESG is available through Datastream / Eikon products. The dataset contains an overall score for companies, as well as individual scores for the four pillars on which it is based. In a nutshell it involved:

  • Overall Equal weighted rating score: A4IR
  • Corporate Governance score: CGVSCORE
  • Economic score: ECNSCORE
  • Environmental score: ENVSCORE
  • Social score: SOCSCORE

These individual 4 scores were based on a lot of components. From 2014 on Thomson stopped collecting data on some of the components and few more data measures from 2017 onwards. The related datatypes got the indicator inactive (between brackets). The old/stale data (as Thomson puts it) has, however, not been removed, or not yet.

A new methodlogy was created and these are no longer indicated as being a specific part of the Asset4 dataset but are Thomson series. The new overall scores are now:

  • The ESG score (TRESGS): is an overall company score based on the self-reported information in the environmental, social and corporate governance pillars.
  • The ESG controversies score (TRESGCCS): is calculated based on 23 ESG controversy topics. During the year, if a scandal occurs, the company involved is penalized and this affects their overall ESG Combined Score and grading
  • The ESG Combined score (TRESGCS): Thomson Reuters ESG Combined Score is an overall company score based on the reported information in the environmental, social and corporate governance pillars (ESG Score) with an ESG Controversies overlay. When companies were involved in ESG controversies, the ESG Combined Score is calculated as the weighted average of the ESG scores and ESG Controversies score per fiscal period, with recent controversies reflected in the latest complete period. When companies were not involved in ESG controversies, the ESG Combined Score is equal to the ESG Score.

In the extranet website of Datastream / Eikon a document is available explaining the new methodology in more detail. There is also an Excel document with details on data types as well as how they are calculated based on other ESG data types.
By filtering the list/glossary using the pillar column on ESG score you can also find new 10 main overall scores for companies for specific items which include: Community Score (TRESGSOCOS), Resource Use Score (TRESGENRRS), and Workforce Score (TRESGSOWOS).

In the recent past the ESG items have increasingly become important and are used by many investors as well as researchers & students. From the latter perspective I hope the inactive items (even though they are discontinued) will not be deleted from the original dataset. I hope this, because of reproducability of earlier research. The University of Manchester blog offers some additional information, including the 2015 list of Asset4 datatypes.

N.B.: The number of companies that have been covered through Asset4 has grown from about 1500 in 2002 to 7000+ these days (world wide). If you wish to know which companies are covered, you can use the Find series button to search on ASSET4 and you will get lists by country etc. This includes the Full universe list with the code: LAST4ESG

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Is it possible to use RIC codes in Datastream?

Recently a student asked the question if RIC codes could be used in other Databases to search for more information on companies. In a nutshell, the answer is: to a certain point and with some manual searches many codes can be indeed turned into codes for other databases if you can use Datastream.

RIC codes are Reuters Instrument codes (Wikipedia). These codes usually originate from Thomson Reuters databases like Thomson One, Eikon or Thomson Reuters News Analytics. The RIC codes for companies usually have a root code and a suffix for the market of a listing. If a company has multiple listings the root stays the same but the suffix gives indicates a different market. A theoretical example: the IBN company has the root code IBM and for a listing at the london stock exchange .L would be added to get the RIC code: IBM.L . If the listing is at the New York Stock exchange .N would be added to create IBM.N .

In the example of the student he got the list from a supervisor and was asked to get some data for these companies. The trouble was: most databases cannot work with RIC codes as these are proprietary and are not included as codes. If your university/organisation does not have a license to Thomson databases (like the ones mentioned above) but does have a Datastream license, the codes can still be converted (somewhat) into codes for this database. Although Datastream is a Thomson Reuters database, and RIC codes (or Thomson1 codes) can be recognised somehow, Datastream sometimes does not provide output or does not return the RIC codes.

Here follows step by step how I still managed to convert the original RIC codes into codes that could be recognised (mostly) by Datastream and return data:

1) Create a new list of codes where the root codes are seperated from the market codes. In excel this can be done by using the function from the Data tab, called: Text to columns. Important to remember here is: not all market codes start with a dot. Some codes stqart with the “equal to” character =. In Excel this character usually signifies the start of a function. Be sure to use the Text to columns option and seperate everything out in one or more rounds and have every column seperated as Text.

2) Create another column with the root codes seperated from the market codes in a similar way as before using the Text to columns option. This time you do not seperate the RIC code out in two text colums. The first column which carries the root code should now be left at “general“:

Some company RIC codes start with numbers that include zero characters in front. These zeros need to stay there because they are part of the code and when converting RIC codes to codes for Datastream these zeros sometimes need to still be there. In essence: we get two columns where you can choose either the original RIC root codes, or the abbreviated ones when necessary.

3) Now create a new column where you recreate the original full suffix code which includes the dot or equal to (=) character. The end result should look as follows:

4) The next step is finding out which RIC market codes represent certain exchanges. I did a few searches and found an excellent site from SIRCA that provided me with a list. I copied and saved this as a text file and imported this text file where each column is a TEXT column. I did not copy the list to Excel immediately from the website as this affected some market codes: specifically the ones that start with the “equal to” character: =
The end result looked as follows:

5) As a next step I matched the market codes from the original list with the list of market codes using 3 times the Excel function =Vlookup(). The result looks as follows:

6) What I now need to do as the final step is create two more columns in case I need to change codes from a specific market/exchange and need to add a suffix code, a prefix code, or both. For the final column I can now use the Excel funstion =Concatenate() to create alternative codes for the RIC codes and have Excel automatically add prefixes or suffixes as required for a list of codes.

7) The last step involves trial and error searches to find out for certain markets what prefix or suffix codes can be added to create either Datastream Mnemonic codes or Local codes that Datastream can actually work with. Example:

Warning 1: Remember that some exchanges that numbers as codes for companies and may require either the original codes with zeroes or not. Choose the relevant Ticker column A or B (which we created) when the Mnemonic or local code requires it.

Warning 2: Most exchanges use common prefixes or suffixes but not all of them: for some exchanges you may need to look up all codes!

8) Using the list or newly created codes we can now do a Static search in Datastream to find out if all the codes are stil current and which are not: the original RIC code list may contain codes that have changed and some codes were deleted (even though the company data is still there). First copy the new codes to a new sheet (or column) and paste them as values to get rid of the Excel formulas. Then upload them using the Datastream option: “Create List(From Range)“:

9) Next Do a Static Request search and use the list to get some basic data back that signifies which companies are associated with these codes. In the example search I requested the following codes back: DSCD (Datastream code), NAME, ISIN code. The list of codes that we uploaded at step 8 we can find/use by clicking the List Picker button below the orange Find series button.

If the original list of RIC codes is a recent one, chances are you will get many good codes back which can then be used to get actual data from Datastream or another database. The result looks something like this:

N.B.: When you have access to databases like Amadeus, Orbis, Orbis Europe or Compustat you can also try uploading the RIC root company codes as tickers and see whether they are recognized there as well. This method is more quick & dirty (tricky), as you do not know whether the right listings and ticker codes will be available for the companies.

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Thomson content update February 2017

At the end of January a change was made that has impacted the content of SDC Platinum, Thomson One and the newer Eikon product. Thomson Reuters uses league tables that capture market/deal activity for both the mergers and acquisitions, equity issuance, debt issuance, syndicated loans and US municipal bond markets. The rankings in the league tables are (in part) based on imputed investment banking fee variables.

Investment banking league tables rank investment banks on their level of involvement in specific types of transactions. An individual league table might, for example, rank the investment advisors of merger and Acquisition deals (usually by aggregate deal volume) and another might rank the underwriters of equity or fixed income offerings. (Source: SC Johnson College of Business website)”

The Thomson Reuters historical imputed fee data & history was created & added to the Thomson database(s) from 1998. Freeman & Company have been an exclusive content provider of investment banking imputed deal fees to Thomson Reuters since 2005. Freeman’s fee engine is powered by Thomson Reuters Deals data.

Starting in February 2017 new imputed fee data is created & added according to the proprietary StarMine model of Thomson. The older Freeman imputed fee data will remain available in the databases. Only in a limited number of cases some of the older data may be replaced with newer imputed fee data: historical deals where fee related fields are updated will also be given a new Thomson Reuters imputed fee.

The following variables are impacted in SDC Platinum databases:

Mergers & Acquisitions database (Deals database):

  • IMPUTED_ACQ_TOTAL_FEE
  • IMPUTED_ACQ_TOTAL_FEE_AUD
  • IMPUTED_ACQ_TOTAL_FEE_EURO
  • IMPUTED_ACQ_TOTAL_FEE_STG
  • IMPUTED_ACQ_TOTAL_FEE_YEN
  • IMPUTED_TARGET_TOTAL_FEE
  • IMPUTED_TARGET_TOTAL_FEE_AUD
  • IMPUTED_TARGET_TOTAL_FEE_EURO
  • IMPUTED_TARGET_TOTAL_FEE_STG
  • IMPUTED_TARGET_TOTAL_FEE_YEN
  • IMPUTED_INV_FEES
  • IMPUTED_INV_FEES_AUD
  • IMPUTED_INV_FEES_EURO
  • IMPUTED_INV_FEES_STG
  • IMPUTED_INV_FEES_YEN

Global New Issues database (Equity, Bonds & Syndicated Loans):

  • IMPUTED_TOTAL_FEES
  • IMPUTED_TOTAL_FEES_AUD
  • IMPUTED_TOTAL_FEES_EURO
  • IMPUTED_TOTAL_FEES_STG
  • IMPUTED_TOTAL_FEES_YEN

An update of the SDC Platinum software is required to keep everything up to date and available as per usual. The newer Thomson products Thomson One and Eikon are also impacted but as these products are web-based no software updates on the client side are necessary. The data in the newer products has already been updated. More background information can be downloaded here.

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Thomson Eikon & Event windows

Not too long ago I was working with the successor of Thomson 1 / Thomson One which is called Eikon or Thomson Eikon. This product is web-based and is similar in look and style as Bloomberg. I found out that in most cases for me the Excel add-in was the most useful part and allowed me to download larger amounts of data.
The specific Add-in did, however, miss a key feature which I use heavily in Datastream: there is no Request Table tool to allow event studies with changing dates and time windows. Using the add-in I was able to figure out how to still do something like this using the commands which Thomson inputs in your Excel worksheet to call up the data.

With a bit of help from the Eikon Helpdesk I was able to come up with a template that kind of does the same thing as a Request Table Excel tool from Datastream. You can download the example here.

Explanation of the worksheet:

0) Before using the worksheet template, make sure that the connection to Eikon on your computer is not yet live. Otherwise the sheet will immediately start updating as the Add-in and Eikon code is usually set up this way.

1) The first sheet is called Events
As the name already suggests, this is where you put the codes for the series (column A). In columns B and C the Start and End dates for the series go. If you need an exact number of trading days (of data) you can use the formulas from a previous post.
If the exact number of days need not be the exact same column D shows the number of calendar days between the start and end days. This is done by deducting the end date from the start date. This works fine as dates are also numbers in Excel. Cell E2 shows the highest number in the entire column D using formula =MAX(D:D)

2) The second sheet is Data
The example sheet is used to download the Eikon data into and is based on generated Eikon code(s).

Cell D1 in sheet Data is linked to Cell E2 from the Events sheet and will show the same value with the formula: =Events!D2
This number is important as this indicates the (maximum) number of rows for each new window to start downloading data without overwriting the data from previous (downloaded) series. This may cause empty rows between series/data, but these empty rows can be deleted afterwards


The formulas in Column C are used to calculate the row/cell numbers for each separate series to start downloading: =((B4-$B$3)*$D$1)

This is also based on the generated number of events in columb B: =B3+1.


The Helper Column is column A. This column is needed to get the download destinations to be included later in the Cells with the Thomson Eikon codes. There is just a technical reason for this, otherwise the Eikon code does not accept the generated destination cells. The formula is: =ADDRESS($C4;6) where the $C4 is the number of rows it takes from column C and 6 indicates the column number where the download should start.


The download code from eikon is as follows: =TR(Events!$A2;”TR.PriceClose”;”Frq=D SDate=#1 EDate=#2 Curn=EUR CH=Fd;date;IN Transpose=Y NULL=NA CODE=ISIN”;INDIRECT($A3);Events!$B2;Events!$C2)

  • This formula takes the series data from the Events tab in column A where it says Events!$A2
  • The start & end dates are included at the end where it says: Events!$B2;Events!$C2
  • The data download cell destinations for the events are indicated where it says: INDIRECT($A3)

You just copy everything downwards (including the Eikon formula) depending on the number of events in the dataset.

3) When you are done copying the events in to the sheet Events and copying the necessary formulas in the Data sheet, you can then go live by signing in into Eikon. You will see that the data download starts automatically.

N.B.: The example download sheet downloads the closing price for series with the Eikon datatype code TR.PriceClose. If you need another datatype this part needs to be changed in the formula.

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