Welcome to Business Research Plus

July 27, 2011 1 comment

Bloomberg Suite

From specialist databases to business literature, Business Research Plus provides advice and tips based on the MBS library service resources and expertise.  See our About page for more details.

Exploring our resources – try the Business and Management Resources page (LibGuides),  scroll down to category or tag cloud in the right-hand column, search this blog (top right) or search Manchester Business Answers 24/7 (FAQ). For latest news see @MBSLibrary 

In 2013 we had 64 new posts, just over 110,000 views, and All UK listed companies (part 1) was the most commented post. Of the top 5 posts Journal Ranking – marketing was from 2012, three were from 2011, and one from 2010. Please read Reflecting on Business Research Plus and comment with your suggestions for improvement. [22 January 2014]

(MBS Library Service is part of The University of Manchester Library. )

Finding Scottish companies with FAME

September 15, 2014 Leave a comment

With the referendum on Scottish independence this Thursday (19 September 2014) there is much news and debate on the potential impact on businesses of a yes vote (BBC News, 2014). To research further you could use the Fame database to identify Scottish companies.

FAME, from Bureau van Dijk, covers UK public and private companies and has a very good search interface for selecting companies. We can select companies by location and one of the options for giving a location is country.

We select “Scotland” as the country. The country search page clarifies that this means companies with a registered office address or a primary trading address in Scotland. (These defaults can be changed – for example if we only wanted companies with their registered address in Scotland.)

From a Fame search the largest Scottish companies by operating revenue (turnover) are given in the screenshot below.


FAME uses a standard set of variables and orders then by operating revenue as its default view of a result list. This can be changed by adding extra variables as required, and by selecting the arrows next to the variable names to reorder. In the screenshot two extra variables have been added: Listed/Unlisted/Delisted and BvD independence indicator.

The Listed/Unlisted variable shows the companies that are Listed (Public) companies – that is have shares/stocks traded on a stock exchange – and those that are Unlisted (Private) companies. The largest private Scottish company is Arnold Clark Automobiles Limited – eighth largest by operating revenue. One of the strengths of FAME is that it includes both public and private companies.

This search also used “Ownership – BvD independence indicator” to refine the search and remove subsidiaries from the results list. An ownership search criteria was added to the Scottish location criteria. This was that companies had to have a BvD independence indicator of A or B, or be listed companies.


tbc http://www.bbc.co.uk/news/uk-scotland-scotland-politics-29169114


Categories: company information Tags:

Historical utility futures data

August 29, 2014 Leave a comment

BloombergBloomberg provides current and historical futures prices for European utilities commodities. In this example, we will look at Nordic electricity (Nordpool) historical prices, looking ahead one week, from August 2013 for one year. We will focus on two Bloomberg functions: Forward European Utility Markets (EUM<GO>) and Commodity Futures Overview (CMBQ<GO>).

Method 1: European utilities

From the Bloomberg command line, type EUM<GO> (that is, the letters EUM followed by the Enter/Return key) to cal up the Forward European Utility Markets screen. You will see an overview of the latest prices for the most common power, gas and other utilities markets in Europe. In the red menu bar, click on 1) Markets > Power > Nordic Power (Nord Pool). This will change the display to one like the image shown below.

Forward European Utilities Markets: Nordic power (blurred)

Forward European Utilities Markets: Nordic power (data blurred for security)

You can change the currency in the labelled orange box; type NOK to change to Norwegian Krone. The first row of data is for 1 week ahead, with the ticker NEL1W Comdty. Right-click this to get options to find out more about this ticker, such as DES – Description. Instead, choose GP – Historical Price Graph to get straight to the chart. This will load up a child window with the price chart for that commodity. From here, you can treat it like any other chart in Bloomberg, such as editing the date range to 08/01/2013-08/01/2014, and the currency to NOK. You can access the data that was used to generate the chart by clicking in the red menu 37) Edit > Copy Data to Clipboard.

Now you know the ticker, you could come straight to this chart with the command NEL1W <F9> GP <GO> (using the F9 key on the keyboard labelled CMDTY).

Bloomberg Nordic power future chart

Historical price graph of commodities future data for Nordic electricity (blurred numbers)

Method 2: Global commodities

There is an alternative route to this ticker. Enter the command CMBQ<GO> and you will be presented with a screen showing and overview of global commodities futures. Click on the red menu 90) Market > Energy > Nordpool Custom Quote to filter the display to Nordic electricity.

Commodity Futures Overview Nordpool (blurred)

Commodity Futures Overview for Nordpool (data is blurred)

This time, you will see a different summary of more kinds of information about that commodity and related items, including futures prices for Baseload and Peakload; before we just saw Baseload but had more choice of the different look ahead periods. You can right-click on the W1 ticker on the first row of price data to get to some of the same options as before, such as DES – Description. From there, you could find the price graph.

Price – adjusted and unadjusted (Berkshire Hathaway)

August 19, 2014 Leave a comment

The BBC business news recently posted a report about Warren Buffet’s Berkshire Hathaway that is a great example of the difference between adjusted and unadjusted prices. See Warren Buffett’s Berkshire Hathaway shares top $200,000, 15 August 2014.

The shares in Berkshire Hathaway surpassed $200,000 because Warren Buffet has never split the company’s class A shares. The class B shares are much cheaper because they have been split.

The screenshot below shows the adjusted price (P), unadjusted price (UP) and adjustment factor for Berkshire Hathaway class A (U:BRKA), class B (U:BRKB) and Google class A (@GOOGL). The Berkshire Hathaway class A shares have never been split so the price and unadjusted price are identical, while the class B shares were subject to a 50 for 1 stock split in January 2010.

(adjusted) price, adjusted price and adjustment factor (click to expand)

(adjusted) price, adjusted price and adjustment factor (click to expand)

The Berkshire Hathaway class A share/stock worth $87,900 on 31 December 2004 is the same as the one worth $188,124 on 31 July 2014. The class B share/stock worth $2,936 on 31 December 2014 is equivalent to 50 class B shares worth $125.43 on on 31 July 2014. The adjustment is always done historically so the class B share worth $125.43 on 31 July 2014 has an adjusted price of $58.72 on 31 December 2014.

  • adjusted price (P) = unadjusted price (UP) * adjustment factor (AF)
  • 58.72 = 2936 * (1 / 50)

The Google class A shares are very slightly different. The Google class A share/stock worth $192.79 on 31 December 2104 was “split” in April 2014 with shareholder getting one Google class C share for every class A share owned. The Google A share worth $579.5498 on 31 July 2014 has an adjusted price of $96.4885 on 31 December 2014. (The adjustment factor is not exactly 0.5 because of a small price difference between Google class A and Google class C stocks.)

The screenshot above is from a Thomson Reuters Datastream request table with 2 requests – the first producing annual values for the end-of-year from 2004 to 2104, and the second producing monthly, end-of-month, values for December 2013 to July 2014.

Categories: Business Databases Tags:

Journal ranking – August 2014 update

August 11, 2014 Leave a comment

The journal rankings based on articles published in 2013 are now available.

The best known journal rankings are the Journal Citation Reports (JCR) from Thomson Reuters. These are not available for free – staff and students from University of Manchester have access through our Web of Science (formerly Web of Knowledge) subscription.

Select the Journal Citation Reports link or select Web of Science and then the Journal Citation Reports tab. Once at the JCR home page select the Journals by Rank tab.

The primary variable calculated by JCR is the Journal Impact Factor (JIF). The new interface also offers a journal connection visualization.

Web of Science - JCR 2013 (click to enlarge)

Web of Science – JCR 2013 (click to enlarge)

The above screenshot is for Incites Journal Citation Reports – year 2013; category business, finance; and edition SSCI;

For more information – Journal Citation Reports Data Release 2014  (JCR 2013 data)

SJR IconThe SJR SCImago Journal and Country Rank – Journal Rankingbased on Scopus data, now have 2013 as their latest year.

The SJR indicator, developed by SCImago, is not as well know as the JIF factor from the Journal Citation ReportsHowever the metrics are freely available – they are based on Scopus rather than ISI Web of Science so more business and management journals are covered, and more business and management subject categories.

The screenshot below shows results for the subject area Business, Management and Accounting. There is  a category Marketing in this subject area (see Journal ranking – marketing posted August 2012). The subject category Finance is in the separate subject area Economics, Econometrics and Finance.

SJR journal ranking – subject area Business, Management and  Accounting (click to expand)

SJR journal ranking – subject area Business, Management and Accounting (click to expand)

The CWTS Journal Indicators are also based on Scopus data, and again 2013 is now the latest year available.

Google Scholar Metrics currently covers articles published between 2009 and 2013 (both inclusive) and are based on citations from all articles that were indexed in Google Scholar in June 2014. (details available via the learn more link)

Google Scholar does not appear to make historical metrics available.

There is an example screenshot in the Journal Ranking – August 2013 update post.

The EAJG (ABS) journal quality guide 2014 is expected later this year – latest progress report.

The latest 2010 version 4 of the  ABS Journal Quality Guide is available form the www.bizschooljournals.com/ archive.  See also  Journal ranking – August 2013 update

Thanks to the Academic Trends & Innovation blog for the latest JCR released post – a reminder that the Journal Citation Reports (2014 edition) is now available.

Beta values from Datastream

There is a datatype BETA available in Thomson Reuters Datastream  but often this is not what researchers are looking for when they are looking for “company betas”.

The Datastream datatype BETA is static – only the current value is available – and it is also adjusts the raw beta value to make it a forecast beta.

There is a formula that you can use to get a time series of historic beta values: REGB#(LN#(X/LAG#(X,1M)),LN#(Y/LAG#(Y,1M)),60M). This uses the regression beta function with three parameters.

  1. The return of the market over one month – LN#(X/LAG#(X,1M)
  2. The return of the equity over one month – LN#(Y/LAG#(Y,1M)
  3. The time period – number of observations in the regression – 60M

The screenshot below shows the historic beta values for the June 2004 FTSE 100 constituents yearly from 31 December 2000 to 31 December 2013. The market return is the FTSE All Share – LN#(FTALLSH/LAG#(FTALLSH,1M). The equity return is LN#(X/LAG#(X,1M) – X will be each of the list constituents in turn, and the time period is 60 months.

Datastream - historical beta values for ftse 100 (click to expand)

Datastream – historical beta values for ftse 100 (click to expand)

The results in the screenshot come from two entries in a Datastream request table

  • Static request for constituent list LFTSE1000604 and datatypes NAME,ESTAT,TIME,WC09802,BETA
  • Time series request for LFTSE1000604 and the beta formula REGB#(LN#(FTALLSH/LAG#(FTALLSH,1M)),LN#(X/LAG#(X,1M)),60M)

The results show that care is required for dead companies. The formula continues to generate results long after the last valid price data (shown by TIME) so for dead companies the beta returned is always zero once the company has been dead 5 years (60 months).

The Worldscope beta datatype (WC09802) just gives the latest value (see Worldscope manual for details). If you use this in a time series request then you just get the same value repeated. In contrast the static datatype BETA gives an error if used in a time series request.

You can use the local index datatype (LI) rather than explicitly choosing the index in the beta formula.

  • REGB#(LN#(X(LI)/LAG#(X(LI),1M)),LN#(X/LAG#(X,1M)),60M)

This will give the same values as displayed above as X(LI) is FTALLSH for all these LSE listed shares.

Related Posts

Beta values on Thomson ONE.com (April 2014)

Beta values for companies (March 2010)

Beta and choosing the market (researchfinancial blog,  May 2014) – includes a useful Excel sheet for constructing a Datastream beta formula

Categories: company information Tags:

US multinationals

A query about finding the top 30 US multinational companies from 1975 onwards has turned out to be very difficult.

Since we are looking at US companies WRDS is the place to start. CRSP Compustat Merged (CCM) provides both the stock price data (from CRSP) and the accounting data (from Compustat) and provides excellent historical data.

However, after some investigation we could find no easy way of finding multinational companies using WRDS-CCM or WRDS-Compustat. We were looking to be able to rank the companies on foreign/international sales and then select the top 30 for each year.

Thsomson Reuters Datastream does provide a foreign/international sales dataype (WC07101). As a test of large US companies we select a historical S&P 500 constituent list (LSP500I0901 – S&P 500 constituents September 2001) and get sales, international sales and foreign/international sales % of total sales for 31 December 2001.

US cos International Sales 2001 (click to expand)

US cos International Sales 2001 (click to expand)

This screenshot shows the results ordered by international sales and certainly has companies that we would recognise as large US multinationals. However, scanning the results include many companies where there are no sales figures and some where only (total) sales available.

Further tests for international sale and total sales for the September 2001 S&P 500 constituents (LSP500I0901) shows that Datastream has no data before 1980. Further checking has shown that there is another better S&P 500 constituents list with 12 additional years of historical data (LS&PCOMP0989 – September 1989, LS&PCOMP0901 – September 2001) see notes below. (However, the results in terms of sales and international sales data are the same.)

In summary,  Datastream has some useful data but it is incomplete, and only goes back to 1980.

Returning to CRSP Compustat Merged on WRDS (CCM) and selecting a small number of companies by hand we can check that CCM does have total sales from 1975 onwards

CCM (WRDS) historical sales (click to expand)

CCM (WRDS) historical sales (click to expand)

By now we have realised that something that sounds easy, finding large quoted multinational companies, is not. If US companies only have to report total sales in their accounts then databases such as Compustat and Worldscope/Datastream will only have foreign/international sales if the companies choose to report this. We can check this by getting the annual reports for individual companies.

If something is harder then expected it is also worth looking at related work to see exactly how they have identified US multinational companies.

As a check I had a look at Capital IQ. This was similar to Compustat in terms of the datatypes (variables) available, which is not surprising as they are both products from Standard and Poors (S&P), but more limited than Compustat in terms of the historical data available.

Bloomberg – I have still to check – however I don’t expect it to be better than Datastream in terms of historical data or in terms of the not available data.

S&P 500 Constituents

While preparing this post, I have discovered that there is more that one S&P 500 constituent list on Datastream.

  • LS&PCOMP from Standard and Poor’s, LS&PCOMP is the current constituents, LS&PCOMP0989 is the September 1989 constituents (oldest available), LS&PCOMP0901 is September 2001, and LS&PCOMP0714 is July 2014 (newest on 4 August 2014)
  • LSP500I (no source provided), date is unclear since several members are acquired, merged or delisted. LSP500I0901 looks to be a correct constituents list for Sept 2001.

Datastream changes: S&P Historical Indexes (posted June 2010 Financial Databases and Research) confirms that LS&PCOMP is the best constituent list for the S&P 500.

Compustat – North America – Index Constituents on WRDS will also give the S&P 500 constituents – use GVKEYX 000003 or TIC  I0003. Historical data is available from March 1964. See Constituent list in Compustat (posted September 2010 Databaser blog) for more details.


Categories: company information Tags: ,

FAQ: CUSIPs in WRDS output

July 25, 2014 Leave a comment

Phil Reed:

Using CUSIP codes can cause headaches in Excel. They can be confused with numbers expressed in scientific format or leading zeroes can be lost. The blog post below, written by EDSC, offers some assistance on the subject.

Originally posted on EDSC tips & hints:

When you download data from WRDS to an Excel spreadsheet and include CUSIPs in your file, you often have CUSIPs that that look weird. See this example from CompuStat North America Index Constituents, the column Company Cusip should contain 9-digit CUSIPs:

cusips with E

The E’s in the CUSIPs of Dun & Bradstreet and Dr Pepper are recognized by Excel as a mathematical constant (2,718) and the CUSIPs of Amazon.com and Apple are incomplete (they start with zero’s, but these are not visible). This can very unhandy if you use these codes as input in other databases.

You can solve this by downloading the data from WRDS in another output format (comma-delimited text or tab-delimited text) and then:

  • Open a new Excel sheet
  • Open the ribbon Data
  • Choose From Text
  • Browse to the CSV or txt file and click Import
  • In the Text Import Wizard, mark Delimited and click Next
  • In step 2…

View original 98 more words

Categories: Business Databases Tags: , ,

Get every new post delivered to your Inbox.

Join 1,016 other followers