LibAnswers Turns One

LibAnswers is an FAQ/Knowledge database platform that has been in use by the Penn Libraries for the past year. The questions and answers in the FAQ database are supplied by Penn Librarians familiar with the typical queries of students, faculty and staff. The Business FAQ, Instant Answers, replaces a decade-old FAQ interface that was developed by Penn’s Library Technology Services.

The Business FAQ questions in Instant Answers  range from the routine (“Where can I find the Wall Street Journal”) to the specialized (“How do I Construct a Currency Futures Contract on Bloomberg”). In the past year, business FAQs were viewed 10,000 times.
To see how the FAQ works:

FAQ Screen1



From Lippincott’s home page  type a question or a series of key words in the search box (located on the upper right of the screen) and click on ‘ASK”.   Then click on any appropriate FAQ retrieved.




Answers typically provide definitions and a variety of links to sources.

FAQ Screen2

When a questions is broad or complex, links are often provided to a Library Guide or to a Blog post that treats the topic in depth and which provides the name of a Library subject specialist.

Here, for example, is a Guide to Health Care which was linked from a FAQ on the subject. Notice that the Guide also includes an FAQ “widget” that will allow you explore further.

FAQ Screen3

The distribution of FAQ questions and the number of views follows a typical 80/20 pattern; about 80% of the FAQs viewed are from 20% of the FAQs in the database. (See this article
for a discussion of the 80/20 distribution)
TAB FAQ Cum percentage graph for blog


The 80/20 ratio implies that about 64% of the views will come from 4% of the FAQs, but the actual distribution is 64 to 1; that is, 64% of the views come from 1% of the FAQs. The single most heavily viewed FAQ (“Where can I find Analyst Reports?”) was viewed more than 2,700 times in the past year, about 28% of all business related FAQ views.


The word cloud below shows the relative importance of the FAQ questions.


FAQ screen 4



The FAQ is just one way the Library can help you with your research. Here are some additional options.

What’s Your Company’s RQ™ (Research Quotient)?

Research and Development (R&D) expenditure is the amount of money a company spends on developing new products and services each year.  Academic business researchers have intensively investigated the relationship between a company’s R&D and its market value, and have searched for ways to derive a firm’s optimal R&D spending.  A recent innovation in the analysis and measurement of a firm’s R&D has been the development of the concept of Research Quotient (RQ).

A company’s Research Quotient is the percentage increase in the company’s revenue from a 1% increase in its R&D. RQ is a measure of a firm’s ability to generate revenue from its R&D expenditures.  RQ is calculated from a formula that combines a company’s measure of capital, labor and R&D. For more details concerning RQ calculation, click on Manuals and Overviews from the WRDS Research Quotient database.

RQ can be used:

  • To Link R&D spending to firm growth
  • Link R&D spending to market value
  • Derive a firm’s optimal R&D spending

The WRDS RQ database includes RQ measures for all companies in the COMPUSTAT database that report R&D expenditures. The data covers 1972 to 2010 and is updated annually.  The file allows searching by 4 digit SIC and by GV Key (COMPUSTAT’s unique company identifier).

Table 1 is an example of the output showing some of the default variables.


  • “Raw RQ” is the “Research Quotient” that identifies the ability of a firm to generate revenue from its R&D expenditures. The higher the RQ the greater the revenue generated.
  • “RSTAR” is a calculation of optimal R&D expenditure.
  • “RD Ratio” is the ratio of R&D expenditure to Revenue.

In Table 2, for clarification, I have supplied tickers and names of companies together with a measure of “RQ” that I calculated from the “Raw RQ” supplied by WRDS. This RQ is analogous to the human IQ measure with a mean of 100 and a standard deviation of 15. An RQ with a mean of 100 is often used by academic writers as a way of making the RQ measure more intuitive.

Table 2 ranks the first 20 companies in the U.S. by their RQ in 2010.


There are more than 260 four digit SIC codes represented in the 2010 files, but only 10 codes have more than 35 companies. Table 3 collapses the codes into 2 digits, and ranks the average RQ of the largest 15 industry groups.


About 78% of the companies in the 2010 file were based in the U.S.  Figure 1 graphs the countries with 3 or more companies in 2010 by average R&D expenditures and average RQ.


The principal developer of the RQ concept is Anne Marie Knott, Professor of Business at Washington University, St. Louis. In a 2012 article in Harvard Business Review, she estimateds that if the 20 largest US firms had optimized their R&D expenditure in 2010, they would increase their aggregate market capitalization by $1 trillion. (Knott, Anne Marie. “The Trillion-Dollar R&D Fix.” Harvard Business Review (90:5) 2012, pp. 76-82.) This article can be accessed using Business Source Complete.

Screening for Alumni-Company Links

Job seekers are often interested in identifying companies that employ alumni from their schools. Here are brief descriptions of four databases that  uncover alumni-company links. The databases report on different although overlapping populations, and vary in the number and type of screening variables they provide. The biographical information given typically includes contact data, employment history, and, if publicly available, compensation.
Continue reading

Stock Price Archeology

April often brings us a spate of income tax related questions about the price of a stock on a particular date. The questions are usually from people who want to establish the cost basis of a stock for tax purposes.  Many standard financial databases can supply daily prices (high, low, close and volume) at least for the companies on the New York Stock Exchange as far back as the early 1960’s.

Table 1.


Here are the earliest dates available for daily prices of General Electric Company (GE) on several financial files.

Continue reading

Ruble Regression: Exploring Correlations with Bloomberg.

In June 2014, the price of oil began to fall from its high of $115 a barrel. The value of the Russian Ruble, as well as the currencies of all major petroleum exporting countries began to drop along with the price of oil. Bloomberg has several correlation modules that allow us to examine the link between market variables. For example, we can quickly explore the relationship between exchange rates and oil prices using Bloomberg’s HRA program.

To plot the Russian Ruble / US Dollar exchange rate against the price of oil in Bloomberg, type:  HRA <GO>

Ruble regression value latest

Continue reading

Capital Cube: Not your Father’s Stock Screener.

Financial databases from Bloomberg to Yahoo Finance can screen equities based on a combination of standard financial variables and ratios, analysts’ estimates, industry and location. But if you want to identify companies with, “Aggressive Accounting Practices”, a high “Fundamental Analysis” score or possible “Sandbagging” (understated or hidden earnings) you will need a different type of stock screener. Try Capital Cube. Capital Cube Menu   As can be seen from the Capital Cube menu, the screening options are unusual. Capital Cube creates unique variables by taking the raw financial data from individual companies and comparing the data with averages from a group of peers. For example, a company is tagged as employing “Aggressive Accounting” when “…the company’s net income margin is higher than its peer median while the percentage of accruals is lower than peer median”. Capital Cube states that this situation is usually indicative of a company with an aggressive accounting policy. Capital Cube computes a daily “Fundamental Analysis” score for each company in its database. “The Fundamental Analysis score is calculated by comparing the company’s performance relative to peer companies across multiple attributes like relative valuation, valuation drivers, operations diagnostic, etc.”

Capital Cube graph


Capital Cube uses fundamental data from the FactSet financial database. It includes more than 45,000 companies worldwide.

For additional information on equities screening see the Business FAQ:

 How can I screen for equities using criteria of my choice?

For information on FactSet see the Business FAQ:

Can I access FactSet through Lippincott Library?

Back to the Future: Finding Historical Economic Forecasts


Business and economic forecasts that are past their shelf life, such as GDP forecasts for the year 2010 made in the year 2008,  might seem to be of little value.  But business researchers examine old forecasts to test their accuracy or to better understand the economic climate of a period. It is well known that forecasters almost universally missed predicting the “Great Recession” business decline of 2008/2009. For example,  The Economist’s  Poll of Forecasters for Jan 12, 2008 (pg. 89) predicted that U.S. GDP would increase 1.8% in 2008 and 2.6% in 2009.  GDP actually fell slightly in 2008 and was down 2.8% in 2009.

Here are some sources of historical forecasts that will let you exercise 20/20 hindsight.

Continue reading

Smoking Out Cigarette Data with PASSPORT


Sean Griffin from Euromonitor’s Passport GMID Database recently sent us a bulletin celebrating the Great American Smokeout. This is an annual event sponsored by the American Cancer Society designed to encourage people to quit smoking for 24 hours with the hope that the decision will be permanent. Sean uses the Passport database to examine smoking habits in the U.S. With his permission, we’ve adopted his examples and descriptions in the following post.



Smoking Habits

Smoking was much more accepted in the past.  For example, RJ Reynolds was a sponsor of The Flintstones in the early 1960’s.  Fred and Barney Rubble became spokestoons for Winston Cigarettes.  RJ Reynolds also went on to introduce the Joe Camel mascot to promote Camel cigarettes.  RJ Reynolds retired Joe Camel in 1997 after the campaign was criticized for influencing children to smoke,

Continue reading

MSCR – Bloomberg’s Municipal Bond Screening Function

Bloomberg’s MSCR function allows you to search a file of more than a million outstanding municipal bonds based on criteria of your choice.

The main search screen shows a search for bonds issued by Philadelphia institutions of higher education.

msrc screen ok

The spreadsheet below gives a page of the resulting list using the default headings. A 154.75 million dollar bond by the University of Pennsylvania is highlighted. Clicking on a row in the spreadsheet allows the display of additional bond details.

Univ PA lisrt

The first page of a description of the Penn Bond is shown.

Penn bond detail

The columns in the output can be edited. For example, for a spreadsheet showing the bonds issued by municipalities with a Moody’s rating of below investment grade (Ba1 to C) we would want a column showing the Moody rating associated with each bond.  Search variables are not automatically added as columns in the display. To add a column or columns, follow this sequence:

Actions => Edit => Display

Choose variables wanted and click APPLY

For more on Bloomberg’s functions, take a look at the blogs in our Bloomberg category and our Bloomberg Help Guide.

A Few WRDS about the S&P 500

The Standard and Poor’s 500 Index, a key measure of the U.S. equity market, closed above 2,000 the first time on August 26, 2014.The Index includes 500 leading U.S. companies and captures about 70% of available U.S. market capitalization.


Standard & Poor’s OUTLOOK

The S&P 500 Index first appeared in March 1957. Described as “The Standard 500” in the OUTLOOK (March 11, 1957, p.908) S&P mentioned the “intricate computing equipment applied to the task” of calculating the Index every hour. Today, the Index is calculated every 15 seconds.


Standard & Poor’s Security Price Index Record

Oddly enough, S&P didn’t list the stocks that composed the Index in the OUTLOOK. You can find the initial list of 500 companies scattered among the 91 industry subgroups that made up the Index in Standard & Poor’s Security Price Index Record for 1957. For the subgroup “Confectionery”, for example, the 500 component companies were American Chicle, Hershey Chocolate, Sweets, and Wrigley (p. 29). The Index has undergone many changes since its inception, both in the companies that compose it, and in the way that it is calculated. Currently, only 86 of the original 500 companies are still part of the Index.

Continue reading