Archive for the ‘Finance’ Category

When “Big Data” gets it wrong

January 4, 2017

Big Data could provide a big advantage to investors, but when the data is wrong or misinterpreted, it can be catastrophic.

“Credit card data sold to investors is making shares of retailers behave strangely, especially when the data gets things wrong.”  So begins an article in the Wall Street Journal ‘Big Data Adds Up to Trading Distortions.’  The first example is about Tailored Brands (owner of Men’s Warehouse and Joseph A. Bank) stock that shot up nearly 40% in one day when investors realized that the data they were basing their decisions on was inaccurate.

For the full article:  how-credit-card-data-might-be-distorting-retail-stocks-wsj

Deficit v. Debt – knowing the diference

July 21, 2016
In simple terms, a budget deficit is the difference between what the federal government spends (called outlays) and what it takes in (called revenue or receipts). The national debt, also known as the public debt, is the result of the federal government borrowing money to cover years and years of budget deficits.

What’s the difference between the U.S. deficit and the national debt …

money.howstuffworks.com/difference-between-u-s-deficitnationaldebt-.htm

The Truth Behind the ‘Summer Rally’

June 6, 2016

Statistics students should read The Wall Street Journal article about the myth of the summer stock market rally (WSJ, June 6, 2016).  By examining the data the author shows that the summer historically had less rallies than other times of the year, and yet the name, the concept and the belief persists.  The author suggests that the name might go back to the Depression when in the summer of 1932 the Dow Jones Industrial Average gained 76.5% from the low close of June to the high close of August.

Sometimes we see what we expect to see, and no one is immune from the perception bias, but being aware of the tendency makes you better prepared to deal with it in an intelligent manner.

For the full article, click here: The Market’s Summer-Rally Myth – WSJ

Using Big Data to Predict Worker Illness and Pregnancies

February 29, 2016

Not a typographical error!  Companies who hire outside consultants are able to get data about their workforce that borders on a serious intrusion of privacy.  Click below for the whole WSJ article, but just a few quotes might give the sense of what I am talking about.

Bosses Tap Outside Firms to Predict Which Workers Might Get Sick – WSJ

“Trying to stem rising health-care costs, some companies, including retailer Wal-Mart Stores Inc., are paying firms like Castlight Healthcare Inc. to collect and crunch employee data to identify, for example, which workers are at risk for diabetes, and target them with personalized messages nudging them toward a doctor or services such as weight-loss programs.”

“To determine which employees might soon get pregnant, Castlight recently launched a new product that scans insurance claims to find women who have stopped filling birth-control prescriptions, as well as women who have made fertility-related searches on Castlight’s health app.”

“Privacy advocates have raised concerns about such practices. Employees generally have a choice in whether to participate in the programs. The services are new enough that relatively few workers are aware of them.”

“Federal health-privacy laws generally bar employers from viewing workers’ personal health information, though self-insured employers have more leeway, says Careen Martin, a health-care and cybersecurity lawyer at Nilan Johnson Lewis PA. Instead, employers contract with wellness firms who have access to workers’ health data.”

A better way to round off.

March 24, 2015

Round up for 5 or greater is a simple method of rounding off, but it introduces a bias.  See below to find out how to avoid the bias, as explained by the authoritative Math Forum.

There are two ways to round that are commonly taught. The rules we give in most of our answers are those taught to children (and commonly used by computers), because they are simpler but are sufficient for
most purposes. Where statistics matter, and where numbers that END
with the 5 are common, rounding to even is preferred.
When an even decimal (or any even number) is followed by a 5, you
round down. When an odd decimal is followed by a 5, you round up.
For example: 75.45 = 75.4, but 75.55=75.6. Or rounding to the nearest
10, 145 = 140, but 155 = 160.

The “proxy,” an easier way to count.

February 10, 2015

The number of new car registrations can be used as a proxy for new car sales in cases where the new car sales numbers are not available, or the new car registration statistics are easier or cheaper to get.

In an article about the European car sales, the Wall Street Journal reports, “Last year, new passenger car registrations, a proxy for new car sales, rose 5.7% to 12.6 million, according to figures released by the European Automobile Manufacturers’ Association, or ACEA.”

Why is the number of new car registrations a good proxy for new car sales numbers? It is based on the assumption that people who buy new cars will register them and use them right away, so it is reasonable to assume a one to one relationship, with a very high degree of accuracy.

Another example of a proxy would be using the sales of various commodities as a measure of the strength of an economy, i.e., the sale of certain luxury goods. Some proxies are better at mimicking the underlying statistic than others.

News Before its News – The New Digitally Connected World

August 27, 2014

Looking for a good article for stats class discussion to talk about how the digital world is changing the world?  An article in the Wall Street Journal Aug. 27, 2014 talks about how Tweets and data miners can work together to get you the news before the news services.  In the opening example, a stock market moving event is reported to “dataminr” followers before the market started to move.  Now that could be cash in the bank!

Check out the full article at http://on.wsj.com/1vNwxIj   If you have trouble getting connected, go to my Tweet, see link below right on this page.

A $1.1 Billion Error…Math Models Are Not Perfect

August 20, 2014

A billion-dollar forecasting error in Walgreen Co. ‘s Medicare-related business has cost the jobs of two top executives and alarmed big investors.

At an April board meeting, Chief Financial Officer Wade Miquelon forecast $8.5 billion in fiscal 2016 pharmacy-unit earnings, based partly on contracts to sell drugs under Medicare.

Last month, directors got a shock. Mr. Miquelon suddenly cut that forecast by $1.1 billion.

In early August, the CFO of the nation’s largest drugstore chain was gone. Walgreen said several days earlier that its pharmacy chief, Kermit Crawford, would retire at year-end.

Behind the botched numbers and management shake-up are Walgreen’s efforts to capture a larger role as a middleman dispensing prescription drugs under Medicare’s Part D, which subsidizes costs for the elderly and disabled. The saga at Walgreen—which derives 25% to 30% of its prescriptions from Medicare Part D plans—shows the broader risks for those operating in the Medicare ecosystem.

The bottom line: Walgreen hadn’t factored in, among other things, a spike in the price of some generic drugs that it sells as part of annual contracts.

WSJ Aug. 20, 2014

Using Math to Become a Billionaire — Case Study

July 10, 2014

Mathematician, code breaker, professor, stock picker using mathematical methods…. Adapted from the New York Times, July 7, 2014

James H. Simons, a retired hedge fund titan who used mathematical strategies to become a billionaire, is financing a new medical research center that plans to apply similar strategies to investigate serious diseases.

Mr. Simons, along with his wife, Marilyn, has given $50 million to Cold Spring Harbor Laboratory to pay for the creation of the Simons Center for Quantitative Biology, according to an announcement on Monday. With experts in applied mathematics, computer science and theoretical physics, the center will use sophisticated algorithms to analyze biological data.

A former mathematics professor and government code breaker, Mr. Simons founded Renaissance Technologies in 1982, employing science and math Ph.D.s and using computer algorithms to trade stocks.

Scientists’ comprehension of confidence intervals is poor.

July 6, 2014

So much of scientific research involves statistical analysis and conclusions that have to be understood in the context of confidence intervals and margins of error. If our scientists don’t comprehend these concepts well, findings and reports are likely to be very misleading or wrong.

A study shows that this is the case.

https://mrsiderer.files.wordpress.com/2014/07/confidence-interval-comprehension.doc