All posts by Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

How long does it take to build a house in 2017?

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

Ever wonder how long, on average, it takes to build a house? According to the most recent data from the U.S. Census Bureau’s Survey of Construction, it takes nearly seven months on average. More than half (52.4 percent) of all new housings are finished within five months (compared to just 46.7 percent 10 years ago), and 93 percent are completed within one year.

Of course, these numbers don’t come close to telling the whole story. Let’s see what the primary factors are that influence the duration of construction.

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The “half your age plus seven” rule — how often is it broken?

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

When is an age difference between partners a little too large? A common rule is that you cannot date someone younger than half your age plus seven. For example, if you are 30, dating someone 22 years old is just barely acceptable.

The following chart illustrates this concept: couples between the dashed lines are the ones following the rule.

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Are actor names a popular choice for babies?

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

How do people choose names for their children? It’s no secret that quite a few name their boys and girls after different celebrities — actor names seem to be one of the common choices.

Is this truly the case? Were there a lot of Dustins born right after “Rain Man” hit the cinema screens? Let’s take a look.

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How important is peer-to-peer loan diversification?

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

In recent posts, we have seen that both the Prosper and Lending Club loan grading systems do their job quite well, allowing you to assess your risks with each particular borrower.

What it doesn’t show us, however, is if there is any correlation in the loan charge-offs. As you may know, one of the reasons for the 2007 subprime mortgage crisis was the underestimation of the probability of a widespread wave of default events.

Now, we should probably provide an important disclaimer: there is no way to estimate the probability of so-called “black swan” events given the data we have. We can only manage systematic risk, and any smart investor should keep in mind that there will always be risks not present in his or her model.

So, with that in mind, let us assume that the individual default events are all independent. We’ll now see how well this assumption fits the data we can download from the Lending Club website.

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The Prosper default rate decreased in 2014

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

We saw in a previous post that Lending Club default rates show relatively low correlation. That, of course, makes systematic risk assessment easier. At the same time, however, this means that geographic diversification doesn’t help investors decrease the risk of their portfolios.

We now take a look at the same chart for the Prosper default rate. Using the historical estimates for the monthly loan default rates for each of the seven Prosper borrower ratings, we project both the expected default rates and the error margin.

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Hottest innovation trends in New York

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

New York is home to the company that leads innovation in the United States: International Business Machines Corp. (IBM). In the last five years, the total number of patents issued to IBM is equal to the sum of the three next largest companies (Microsoft, Qualcomm and Google) combined!

Let’s see what kind of impact such an organization can have on statewide innovation trends.

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Innovation in Silicon Valley

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

One particular area always stands out when discussing innovation in the United States: a portion of Santa Clara County, CA better known as Silicon Valley. The valley serves as a home to several of the largest high-tech companies in the country.

It’s not about the high-tech giants, though. The number of companies which were issued at least one new patent has doubled since 2000. The number of individually issued patents also keeps growing slowly but steadily, as evidenced by the chart below.

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Cloud technology and its evolution in the U.S.

Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

What is cloud computing and why is it important?  In recent years there has been a surge in the number of internet-based services that provide shared computer resources, minimizing both upfront and continuing maintenance costs for new projects. It is often regarded as most useful for smaller setups as it allows access to a significant amount of resources, regardless of the project size.

Despite being technically present since the 1990s, the technology only really took off in 2009 (Microsoft announced its Azure service in October 2008). After this, it didn’t take long for major U.S. companies to start patenting their developments.

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