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Job Postings Trend Correctly Predicts Layoffs

Published: July 18, 2014

By: Kevin Coogan

Yesterday, Microsoft announced layoffs.  These layoffs were correctly predicted in our blog post Microsoft’s Coming Layoffs? just a few days ago.

The important point to highlight is not that we made a good call — people in the market make good calls every day — but that we did it using a new form of data.

Predicting a job layoff announcement looking at job posting trends is entirely new, at least we have not seen a similar type of analysis.  It leverages the open web, publicly available information, and of course common sense.

The logic backing this type of analysis is very intuitive and easy to understand.  Struggling companies, those likely to layoff a portion of their workforce, will tend to decrease hiring prior to the announcement of layoffs.  Company management understands that there is a problem with the outlook of the company and begins to adjust well before the official decision, let alone announcement, of layoffs.

Microsoft’s trend in job postings has been on a clear downward trend for the last year.  In the face of a strong job market such a downward trend is a red flag.  Additionally, within 24 hours of the company’s new CEO stressing “change”, its job postings declined dramatically — with the largest one day drop of the previous 12 months.

The actual announcement was aggressive, stating that company plans include “the elimination of up to 18,000 positions over the next year”.  Such layoffs amount to the largest in the company’s history.  We would expect to see signals prior to the announcement in the trend in job postings, and this is exactly what happened.

Monitoring job postings on a company-specific level will most likely become an integral part of fundamental company analysis.  It is useful in providing insights into a company’s health, its growth prospects, and the timing of change of its strategy, among other things.


Microsoft’s Coming Layoffs?

Published: July 15, 2014

By: Kevin Coogan

Satya Nadella, Microsoft’s CEO, has the markets speculating about its future. In his July 10, 2014 memo entitled Bold Ambition & Our Core he frequently mentions “change” and “focus”. Intertwined in his message is the potential for change so drastic that Microsoft could institute layoffs, an unusual event for the company.

Currently, such speculation is just that, speculation. However certain the markets make things appear, big announcements such as layoffs are historically difficult to predict. There are just so many variables to take into account such as history of layoffs at that company (No, $MSFT does not have a strong history of layoffs), recent acquisitions (Yes, recent purchase of Nokia Oyj’s handset unit), management changes (Yes, new CEO), competitive pressures (Yes, $MSFT feeling intense pressure from a variety of competitors), etc.

Even if you expect Microsoft to announce layoffs, why this week and not in a few quarters? Timing can be extremely difficult to ascertain, even when done by the best analysts and journalists. In this case, a recent Bloomberg article Microsoft to Announce Job Cuts as Soon as This Week clearly states that a layoff announcement will happen very soon. It appears the markets expect quick movement on this.

The above analysis is very traditional. This is how things have been done for decades. Analysts and commentators look at the indications provided by company executives, combine it with some “insider” comments (who always seem to want to remain anonymous, leaving in doubt the reliability of the information) together with some fundamental analysis mentioned above and you have the formula for ripe speculation.

Living in a data driven world, we should be able to improve or at least buttress this analysis. By looking at the trends in job postings on a company level, the probability of layoffs should become apparent.

In Microsoft’s case, its job postings have been trending down for some time. Looking at its trend over the last year, you can clearly see a general trend downwards. In a generally strong job market, this indicator should be seen as relatively negative. Frankly, an analyst with this data prior to the Memo could have logically concluded the potential for layoffs in the near future.

Microsoft Job Postings_memo


Perhaps the most telling for our current purposes of verifying a short term announcement for layoffs is the timing of the Memo versus the trend in job postings. The Memo came out on July 10. By July 11, the number of job postings for $MSFT had declined dramatically.

This could be a coincidence, but not likely. The fact that the within a day of the CEO putting forth an implied new direction for the company the HR department culled its job postings is a serious clue of future developments.

If the presumed layoffs were to just focus on the integration of Nokia’s unit, then you would not expect such a sharp decline. It appears from the overall trend of the company’s job postings together with the Memo timing, that layoffs will likely be announced, will be announced somewhat soon, and that they will cover the broader company, as opposed to just one unit.

In terms of relevance of this relatively new data of company-specific job postings, it appears that its usefulness is apparent for fundamental purposes and for timing of / confirming of speculative events.




To Hadoop or not to Hadoop

Published: April 29, 2014

By: Kevin Coogan

Hadoop has become synonymous with Big Data. And, more recently it has become an increasingly polarizing topic, in many ways like the term Big Data itself.

Hadoop was not an original to finance. It is slower with real-time data and in comparison to relational databases which dominate finance is less apt to slice and dice data. Hadoop however has its own advantages – not least of which is its distributed nature which allows for faster larger scale analytics. Another major advantage is its file system, Hadoop distributed file system or HDFS. It is excellent at storing unstructured and semi-structured data that do not have a well and previously defined schema. Such a schema fits new and novel data sources especially well.

It is important to stress that when people refer to Hadoop they are often mixing terms. Some people refer to Hadoop as being map-reduce (or the distributed and batch oriented processing), whereas others imply the file system or HDFS, and others are really talking about the Hadoop ecosystem which today includes many other open-source Big Data projects that integrate with or leverage the Hadoop infrastructure.

Going forward, it would appear that map-reduce will lose traction. It is inherently slow for real-time analysis, but actually good for certain larger scale calculations that might not be as time critical. Over time, batch type analysis will become less important, especially for finance, but will still continue to be used. Especially for overnight type calculations or end of day analysis, map-reduce will continue to dominate.

There are many new projects emerging ready to fill the gap between streaming Big Data analytics and Hadoop. Such projects as Storm and Spark allow for increasingly real time coverage, and resolve many of the issues revolving around Hadoop’s slower real-time analysis. Summingbird, a newer open source project, actually focuses on bridging the gap between Hadoop and real-time by creating a platform to dial back and forth between these two paradigms. In short, Hadoop’s map-reduce will likely decrease in usage and importance but it will not disappear – it will be used for less time-critical cases and will be buttressed by emerging open source projects made to fill in where map-reduce left off.

Its file system, HDFS, in contrast appears to have become fully ingrained in the Big Data world. It appears that even though map-reduce will diminish in importance over time, the file system will remain. That is not to say that all data will necessarily be required to land there before analytics take place. In fact, there are considerable financial uses cases where streaming data is first analyzed outside of Hadoop, only to end up in HDFS later for longer term storage and batch processes.

As for finance in general, most will likely continue to rely upon relational databases. This will especially be true for highly structured data that have very well defined use cases. If you want the data fast to do the same or similar types of straightforward analysis, relational databases are likely a good choice and will continue to be. If you want to leverage new data sources, especially those that contain unstructured or semi-structured data where the use cases and analytical paths are less well defined, you will likely end up using, or at least wanting your service provider, to use Hadoop and its related ecosystem.

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