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Preface

Published online by Cambridge University Press:  20 October 2016

Mark J. Bennett
Affiliation:
University of Chicago
Dirk L. Hugen
Affiliation:
University of Iowa
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Summary

In 1994 the Channel Tunnel opened between England and France, allowing high-speed Eurostar trains to whisk passengers from the continent to the United Kingdom and back on a grand scale. What an amazing engineering feat it was for the time (beyond many people's earlier imaginations), yet we take it for granted today. In 1994, Grumman Aerospace Corporation, the chief contractor on the Apollo Lunar Module, was acquired by Northrop Corporation to form the new aerospace giant, Northrop Grumman. It was the prime contractor of the newly deployed advanced technology B-2 Stealth Bomber. On a much more mundane and personal scale, also in 1994, in a townhouse just outside the City of Chicago, I was performing a tedious daily exercise: looking up daily closing prices each evening in a stack of Investor's Business Daily newspapers for the two stock investments that were about to be purchased. This was not only to find out their running rate of return but also to find out their historical volatility relative to other stocks before entering into the positions. Doing this manual calculation was slow and tedious. The WorldWideWeb was introduced in the form of the Mosaic browser the next year. It was not long before Yahoo! was posting stock quotes and historical price charts, as well as technical indicators on the charts, available on demand for free in just a few seconds via the new web browsers.

The advent of spreadsheet software took analysts to a new level of analytical thinking. No longer were live, human-operated calculations limited to a single dimension. Each row or column could present a time dimension, a production category, a business scenario. And the automated dependency feature made revisions quite easy. Now spreadsheets can be used for a prototype for a more sophisticated and permanent analytical product: the large-scale, analytical computer program.

With modern programming languages like R and Python®, a skilled analyst can now design their analytic logic with significantly less effort than before, using resources such as Yahoo! or other free services for historical quotes. It has been said that Python's terse syntax allows for programs with the same functionality as their Java equivalents, yet four times smaller, and we suspect that R is similar. A small financial laboratory can be built on a laptop costing less than $200 in a matter of weeks, simulating multiple market variables as required.

Type
Chapter
Information
Financial Analytics with R
Building a Laptop Laboratory for Data Science
, pp. xiii - xvi
Publisher: Cambridge University Press
Print publication year: 2016

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  • Preface
  • Mark J. Bennett, University of Chicago, Dirk L. Hugen, University of Iowa
  • Book: Financial Analytics with R
  • Online publication: 20 October 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316584460.001
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  • Preface
  • Mark J. Bennett, University of Chicago, Dirk L. Hugen, University of Iowa
  • Book: Financial Analytics with R
  • Online publication: 20 October 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316584460.001
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Preface
  • Mark J. Bennett, University of Chicago, Dirk L. Hugen, University of Iowa
  • Book: Financial Analytics with R
  • Online publication: 20 October 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316584460.001
Available formats
×