357 search results for "market research"

Financial Data Accessible from R – part III

November 8, 2013
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I came across a new source of data which I think is really worth sharing: ThinkNum. It gathers around 2,000 sources of data but more importantly it allows the user to manipulate this data via functions and graphics and there is an R package available on CRAN. Interested readers can find a very good post

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Financial Data Accessible from R – part II

October 30, 2013
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I updated my initial post with two new sources of data and the associated R packages: Datastream and PWT. I also added the fImport package from Rmetrics. Following a reader suggestion, I made the initial table  more interactive, moved  the data description and package detail below the main table and updated them. Enjoy! Source R

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Knoxville R User’s Group Meeting November 1

October 22, 2013
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Knoxville R User’s Group Meeting November 1

The next meeting of the Knoxville R User’s Group will consist of four 20-minute talks followed by an open planning session. It will take place on Friday, November 1, from 2:00 p.m. to 4:00 p.m. at The University of Tennessee, … Continue reading →

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Quant finance blogs

October 22, 2013
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Quant finance blogs

What I’ve learned from updating the blogroll. New entries The easy option is to go to The Whole Street which aggregates lots of quant finance blogs. Somehow Bookstaber missed out being on the blogroll before — definitely an oversight. Timely Portfolio was another that I was surprised wasn’t already there. The R Trader talks about … Continue reading...

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A Nation of Real Estate Agents

October 20, 2013
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A Nation of Real Estate Agents

I read a series of articles related to the goings of the UK housing market, the likely effects of the new Help To Buy scheme, the 10% increase in mean London house price over the last year, and employment statistics. I failed to reproduce some numbers cited in the economist (below). This post talks about this.It all starts with this...

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How To Put Your Meetup On the Map (Literally)

October 14, 2013
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How To Put Your Meetup On the Map (Literally)

This is a guest post by Alan Briggs. Alan is a Data Scientist with Elder Research, Inc. assisting with the development of predictive analytic capabilities for national security clients in the Washington, DC Metro area. He is President-Elect of the Maryland … Continue reading → The post How To Put Your Meetup On the Map (Literally) appeared first on...

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R and Data Week 2013

October 3, 2013
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R and Data Week 2013

by Joseph Rickert Data Week 2013 is being held this week in sunny San Francisco at the Fort Mason conference center overlooking the Bay. Holding a Bay Area R User Group Meeting (BARUG) at Data Week helped to raise the R consciousness among the hip conference crowd attracted by the intoxicating mix of blue skies, big data hype, startups...

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Direction of Change Forecasting II: The case of the UK

September 25, 2013
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Direction of Change Forecasting II: The case of the UK

In the previous blog article I discussed a dynamic binary model for the directional forecast of the US equity market using a select number of economic, fundamental and technical variables as predictors. A natural direction for extending that research would be to look at similar models in different countries or for other asset classes. In

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Direction of Change Forecasting using a Dynamic Binary Model

September 12, 2013
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Direction of Change Forecasting using a Dynamic Binary Model

While it is generally accepted that the returns of financial assets are almost impossible to forecast with any degree of accuracy which would provide meaningful profit1 , there is evidence that the sign of the returns is much more forecastable. Theoretically, Christoffersen and Diebold (2006) have shown how the forecastability of the sign is related

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Latent Variable Mixture Modeling: When Heterogeneity Requires Both Categories and Dimensions

August 26, 2013
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Latent Variable Mixture Modeling:  When Heterogeneity Requires Both Categories and Dimensions

Dichotomies come easily to us, especially when they are caricatures as shown in this cartoon.  These personality types do seem real, and without much difficulty, we can anticipate how they might react in different situations.  For example, if...

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