Background and ideaOften we are looking at a particular sector, and want to get a quick overview of a group of companies relative to one another. I thought I might apply Multidimensional Scaling (MDS) to various financial ratios and see if it...

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Recently, I came across quite a few examples of time series forecasting using R. Here are some examples: Time series cross-validation 4: forecasting the S&P 500 Holt-Winters forecast using ggplot2 Autoplot: Graphical Methods with ggplot2 Large-Scale Parallel Statistical Forecasting Computations in R (2011) by M. Stokely, F. Rohani, E. Tassone Forecasting time series data ARIMA

Photo Credit: Jesús Villaseca Pérez Ever since March 2008 Ciudad Juárez began to register an alarming number of homicides becoming Mexico's most violent city. According to the Mexican vital statistics system Ciudad Juárez (coterminous with the Juárez municipality) went from having just 202 murders in 2007 to 1,616 in 2008, 2,397 in...

As part of my Google Summer of Code, I am also working on another package for R called rvertnet. This package is a wrapper in R for VertNet websites. Vertnet is a vertebrate distributed database network consisting of FishNet2, MaNIS, HerpNET, and ORNIS. Out of that currently Fishnet, HerpNET and ORNIS have their v2 portals serving data. rvertnet has functions now to access

ScraperWiki describes itself as an online tool for gathering, cleaning and analysing data from the web. It is a programming oriented approach, users can implement ETL processes in Python, PHP or Ruby, share these processes among the community (or pay for privacy) and schedule automated runs. The software behind the service is open source, and there is...

In my last post, Factor Attribution to improve performance of the 1-Month Reversal Strategy, I discussed how Factor Attribution can be used to boost performance of the 1-Month Reversal Strategy. Today I want to dig a little dipper and examine this strategy for each sector and also run a sector-neutral back-test. The initial steps to

In Andrew Ng's Machine Learning class, the first section demonstrates gradient descent by using it on a familiar problem, that of fitting a linear function to data.Let's start off, by generating some bogus data with known characteristics. Let's make y just a noisy version of x. Let's also add 3 to give the intercept term something to...