similar to: [R-SIG-Finance] forecasting earnings, sales and gross margin of a company...

Hi all, I am playing with some companies' balance sheets and income statements and want to apply what I've just learned from Stats class to see if I can forecast the companies earnings, sales and gross margin in the short term (3rd and 4th Quarter), mid-term (2009) and long term (2011, etc. ) I pulled up some data from companies' financial statements over the past a few years. The

Hi R, Can we download Reuters (3000 Xtra) data from R? Does ODBC package help me in this? Or otherwise, is there a way to extract daily closing prices data of Reuters from R? Thank you very much, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:13}}

Hi all Does anyone know if it is possible when plotting a line or scatter plot, to selectively color the data points based on the data value? i.e. if plotting say the percentage change in stock price movements, to color +ve points in green and -ve points in red? And extending this to a user-defined range of colors based on the quartile of the data points? Thanks Rory Rory Winston RBS Global

Hi all If I have a string, say "ABCDA", and I want to convert this to the sum of the letter values, e.g. A -> 1 B -> 2 etc, so "ABCDA" = 1+2+3+4+1 = 11 Is there an elegant way to do this? Trying something like which(LETTERS %in% unlist(strsplit("ABCDA", ""))) is not quite correct, as it does not count repeated characters. I guess what I need is

Hi all (I'm sure this question has been asked before, but I cant find it). If I have two character vectors: > x <- c("aaa","bbb","ccc") > y <- c("1","2","3") How can I get the cartesian product of the string values? > expand.grid(x,y) Gives me a data frame with separate columns...however, I cant seem to get *apply

Hi all Here is a rather naive implementation of the SEPA algorithm for generating lexical permutations: lexperm3 <- function(x, n=length(x)) { perms <- list() k <- 1 perms[[k]] <- x k <- k + 1 for (y in 1:(factorial(n)-1)) { i <- n-1 while (x[i] > x[i+1] && i > 0) { i <- i - 1 } # i is largest index st x[i] > x[i+1] j <- n #

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

Hi I have a data frame which looks like the following (it is a table of commit logs to a source repo). The Author column is treated as a factor, by virtue of stringsAsFactors being set to TRUE, so I can extract the number of commits per author by using table(logs$Author). Is there an elegant way to extract all subsets of this frame by the Author column - i.e. that returns say a list of data

Hi all This may be an issue that has been discussed at length before, so apologies if I have missed that discussion, but is there any desire to switch from Jitterbug to something more capable and modern? Say JIRA, for instance? Rory Rory Winston RBS Global Banking & Markets Office: +44 20 7085 4476 *********************************************************************************** The

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-

Hi I have a question (which may be an obvious one). It is about an idiom which I have seen quite often: o <- order(x); <- x[o] vs. the alternative x <- sort(x) I am just wondering as to the rationale behind the order/reindex idiom vs sorting. Especially as there seems to be a marked performance difference (especially for integer vectors): > x <- trunc(runif(1E6, 1, 100)) >

On 30 December 2011 10:21, Michael <comtech.usa at gmail.com> wrote: > But are there reasonably good and realistic methods of identifying > outliers/errornous quotes ?in tick data in R? Check out the OutlierD package at http://www.bioconductor.org/packages/release/bioc/html/OutlierD.html. -- Sent from my mobile device Envoyait de mon portable

Hi all, My application usually works on huge amounts of data in the size of MB's .So the performance of "R" in this case will increase if Huge pages are used instead of malloc'ing (as in memory.c) . So Are there any methods to support Huge Pages in "R" ? Can any one give some inputs to help me implementing this feature? [[alternative HTML version deleted]]

Hi all, My application usually works on huge amounts of data in the size of MB's .So the performance of "R" in this case will increase if Huge pages are used instead of malloc'ing (as in memory.c) . So Are there any methods to support Huge Pages in "R" ? Can any one give some inputs to help me implementing this feature? [[alternative HTML version deleted]]

I am looking at histogram breaks, and notice something odd: > foo <- hist(runif(10000),breaks=20) > length(foo$breaks) [1] 21 This makes sense to me. > foo <- hist(runif(10000),breaks=200) > length(foo$breaks) [1] 201 This also makes sense. BUT > foo <- hist(runif(10000),breaks=250) > length(foo$breaks) [1] 201 > foo <- hist(runif(100000),breaks=250) >

Dear All, I have a correlation matrix of size 100 x 100 and would like to extract the diagonal matrix from it. I have used the for loop to store tha correlation values of the diagonal matrix. Is there a 'R way' of doing this? Thanks in advance. Kind regards, Ezhil

Hi. I am trying to follow this forecasting tutorial at: https://www.r-bloggers.com/basic-forecasting/ Using my own data, I cannot get past the first step, lots of laughs. dat3 <- structure(c(5973156.76, 5159011.20, 6695766.64, 6365359.00, 6495218.53, 7226302.39, 6835272.70, 7383501.57, 6962748.19, 7623278.72, 7274994.33 ,7919421.80, 7360740.81, 7436693.35,

Instead of Tsp = c(2016, 2018, 12) try Tsp = c(2016, 2018.25, 12) Hence, you can specify the object as structure(c(5973156.76, 5159011.2, 6695766.64, 6365359, 6495218.53, 7226302.39, 6835272.7, 7383501.57, 6962748.19, 7623278.72, 7274994.33, 7919421.8, 7360740.81, 7436693.35, 8545765.55, 7337269.76, 8180585.44, 8376635.05, 7758261.24, 10374641.22, 8000314.11, 9114958.9, 9805149.15,

Hello, does anyone know of any R routines capable of whats called Hierarchical Forecasting, reconciling the different hierarchies. Example: A top down forecast where the corporate forecast is created and then all the regions within the corporate entity are also forecasted, with the constraint they sum to the corporate forecast.

Hello everybody! I have a problem when I try to perform a forecast of an ARIMA model produced by an auto.arima function. Here is what I'm doing: c<-auto.arima(fil[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE,trace=TRUE) # fil[[1]] is time series of monthly data ARIMA(0,0,0)(0,1,0)[12] with drift : 1725.272 ARIMA(0,0,0)(0,1,0)[12] with drift