Imputing missing values for Stock Market



I have downloaded the data from and there are missing stock values for the holidays.
How to impute the missing values for the stock market daily data in R ? Can someone help me here.


Just remove the observations that dont have values, because in reality there is no data at that instant, we use to do imputation where we have strong ground to assume that data was actually there but due to some error it has not been recorded. In your Stocks problem when there is no data then you are not required to impute.



Thanks for the reply.
Without imputing the data for fewer dates (which are not present) in training data set, will that be accurate when we predicting the stock market value for one week/month.

Please advise


It would be really helpful if you suggest me any reference link/guide for the stock market prediction in R.


I can suggest you a book which is awesome in stock market analysis both financial and forecasting which i think may be of interest to you.

and i will suggest to search for feature extraction if you want to apply machine learning over stocks data.
Please try to explore more since this is a huge topic and you have lots and lots of strategies and methodologies to solve this kind of problem.


Missing values handling comes as part of data cleaning process where most of the time is spent on data analytics process.

Imputing missing data which referred as NA has various approach as listed below

  1. First what percentage of data has missing values , say for example if only 4 % data has missing values then it is safe to delete or ignore it.

  2. If the data portion of missing values is more for example 15 % then we use imputation techniques such as replacing with mean, mode, etc…based on type of variable such categorical or quantitative variable…