<p>I have found some posts to help answer your question.   In a post on ResearchGate.net,<br />
<a href="https://www.researchgate.net/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data" rel="nofollow noreferrer">https://www.researchgate.net/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data</a></p>
<p>One of the responses that I think has the best answer for your is to carry forward the previous data . However,  as the person stated,  there is a problem with that method.    “<em>--- if you are developing a trading system, for example, and assume that prices are &quot;tradable&quot;, then your analysis will be flawed since you can't trade at a &quot;carried forward price.&quot; My solution to this problem is this: create a 'locf' price and as a separate variable, a &quot;tradable price&quot;, which when data is missing, is (approximately) the next price you could have realized. “</em></p>
<p>However,  Mazin A. M. Al Janabi  posted a link on that page which has a better answer for you.  <a href="https://www.researchgate.net/profile/Mazin-Al-Janabi/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data/attachment/5b73a9633843b0024405fcb1/AS%3A659744100450305%401534306659149/download/Dealing+with+the+Problem+of+Missing+Data-+A+short+Note.pdf" rel="nofollow noreferrer">https://www.researchgate.net/profile/Mazin-Al-Janabi/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data/attachment/5b73a9633843b0024405fcb1/AS%3A659744100450305%401534306659149/download/Dealing+with+the+Problem+of+Missing+Data-+A+short+Note.pdf</a>
He states that you either not include the missing data or you can calculate the price from a geometric mean of the previous and other price data. <strong>However using average prices will underestimate the volatility of the stock price data.</strong></p>
<p>Here is another response to a similar question on Stackexchange: <a href="https://quant.stackexchange.com/questions/41171/how-to-deal-with-missing-value-in-a-time-series-stock-market-data">https://quant.stackexchange.com/questions/41171/how-to-deal-with-missing-value-in-a-time-series-stock-market-data</a></p>
<p>I have also posted a similar answer on missing data but not missing stock price data <a href="https://economics.stackexchange.com/questions/27591/how-do-i-fill-gaps-in-my-data/27594#27594">How do I fill gaps in my data?</a></p>
<p>Either case I think it is VERY important that you document that you have missing data, report which observations are missing, and what method you used to remedy it.</p>

CC BY-SA 4.0
2021-05-03T23:32:18.313Z
Thank you so much for your reply. I will go through all your reading suggestions. Much appreciated.
Sima
CC BY-SA 4.0
2021-05-04T04:14:05.023Z
Awesome.  Good luck with your project.
Mike J
CC BY-SA 4.0
2021-05-03T02:11:11.813Z
2021-05-03T02:11:11.813Z
Mike J
<p>I am trying to test some asset pricing models on 10 portfolios for the period of 2010-2020. The problem is that three of these portfolios included stocks that are newly listed in 2017 and 2018, so I only have three years of historical data for these stocks. Can anyone help how to solve this problem? I don't want to exclude these stocks; does imputation makes sense for such a long period?</p>

CC BY-SA 4.0
2021-05-05T09:43:58.183Z
The same on Cross Validated: [here](https://stats.stackexchange.com/questions/522402).
Richard Hardy
<econometrics><finance><financial-economics><time-series>
How to handle the missing values' issue of newly listed stocks?
Sima
# How to handle the missing values' issue of newly listed stocks?

(Asked by: username_0 on 2021-05-01)

I am trying to test some asset pricing models on 10 portfolios for the period of 2010-2020. The problem is that three of these portfolios included stocks that are newly listed in 2017 and 2018, so I only have three years of historical data for these stocks. Can anyone help how to solve this problem? I don't want to exclude these stocks; does imputation makes sense for such a long period?

**Tags:** econometrics, finance, financial-economics, time-series

> **Comments:**
> **username_1**: The same on Cross Validated: [here](https://stats.stackexchange.com/questions/522402).  

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## 1 answers

### Answer by username_2 on 2021-05-03. Score: 1

I have found some posts to help answer your question. In a post on ResearchGate.net,  
<https://www.researchgate.net/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data>

One of the responses that I think has the best answer for your is to carry forward the previous data . However, as the person stated, there is a problem with that method. “*--- if you are developing a trading system, for example, and assume that prices are "tradable", then your analysis will be flawed since you can't trade at a "carried forward price." My solution to this problem is this: create a 'locf' price and as a separate variable, a "tradable price", which when data is missing, is (approximately) the next price you could have realized. “*

However, Mazin A. M. Al Janabi posted a link on that page which has a better answer for you. <https://www.researchgate.net/profile/Mazin-Al-Janabi/post/How-to-deal-with-missing-value-in-a-time-series-stock-market-data/attachment/5b73a9633843b0024405fcb1/AS%3A659744100450305%401534306659149/download/Dealing+with+the+Problem+of+Missing+Data-+A+short+Note.pdf>
He states that you either not include the missing data or you can calculate the price from a geometric mean of the previous and other price data. **However using average prices will underestimate the volatility of the stock price data.**

Here is another response to a similar question on Stackexchange: <https://quant.stackexchange.com/questions/41171/how-to-deal-with-missing-value-in-a-time-series-stock-market-data>

I have also posted a similar answer on missing data but not missing stock price data [How do I fill gaps in my data?](https://economics.stackexchange.com/questions/27591/how-do-i-fill-gaps-in-my-data/27594#27594)

Either case I think it is VERY important that you document that you have missing data, report which observations are missing, and what method you used to remedy it.

> **Comments:**
> **username_0**: Thank you so much for your reply. I will go through all your reading suggestions. Much appreciated.  
> **username_2**: Awesome.  Good luck with your project.  

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