I hope this is on-topic. I want to set-up a set of investment rules and back-test it on a mix of asset-classes. Thus I thought that using ETFs for the back-test would be a good idea (time series could be of good quality, I can actually invest in them). I am using R with the package quantmod and I find it quite annoying and difficult to search for ETFs and the (google/yahoo finance) symbols to get the price series.

It would be perfect to have something like this:

  • I want e.g. European stocks, EM hard currency, corporate bonds
  • ETF A covers the stocks, B EM HC and C Corporates
  • the symbols are ...

How do you solves such problems?

EDIT: This is a quite beta website that groups ETFs by asset class and provides tickers ... I guess there is something better.

  • $\begingroup$ Hi @Richard ! My 2 cents: Maybe it is more practical to do the following - Get data on the actual tracking error and, most importantly, tracking difference from trackinsight.com (i took a fresh look today it seems you have to register now), which is the most CLEANED data provider on etfs out there. Then you could take the historical index and subtract the tracking difference. You can use the tracking error to model a random factor. If you use historical data (fewer etfs!) you have both a shorter history and a less efficient ETF market than today. $\endgroup$ – vanguard2k Jul 7 '15 at 14:46

There are plenty of sites you can get this information from. etfdb.com and etf.com are two of the bigger ones.

See this for an example: http://etfdb.com/etfdb-category/europe-equities/


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    $\begingroup$ This is pretty much what I was looking for. I just found the google finance page quite unsatisfactory in this regard ... $\endgroup$ – Ric Jul 7 '15 at 12:14

You may want to visit http://www.trackinsight.com for extensive data on ETFs listsed globaly, including cleansed and adjusted performance data for accurate Tracking Error and Tracking Difference calculation

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    $\begingroup$ You should disclose that this is your company. $\endgroup$ – LocalVolatility Jan 4 '18 at 12:38

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