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    2 replies to "Danger Zone: Value Investors"

    • Maya Nair

      This is a classic case where XBRL data should be used.

      Let me take the example of an actuarial gain of $7.8 billion on At&T’s pension and post-retirement benefit plans. This actuarial gain of $7.8 billion is split as follows (this is based on the information given in Note 12 – Pension and Postretirement Benefits)

      Actuarial Gain ($ in millions)
      Pension Plans $ 5,013
      Postretirement Plans $ 2,738

      I checked the XBRL files for this filing. Both the above values are captured in the XBRL document with the tag DefinedBenefitPlanActuarialGainLoss (ideally the tag should have been DefinedBenefitPlanAmortizationOfGainsLosses). Now, the advantage when data is captured in XBRL is that you can use it for your own analysis. For eg. in our analytics tool, we derive the value of ‘Total Actuarial Gain (Loss)’ from the individual values (given for independent pension/postretirement plans). We can not only derive the value of ‘Total Actuarial Gain (Loss)’, but we can also derive NOPAT from the above values.

      We can also develop applications where an existing financial model used by the analysts can be powered with XBRL data (XBRL data can flow into an existing financial model at the analyst’ behest). This coupled with the capability to derive values can certainly solve the issues faced by analysts (or anyone who consumes data).

    • David Trainer

      Maya Nair:

      Thanks for reading and commenting. I’ve actually written about XBRL in the past, which you can see here:
      https://www.newconstructs.com/2013/11/04/danger-zone-11413-xbrl-extensible-business-reporting-language/

      XBRL has a great deal of potential, but it still has a long way to go before it can become a trusted resource for analysts and investors to use. There are just too many errors with the data right now for it to work. I like XBRL and hope it succeeds, but it will take a concerted effort from the SEC to ensure that the data companies put out is accurate.

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