Update Study of Graded Comic Sale Prices20324

Originally Posted by Rafel
This sounds like your making your own price guide. Or are you trying to make a list (or guide) with two columns. One side CGC and the other side CBCS.

Originally Posted by Davethebrave
For highly traded books if you have the right census data and model, you should rather easily plug gaps. The thing is that you won’t need to as often… because they are regularly traded.

So gets back to purpose. You won’t get much accuracy for thinly traded/rare books and you won’t need an implied model for most heavily traded books.

Originally Posted by EbayMafia
Quote:

Originally Posted by Davethebrave
For highly traded books if you have the right census data and model, you should rather easily plug gaps. The thing is that you won’t need to as often… because they are regularly traded.

So gets back to purpose. You won’t get much accuracy for thinly traded/rare books and you won’t need an implied model for most heavily traded books.

Would it be helpful for prediction purposes if one could gather more information about the reasons behind the thin trade volume? I.E. understanding if it is a supply issue or a lack of demand issue? I know that small numbers become less consistent, but I wonder if one could put together a set of data points (MCS want list numbers and annual sales volume vs. current quantity available on specific platforms, asking prices vs. most recent sales prices, Ebay page views/watchers, etc. that would help to narrow down a more likely range?

Originally Posted by EbayMafia
Quote:

Originally Posted by Davethebrave
For highly traded books if you have the right census data and model, you should rather easily plug gaps. The thing is that you won’t need to as often… because they are regularly traded.

So gets back to purpose. You won’t get much accuracy for thinly traded/rare books and you won’t need an implied model for most heavily traded books.

Would it be helpful for prediction purposes if one could gather more information about the reasons behind the thin trade volume? I.E. understanding if it is a supply issue or a lack of demand issue? I know that small numbers become less consistent, but I wonder if one could put together a set of data points (MCS want list numbers and annual sales volume vs. current quantity available on specific platforms, asking prices vs. most recent sales prices, Ebay page views/watchers, etc. that would help to narrow down a more likely range?

Originally Posted by drloko
Quote:

Originally Posted by Davethebrave
To calc implied values you need to focus on census data. That is critical as grade distributions vary.

For thinly traded books you will need to accept greater variance - it will be more difficult to accurately predict. Reasons are twofold but both have to do with liquidity: 1) seller desperation - a desperate seller will have a greater impact on a price result for a thinly traded (rare) book than a book with high trading volume and 2) buyer desperation - a desperate buyer may pay “off market” prices for a book with limited availability.

For highly traded books if you have the right census data and model, you should rather easily plug gaps. The thing is that you won’t need to as often… because they are regularly traded.

So gets back to purpose. You won’t get much accuracy for thinly traded/rare books and you won’t need an implied model for most heavily traded books.

The accuracy of the model is within the tolerance of the ordinary transaction variance. Although there are exceptions at the extreme volumes, the majority of sales are outside of the drift window of the price.

Originally Posted by Neutrons
Do you have a preview of your study?

Originally Posted by Davethebrave
Quote:

Originally Posted by drloko
Quote:

Originally Posted by Davethebrave
To calc implied values you need to focus on census data. That is critical as grade distributions vary.

For thinly traded books you will need to accept greater variance - it will be more difficult to accurately predict. Reasons are twofold but both have to do with liquidity: 1) seller desperation - a desperate seller will have a greater impact on a price result for a thinly traded (rare) book than a book with high trading volume and 2) buyer desperation - a desperate buyer may pay “off market” prices for a book with limited availability.

For highly traded books if you have the right census data and model, you should rather easily plug gaps. The thing is that you won’t need to as often… because they are regularly traded.

So gets back to purpose. You won’t get much accuracy for thinly traded/rare books and you won’t need an implied model for most heavily traded books.

The accuracy of the model is within the tolerance of the ordinary transaction variance. Although there are exceptions at the extreme volumes, the majority of sales are outside of the drift window of the price.

Goes to the original point - regularly transacted books don’t really need a model to predict (easy to interpolate between grades). Books rarely transacted and/or scarce overall - you won’t get much predictive value out of the model.

Edit: for platform impact on price I’d like to see that analysis… I suspect it will be hard to draw conclusions with much confidence between the major platforms when looking at the same format (auction vs auction, BIN vs BIN, etc).