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Biased Auctioneers
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| PSB lt.dasar - Pascasarjana (Koleksi Majalah) | 1 |
| Penerbit | USA The American Finance Association., 2023 |
|---|---|
| Edisi | Volume 78, Issue 2, April 2023, Pages 795-833 |
| Subjek | Market value Price Predictions |
| ISBN/ISSN | 1540-6261 |
| Klasifikasi | NONE |
| Deskripsi Fisik | First Published: 18 January 2023 |
| Info Detail Spesifik | The Journal of Finance |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas |
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