Selection-Neglect in the NFT Bubble

Dong Huang and William Goetzmann

No 31498, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble. We show that the price-volume relationship is consistent with extrapolative beliefs about increasing prices which were plausibly triggered by a macroeconomic shock. We test the hypothesis that agents prone to selection-neglect formed even more optimistic beliefs and traded more aggressively than their counterparts during the boom. When liquidity for NFTs declined, observed NFT prices were subject to severe selection bias due in part to seller loss aversion delaying the onset of the crash. Finally, we show that market participants with sophisticated bidding behavior were less subject to selection bias and performed better.

JEL-codes: G1 G12 G14 G4 G40 G41 (search for similar items in EconPapers)
Date: 2023-07
New Economics Papers: this item is included in nep-fmk, nep-inv and nep-pay
Note: AP
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Citations: View citations in EconPapers (1)

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