Hypothetical valuations and preference reversals in the context of asset trading

1987 ◽  
pp. 131-154 ◽  
Author(s):  
Marc Knez ◽  
Vernon L. Smith
Keyword(s):  
Preference Reversals ◽  
Asset Trading

scholarly journals Trust me; I know what I am doing investigating the effect of choice list elicitation and domain-relevant training on preference reversals in decision making for others

2021 ◽  
Author(s):  
Sebastian Neumann-Böhme ◽  
Stefan A. Lipman ◽  
Werner B. F. Brouwer ◽  
Arthur E. Attema
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Medical Students ◽  
Practical Problem ◽  
Applied Research ◽  
Significant Problem ◽  
Preference Reversals ◽  
Health Domain ◽  
Economics Students ◽  
Financial Domain

AbstractOne core assumption of standard economic theory is that an individual’s preferences are stable, irrespective of the method used to elicit them. This assumption may be violated if preference reversals are observed when comparing different methods to elicit people’s preferences. People may then prefer A over B using one method while preferring B over A using another. Such preference reversals pose a significant problem for theoretical and applied research. We used a sample of medical and economics students to investigate preference reversals in the health and financial domain when choosing patients/clients. We explored whether preference reversals are associated with domain-relevant training and tested whether using guided ‘choice list’ elicitation reduces reversals. Our findings suggest that preference reversals were more likely to occur for medical students, within the health domain, and for open-ended valuation questions. Familiarity with a domain reduced the likelihood of preference reversals in that domain. Although preference reversals occur less frequently within specialist domains, they remain a significant theoretical and practical problem. The use of clearer valuation procedures offers a promising approach to reduce preference reversals.

Preference reversals and effects of D-amphetamine on delay discounting in rats

2012 ◽  
Vol 23 (3) ◽  
pp. 228-240 ◽  
Author(s):  
Christopher A. Krebs ◽  
Karen G. Anderson
Keyword(s):  
Delay Discounting ◽  
Preference Reversals

scholarly journals Strategic Diversification for Asynchronous Asset Trading: Insights from Generalized Coherence Analysis of Cryptocurrency Price Movements

Ledger ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Nirvik Sinha ◽  
Yuan Yang
Keyword(s):  
Complex Dynamics ◽  
The Novel ◽  
Novel Approach ◽  
Small Market ◽  
Asset Trading ◽  
Non Linear ◽  
Price Trends ◽  
Frequency Coupling ◽  
Price Movements ◽  
Reduced Risk

Non-linear interactions between cryptocurrency price movements can elicit cross-frequency coupling (CFC) wherein one set of frequencies in the 1st timeseries is coupled to another set of frequencies in the 2nd timeseries. To investigate this, we use a generalized coherence approach to detect and quantify both linear (i.e., iso-frequency coupling, IFC) and non-linear coherence (CFC) and the associated phase relationships between the intra-day price changes of various pairs of cryptocurrencies for the year 2020. Using this information, we further assess the risk reduction associated with diversification of portfolios between each pair of a small market capital and a large market capital cryptocurrency, for both synchronous and asynchronous trading conditions. While mean pairwise IFC values were lower for smaller cryptocurrencies, pairwise CFC values were more heterogeneous and had no correlation with the market capital size. Diversification of portfolios resulted in reduced risk for synchronously-traded pairs of those cryptocurrencies which had low IFC. For asynchronous trading conditions, if the larger market capital cryptocurrency was traded at a higher frequency, diversification almost always reduced risk. Thus, the novel approach used in this study reveals important insights into the complex dynamics that govern the price trends of cryptocurrencies.

scholarly journals Optimal Multi-Asset Trading With Linear Costs: A Mean-Field Approach

2019 ◽  
Author(s):  
Matt Emschwiller ◽  
Benjamin Petit ◽  
Jean-Philippe Bouchaud
Keyword(s):  
Mean Field ◽  
Field Approach ◽  
Asset Trading

scholarly journals The fragile nature of contextual preference reversals: Reply to Tsetsos, Chater, and Usher (2015).

2015 ◽  
Vol 122 (4) ◽  
pp. 848-853 ◽  
Author(s):  
Jennifer S. Trueblood ◽  
Scott D. Brown ◽  
Andrew Heathcote
Keyword(s):  
Preference Reversals

Trade or Trick?

2021 ◽  
Vol 5 (3) ◽  
pp. 1-26
Author(s):  
Pengcheng Xia ◽  
Haoyu Wang ◽  
Bingyu Gao ◽  
Weihang Su ◽  
Zhou Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Security And Privacy ◽  
Smart Contracts ◽  
Early Stages ◽  
Asset Trading ◽  
Digital Asset ◽  
Guilt By Association ◽  
Privacy Issues ◽  
Digital Assets

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.

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