Insider Trading 2.0? The Ethics of Information Sales

Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing

“…Algorithmic pricing is based on different forms of computerassisted real-time pricing mechanisms (Angel and McCabe 2018;Calvano et al 2019;Chen et al 2016b;Faruqui and Sergici 2013). In this study, we focus on the two major types of algorithmic pricing, that is, dynamic and personalized pricing.…”

“…In this study, we focus on the two major types of algorithmic pricing, that is, dynamic and personalized pricing. The two pricing types are characterized and interpreted differently within scientific communities, as they stand at the frontier of and overlap with several research fields, predominantly Marketing, Operations Research/Management Science, Economics, Law and Computer science and not least Business and Marketing Ethics (Angel and McCabe 2018;Bar-Gill 2019;Cosguner et al 2018;den Boer 2015;Elmaghraby and Keskinocak 2003;Gal 2019; 1 "For a definition of care ethics, see https ://www.iep.utm.edu/careeth/" and additionally Gössling and van Liedekerke (2014); in recent years, care ethics has developed beyond its context of origin in feminist ethics so as to encompass the specificities brought on by starkly asymmetrical situations (helping/helpless person; adult/child; healthy/ incapacitated subject, etc. ); a good intro into and survey of the field is given by: Held, Virginia.…”

“…As a result, unregulated prices are central to two conventional legitimations of market economies: They allow-or so the story goes-for individual economic freedom while at the same time assuring that this freedom, coupled with rational maximization behavior, leads to the overall most efficient use of societal resources. Given this pivotal economic role, any substantial change in the way how prices are set is bound to be of significance from a business and marketing ethics perspective; especially when decisions on pricing are automated via algorithms and do not require human input anymore (Angel and McCabe 2018;Miklós-Thal and Tucker 2019).…”

Tiered Information Disclosure: An Empirical Analysis of the Advance Peek into the Michigan Index of Consumer Sentiment

“…These negative externalities can result in overinvestment in equilibrium (Biais, Foucault and Moinas, ). While the capital markets rely on investors obtaining data for prices to better reflect the fundamental values of assets, the resources expended to provide and process faster access to data are in effect not creating new data but instead simply accessing it more quickly (Angel and McCabe, ). If permitted, such practice may encourage data providers to sell tiered information to traders at a premium cost leading to misallocation of resources.…”

The sensitivity of trading to the cost of information

“…Wu, Liu, Suardi, and Chang (2015) and Angel and McCabe (2018) analyze the issue of the advance peek sale of Consumer Sentiment Data by the University of Michigan. Wu et al (2015) find that liquidity demanding high frequency traders (HFT) with privileged access to data devised a trading strategy to avoid revealing their information to the market and were able to generate significantly higher profits than traders without early access.…”