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A novel adjusted learning algorithm for online portfolio selection using peak price tracking approach

Dai, Hong-Liang; Huang, Cui-Yin; Dai, Hong-Ming; Lai, … - In: Decision analytics journal 7 (2023), pp. 1-10

machine learning. We propose a novel adjusted learning algorithm based on peak price tracking for OLPS to tackle this … challenge. The algorithm is based on an aggressive strategy with residual information and transaction costs. We first propose an … adjusted online portfolio selection algorithm using Peak Price Tracking Approach (PPTA) to improve the accuracy of return …

Persistent link: https://www.econbiz.de/10014505333

Collusion by algorithm: the role of unobserved actions

Martin, Simon; Rasch, Alexander - 2022

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

Persistent link: https://www.econbiz.de/10013093034

Can unbiased predictive AI amplify bias?

Khan, Tanvir Ahmed - 2023

Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...

Persistent link: https://www.econbiz.de/10014452605

Informationally Robust Welfare Predictions Under Second-Degree Price Discrimination

Yang, Kai Hao - 2019

In an environment that features second-degree price discrimination, this paper fully characterizes the set of surplus divisions that can arise from all possible information consumers have about their valuation. By extending the techniques developed in a companion paper (Yang, 2019a), I show that...

Persistent link: https://www.econbiz.de/10012894284