arXiv subjects

Peyman Khezr

Publications and source records attributed to Peyman Khezr.

5 recordsLinked to original sources

arXiv2024

Uniform price auctions with pre-announced revenue targets: Evidence from China's SEOs

This study explores the performance of auctions in China's seasoned equity offering (SEO) market, both theoretically and empirically. In these auctions, issuers must commit to a pre-announced revenue target and a maximum number of shares available for auction. We use a common value framework to analyze this auction mechanism, detailing its operation, share allocation, and pricing. The theoretical findings suggest that when buyers bid truthfully, the seller's optimal strategy is to set the total share quantity equal to the target revenue divided by the reserve price. We demonstrate that committing to a target revenue results in a higher level of truthful bidding compared to a standard uniform-price auction without any revenue commitment. We empirically test our theoretical findings using data from China's SEO markets. First, we assess the impact of various issuer strategies on firm-level SEO discounts, categorizing scenarios based on share availability and target revenue. We find that the scenario where the reserve price times the share quantity matches the target revenue is the most optimal for sellers. Second, we examine bidding behavior and auction performance, showing that China's SEO uniform price auction performs exceptionally well. Specifically, the actual issue prices are only 0.029 below the truthful case prices, indicating that the revenue raised is still close to what would have been achieved with truthful bids.

econ.GN↗

arXiv2024

Artificial Intelligence for Multi-Unit Auction design

Understanding bidding behavior in multi-unit auctions remains an ongoing challenge for researchers. Despite their widespread use, theoretical insights into the bidding behavior, revenue ranking, and efficiency of commonly used multi-unit auctions are limited. This paper utilizes artificial intelligence, specifically reinforcement learning, as a model free learning approach to simulate bidding in three prominent multi-unit auctions employed in practice. We introduce six algorithms that are suitable for learning and bidding in multi-unit auctions and compare them using an illustrative example. This paper underscores the significance of using artificial intelligence in auction design, particularly in enhancing the design of multi-unit auctions.

cs.GT↗

arXiv2024

Carbon Pricing and Resale in Emission Trading Systems

Secondary markets and resale are integral components of all emission trading systems. Despite the justification for these secondary trades, such as unpredictable demand, they may encourage speculation and result in the misallocation of permits. In this paper, our aim is to underscore the importance of efficiency in the initial allocation mechanism and to explore how concerns leading to the establishment of secondary markets, such as uncertain demand, can be addressed through alternative means, such as frequent auctions. We demonstrate that the existence of a secondary market could lead to higher untruthful bids in the auction, further encouraging speculation and the accumulation of rent. Our results suggest that an inefficient initial allocation could enable speculators with no use value for the permits to bid in the auction and subsequently earn rents in secondary markets by trading these permits. Even if the secondary market operates efficiently, the resulting rent, which represents a potential loss of auction revenue, cannot be overlooked.

econ.TH↗

arXiv2024

Strategic Bidding in Knapsack Auctions

This paper examines knapsack auctions as a method to solve the knapsack problem with incomplete information, where object values are private and sizes are public. We analyze three auction types-uniform price (UP), discriminatory price (DP), and generalized second price (GSP)-to determine efficient resource allocation in these settings. Using a Greedy algorithm for allocating objects, we analyze bidding behavior, revenue and efficiency of these three auctions using theory, lab experiments, and AI-enriched simulations. Our results suggest that the uniform-price auction has the highest level of truthful bidding and efficiency while the discriminatory price and the generalized second-price auctions are superior in terms of revenue generation. This study not only deepens the understanding of auction-based approaches to NP-hard problems but also provides practical insights for market design.

cs.GT↗

arXiv2024

Blockchains, MEV and the knapsack problem: a primer

In this paper, we take a close look at a problem labeled maximal extractable value (MEV), which arises in a blockchain due to the ability of a block producer to manipulate the order of transactions within a block. Indeed, blockchains such as Ethereum have spent considerable resources addressing this issue and have redesigned the block production process to account for MEV. This paper provides an overview of the MEV problem and tracks how Ethereum has adapted to its presence. A vital aspect of the block building exercise is that it is a variant of the knapsack problem. Consequently, this paper highlights the role of designing auctions to fill a knapsack--or knapsack auctions--in alleviating the MEV problem. Overall, this paper presents a survey of the main issues and an accessible primer for researchers and students wishing to explore the economics of block building and MEV further.

econ.GN↗