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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.19956 |
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| _version_ | 1866918461055172608 |
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| author | Aldridge, Irene Annaeva, Gavhar Beriker, Leyla Cai, Zhiheng Choudhary, Samyak Godoy, Camila Gong, Kaicheng Huang, Zitao Ji, Jonah Kharvasiya, Hetvi Li, Heng Li, Yuxuan Ma, Tianchi Meng, Qingcheng Shi, Ruiyang Shrivastava, Ananya Wang, Jiaqi Wang, Yifan Wu, Zihua Xu, Jiayang Yan, Yuheng Zeng, Zijun Zhang, Bowen Zhang, Francesco |
| author_facet | Aldridge, Irene Annaeva, Gavhar Beriker, Leyla Cai, Zhiheng Choudhary, Samyak Godoy, Camila Gong, Kaicheng Huang, Zitao Ji, Jonah Kharvasiya, Hetvi Li, Heng Li, Yuxuan Ma, Tianchi Meng, Qingcheng Shi, Ruiyang Shrivastava, Ananya Wang, Jiaqi Wang, Yifan Wu, Zihua Xu, Jiayang Yan, Yuheng Zeng, Zijun Zhang, Bowen Zhang, Francesco |
| contents | Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations management literature. We study on-chain peak shaving, the systematic scheduling of Ethereum transactions toward low-congestion windows to reduce gas fee exposure. We use transaction-level data from seven firms across seven industries (N = 62,142 transactions, January-March 2026).
Gas fees vary significantly throughout the day: the peak-hour premium at 10 AM Eastern Time reaches USD 0.220 per transaction above the overnight baseline, driven primarily by speculative-arbitrage demand rather than operational activity. Firm-level scheduling responses are heterogeneous and not uniformly disciplined. Only three of seven firms transact disproportionately during off-peak hours; four transact counter-cyclically, concentrated in peak windows due to external deadlines or governance cycles. This heterogeneity is explained by two moderators: transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix that maps firms to four regimes: 1) full peak shaving, 2) selective peak shaving, 3) cost provisioning, and 4) accept-market-rate, with regime membership predicting both fee savings and residual cost floors (40-92 percent of actual expenditure).
Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs imposed by congestion externalities. In addition to extending Williamson's original cost taxonomy, we introduce a dual classification of gas fees as execution costs in timing but maladaptation costs in origin. The findings reposition on-chain gas-fee management alongside energy procurement and foreign exchange hedging as a domain requiring systematic operational planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19956 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On-chain Peak Shaving Aldridge, Irene Annaeva, Gavhar Beriker, Leyla Cai, Zhiheng Choudhary, Samyak Godoy, Camila Gong, Kaicheng Huang, Zitao Ji, Jonah Kharvasiya, Hetvi Li, Heng Li, Yuxuan Ma, Tianchi Meng, Qingcheng Shi, Ruiyang Shrivastava, Ananya Wang, Jiaqi Wang, Yifan Wu, Zihua Xu, Jiayang Yan, Yuheng Zeng, Zijun Zhang, Bowen Zhang, Francesco Econometrics Trading and Market Microstructure E.3 Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations management literature. We study on-chain peak shaving, the systematic scheduling of Ethereum transactions toward low-congestion windows to reduce gas fee exposure. We use transaction-level data from seven firms across seven industries (N = 62,142 transactions, January-March 2026). Gas fees vary significantly throughout the day: the peak-hour premium at 10 AM Eastern Time reaches USD 0.220 per transaction above the overnight baseline, driven primarily by speculative-arbitrage demand rather than operational activity. Firm-level scheduling responses are heterogeneous and not uniformly disciplined. Only three of seven firms transact disproportionately during off-peak hours; four transact counter-cyclically, concentrated in peak windows due to external deadlines or governance cycles. This heterogeneity is explained by two moderators: transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix that maps firms to four regimes: 1) full peak shaving, 2) selective peak shaving, 3) cost provisioning, and 4) accept-market-rate, with regime membership predicting both fee savings and residual cost floors (40-92 percent of actual expenditure). Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs imposed by congestion externalities. In addition to extending Williamson's original cost taxonomy, we introduce a dual classification of gas fees as execution costs in timing but maladaptation costs in origin. The findings reposition on-chain gas-fee management alongside energy procurement and foreign exchange hedging as a domain requiring systematic operational planning. |
| title | On-chain Peak Shaving |
| topic | Econometrics Trading and Market Microstructure E.3 |
| url | https://arxiv.org/abs/2604.19956 |