Jing huang finance install#

A small risk would increase the threshold computation power to attack by 36%. We show that, the gain from selfish mining relies on a quick difficulty adjustment, and the miner is most concerned with maintaining the new difficulty in long run. We present a continuous-time model that incorporates discounted cash flows, a new risk of being temporarily orphaned and endogenous difficulty adjustment. When the miner is impatient, selfish mining is a long-term deviation, and the short-term frictions incentivize honest mining. This paper reconciles the discrepancy between the absence of block-withholding attacks in practice and the profits as suggested by the literature. Presentations : Workshop on Financial Intermediation and Regulation at Queens University 2022, WFA 2022, CICF 2022, Indiana Kell ey, T oronto Junior Finance/Macro Conference, Berkeley Haas, FTG Spring 2023 (short presentation), FDIC, Korea University, FIRS 2023, Bank of Canada workshop on Payments and Securities Settlement, EFA 2023, INFORMS 2023ĭiscounted Selfish Mining: Is It Profitable? 10 /2021Ī rational miner may gain from withholding blocks and strategically timing the release to orphan the others' blocks, which questions the existence of honest majority and blockchain consensus. Big data technology enables the fintech to leverage data from its early-stage operations in unbanked markets to develop predictive models for expansion into wealthy markets. As the fintech lending technology improves, better enforcement harms, while better information technology benefits traditional banking sector profits. Despite flexible information acquisition technology, the endogenous fintech learning is surprisingly coarse-only sets a single threshold to screen out low-quality borrowers. I study credit market outcomes with different competing lending technologies: A fintech lender that learns from data and is able to seize on-platform sales, and a banking sector that relies on physical collateral. Presentations : Yale Junior Finance Conference, Tsinghua PBCĢ022 Western Finance Association PBCSF Award for the Best Paper in FinTech Our model also sheds lights on the recent growth in fintechs who often have advantage in processing structured hard data but not in specialized soft information.

We hence are able to explain the empirical regularity documented by Blickle, Parlatore, and Saunders (2021) that loans by specialized lenders are with lower rates and better ex-post performance.

In the equilibrium with lender specialization, both lenders use hard signals to screen loan applications, and the specialized lender prices the loan based on its additional soft signal conditional on making a loan.

Jing huang finance install#

Ex ante symmetric banks can install data processing systems which allow them to process general 'hard'' information for all industries, and further acquire a specialized 'soft'' signal in any industry. Multi-Dimensional Information with Specialized LendersĪbstract: We study endogenous lender specialization in credit market competition with information acquisition.