arXiv subjects

Kensuke Ito

Publications and source records attributed to Kensuke Ito.

5 recordsLinked to original sources

arXiv2026

Redefining Stablecoins from Nominal to Real Value: A Maximum Likelihood Approach

Stablecoins, typically pegged to fiat currencies, cannot achieve true stability because they inherit fluctuations in the underlying unit of account. To overcome this limitation, we introduce a stablecoin pegged to the Maximum Likelihood Value (MLV), a newly defined unit of account derived as the most probable configuration of latent real-value movements that explains observed nominal-value (price) changes. Grounded in inferential statistics and modern portfolio theory, MLV represents the most stable unit of account, as it enforces a zero real return on the minimum-variance portfolio. Empirical results confirm the operational viability of an MLV-pegged stablecoin: MLV can be computed in real time from 500 asset price series and improves annualized returns and Sharpe ratios while substantially reducing turnover in portfolio optimization.

cs.CE↗

arXiv2024

Cryptoeconomics and Tokenomics as Economics: A Survey with Opinions

This paper surveys products and studies on cryptoeconomics and tokenomics from an economic perspective, as these terms are still (i) ill-defined and (ii) disconnected from economic disciplines. We first suggest that they can be novel when integrated; we then conduct a literature review and case study following consensus-building for decentralization and token value for autonomy. Integration requires simultaneous consideration of strategic behavior, spamming, Sybil attacks, free-riding, marginal cost, marginal utility and stabilizers. This survey is the first systematization of knowledge on cryptoeconomics and tokenomics, aiming to bridge the contexts of economics and blockchain.

cs.GT↗

arXiv2022

Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation

Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.

q-fin.ST↗

arXiv2020

What is Stablecoin?: A Survey on Its Mechanism and Potential as Decentralized Payment Systems

Our study provides a survey on how existing stablecoins-- cryptocurrencies aiming at price stabilization-- peg their value to other assets, from the perspective of Decentralized Payment Systems (DPSs). This attempt is important because there has been no preceding surveys focusing on the stablecoin as DPSs, i.e., the one aiming at not only price stabilization but also decentralization. Specifically, we first classified existing stablecoins into four types according to their collaterals (fiat, commodity, crypto, and non-collateralized) and pointed out the high potential of non-collateralized stablecoins as DPSs; then, we further classified existing non-collateralized stablecoins into two types according to their intervention layers (protocol, application) and confirmed details of their representative mechanisms. Utilizing concepts such as Quantity Theory of Money (QTM), Tobin tax, and speculative attack, our survey revealed the status quo where, despite the high potential of non-collateralized stablecoins, they have no standard mechanism to achieve the stablecoin for practical DPSs.

cs.CR↗

arXiv2019

Token-Curated Registry with Citation Graph

In this study, we aim to incorporate the expertise of anonymous curators into a token-curated registry (TCR), a decentralized recommender system for collecting a list of high-quality content. This registry is important, because previous studies on TCRs have not specifically focused on technical content, such as academic papers and patents, whose effective curation requires expertise in relevant fields. To measure expertise, curation in our model focuses on both the content and its citation relationships, for which curator assignment uses the Personalized PageRank (PPR) algorithm while reward computation uses a multi-task peer-prediction mechanism. Our proposed CitedTCR bridges the literature on network-based and token-based recommender systems and contributes to the autonomous development of an evolving citation graph for high-quality content. Moreover, we experimentally confirm the incentive for registration and curation in CitedTCR using the simplification of a one-to-one correspondence between users and content (nodes).

cs.DL↗