Search arXivSearch

arXiv · 2511.00365

A parallel monetary system based on the redeemable self-decaying money -- The ultimate hedge and safe haven of private wealth in the rising wave of over issuance of fiat and token money/stablecoin

Abstract

A currency with stable purchasing power can always provide a psychological haven for people around the world. However, since the collapse of the Bretton Woods system, issuing more cheap currencies has become a common trend in the international community, and the legalization and over issuance of stablecoins will strengthen this trend. In this context, our study focused on a parallel monetary system based on a redeemable self-decay/devalued money(RSDM). Firstly, we point out the idea of redeeming gold at a fixed denomination with gold certificates is similar to an impossible perpetual motion machine. Only when the face value of a gold token self-decays or self-depreciates and the weight of the reduced value can compensate for the storage cost of physical gold, can it be convertible or redeemable. Secondly, we pointed out that as a modern "good money" under the Internet environment, it must have two basic functions: long-term value storage and zero logistics cost of money circulation. Thirdly, we found that a single type of money is difficult to shoulder the responsibility of modern "good money". Only a parallel monetary system, including RSDM, such as a triple-monetary system consisting of RSDM, domestic fiat and major international reserve currencies, can form the ultimate safe haven of wealth and safeguard the reverse Gresham law. Based on this analysis, we build an integer programming model for currency optimization selection in a multi-monetary pool. Fourthly, several potential application scenarios of RSDM in the real world were discussed, including a new approach to activate dormant gold assets in India based on RSDM, and the gold monetization scheme in the United States. Finally, the demand for RSDM with precious metals as collateral was analyzed, providing theoretical support for establishing a sound parallel monetary system based on RSDM.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Boliang Lin, Ruixi Lin. 2025-11-01. A parallel monetary system based on the redeemable self-decaying money -- The ultimate hedge and safe haven of private wealth in the rising wave of over issuance of fiat and token money/stablecoin. https://arxiv.org/abs/2511.00365

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Prediction Markets Beat the Weather Forecast on Tomorrow's High Temperature

The sooner we receive information, and the more accurate it is, the better planning decisions we can make. Every day, prediction markets let anyone bet on tomorrow's high temperature in cities around the world, creating a market-implied forecast built on dispersed information. We use the past five years of market data from the Kalshi exchange for seven American cities to extract, hour by hour, the market-implied forecast. We use this forecast as a measuring instrument to see how much information about the temperature the market makes public before the public forecasting system does. We race it against the leading American and European weather forecasts. In six of the seven cities we study, the market beats the most accurate single public forecast, the National Blend of Models (NBM). Aggregating every city-day, at the end of the market's first hour of trading it beats the best single public product by about 10 percent in root-mean-square error, and holds its lead through the day, overnight, and into the target day. Looking at how the forecasts move over time, we find the National Blend travels four times further toward the market between its postings than the market travels toward the NBM. The market does not react to new weather forecast updates; instead, the forecast slowly publishes information that the market had already shared publicly.

q-fin.GN

Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem

Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.

q-fin.GN

Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted trees, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 2.08 for classification and 1.39 for regression. This outperformance strengthens with class granularity and persists across subsamples and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to more precise separation of return deciles.

q-fin.GN