Analysis of Decentralization in Governance and Financial Efficiency of Companies: Studying the Relationship in the Field of Decentralized Finance

Kirill Kolmykov

Home/Archive/arXiv

arXiv · 2311.02434

Analysis of Decentralization in Governance and Financial Efficiency of Companies: Studying the Relationship in the Field of Decentralized Finance

Kirill Kolmykov

View original source ↗Read source document ↗Export metadata ↓

Abstract

Currently, the advantages of decentralization through blockchain technology in the financial sector are actively discussed. In this article, we investigate the decentralization in the governance of Decentralized Autonomous Organizations (DAO) using the Gini coefficient as an indicator of inequality among the token owners. This metric is analyzed in the context of Return on Investment (ROI) for companies in the decentralized finance (DeFi) sector. Our goal is to understand whether the level of "real" decentralization in blockchain-based governance affects financial efficiency, and to explore the benefits and possible limitations of such an approach. This analysis allows for a deeper understanding of the significance and impact of decentralization on the functioning and productivity of organizations in the DeFi sector, and to determine the extent to which this impact is positively or negatively reflected in their success and profitability. Additionally, the results of this analysis will provide a fuller understanding of the dynamics and potential of blockchain for organization governance.

Explore related subjects

q-fin.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Kirill Kolmykov. 2023-11-04. Analysis of Decentralization in Governance and Financial Efficiency of Companies: Studying the Relationship in the Field of Decentralized Finance. https://arxiv.org/abs/2311.02434

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

KEEP EXPLORING

Related papers

arXiv2026

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↗

arXiv2026

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↗

arXiv2026

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↗