Enigma in Economics

The Future of the Firm: A Comparative Institutional Analysis of Transaction Costs in DAOs versus Traditional Corporations

Fri, 20 Jun 2025 00:00:00 +0000

The emergence of Decentralized Autonomous Organizations (DAOs) presents a fundamental challenge to the traditional corporate form, which has dominated economic organization for over a century. Built on blockchain technology, DAOs propose a new model for coordinating economic activity. This study addressed the critical question of institutional efficiency by applying the lens of Transaction Cost Economics (TCE) to compare DAOs and traditional corporations. A comparative institutional analysis was conducted using a mixed-methods approach. We employed a multiple case study design, analyzing two representative DAOs and two analogous traditional corporations from Q1 2023 to Q4 2024. Data collection involved the systematic analysis of archival records, including 215 DAO governance proposals and corporate filings, and 32 semi-structured interviews with key participants. A novel analytical framework was developed to categorize transaction costs into ex ante (search, bargaining) and ex post (monitoring, enforcement), further distinguishing between 'on-chain' and 'off-chain' costs. The study revealed significant trade-offs between the two organizational forms. Traditional corporations exhibited high ex ante bargaining costs (legal, negotiation) and ex post monitoring costs (managerial overhead), but benefited from established legal frameworks that reduced enforcement uncertainty. Conversely, DAOs significantly lowered specific transaction costs through automation via smart contracts, particularly in on-chain bargaining and enforcement for codified tasks. However, DAOs incurred substantial, often hidden, new transaction costs related to off-chain social coordination, governance participation, and navigating legal ambiguity. This was termed the 'Governance Overhead Paradox'. In conclusion, DAOs do not represent a universally superior organizational form but rather a new point on an institutional possibility frontier. They are highly efficient for tasks that are global, permissionless, and computationally verifiable. Traditional firms retain advantages in contexts requiring complex, subjective decision-making and legal certainty. The future of the firm is likely not a replacement of one form by the other, but a pluralistic ecosystem where hybrid models emerge.

Systemic Contagion or Digital Diversifier? A Dynamic Quantification of the Cryptocurrency Market's Evolving Role in Global Financial Risk Transmission

Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin — Fri, 10 Oct 2025 04:20:24 +0000

The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.

Synergistic Alpha: A Deep Learning Framework for Forecasting Cryptocurrency Returns by Fusing On-Chain, Sentiment, and Market Data

Gayatri Putri, Sonia Vernanda, Anies Fatmawati, Muhammad Faiz — Sat, 11 Oct 2025 04:01:08 +0000

The inherent volatility and unique economic characteristics of cryptocurrencies pose significant challenges to conventional asset-pricing models. This study investigates whether a synergistic fusion of the network’s fundamental data (on-chain metrics), market behavioral dynamics (social media sentiment), and historical market data can uncover statistically and economically significant predictive power when analyzed by advanced deep learning architectures. We developed a sophisticated forecasting and backtesting framework to predict the daily log returns of Bitcoin (BTC). The methodology is grounded in rigorous time-series analysis, beginning with Augmented Dickey-Fuller tests to ensure data stationarity. We constructed a multi-modal dataset from specified, high-frequency sources (Kaiko, Glassnode, and a custom-built FinBERT sentiment model) spanning January 1, 2018, to December 31, 2023. We systematically compared the performance of a state-of-the-art Transformer model against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and robust econometric baselines, including GARCH(1,1) and ARIMA. The models were evaluated not only on statistical accuracy (such as Root Mean Squared Error and Directional Accuracy) but also on their economic significance via a realistic trading backtest that incorporates transaction costs. The fully integrated Hybrid Transformer model demonstrated superior forecasting accuracy, achieving the highest Directional Accuracy (61.25%). More importantly, in a transaction-cost-aware backtest, a trading strategy guided by this model yielded an annualized Sharpe Ratio of 1.58, significantly outperforming a buy-and-hold benchmark (Sharpe Ratio: 0.72). The strategy generated a statistically significant Jensen's Alpha of 0.18 (p < 0.01), indicating substantial risk-adjusted excess returns. Feature importance analysis via SHAP confirmed that social media sentiment and the NVT Signal were the most influential predictors beyond past returns. In conclusion, the findings provide strong evidence that the cryptocurrency market exhibits exploitable inefficiencies. The fusion of on-chain, sentiment, and market data, when processed by attention-based neural networks, uncovers a statistically and economically significant predictive edge. This work challenges the semi-strong form of market efficiency for digital assets and suggests that alpha is derivable from the complex, high-dimensional data footprints unique to this asset class, providing a robust framework for quantitative investment strategies.

Plutocracy in the Protocol: A Quantitative Triangulation of Power Concentration in Decentralized Finance Governance

Arya Ganendra, Neva Dian Permana, Muhammad Faiz, Henry Clifford — Sat, 11 Oct 2025 04:55:15 +0000

Decentralized Finance (DeFi) proposes a paradigm shift towards a democratized financial ecosystem governed by its users. This vision of decentralization is predicated on the distribution of governance tokens. However, the verity of this claim lacks rigorous empirical validation, raising concerns about a potential "decentralization illusion." This study quantitatively investigates the concentration of governance power within leading DeFi protocols to empirically test this narrative. We employed a multi-faceted quantitative triangulation framework using on-chain data from three archetypal DeFi protocols, selected to represent the core sectors of the ecosystem: a lending market (ProtoLend), a decentralized exchange (ProtoSwap), and a yield aggregator (ProtoYield). Our methodology integrates: (1) Empirical Network Analysis based on on-chain voting power delegation to map the topology of influence; (2) Economic Inequality Metrics, including the Gini Coefficient and Lorenz Curve Analysis, to quantify the distribution of governance tokens; and (3) Systemic Risk Assessment via the Nakamoto Coefficient to determine the minimum number of colluding actors required for a 51% governance attack. The empirical network analysis revealed a distinct core-periphery topology across all protocols, indicative of highly centralized influence structures. This was substantiated by extreme economic inequality, with Gini coefficients of 0.91 for ProtoLend, 0.95 for ProtoSwap, and 0.89 for ProtoYield. Lorenz curves visually confirmed that a minuscule fraction of holders controls the vast majority of voting power. The Nakamoto coefficients were critically low, calculated at 8 for ProtoLend, 5 for ProtoSwap, and 11 for ProtoYield, exposing profound vulnerabilities to collusion and capture. In conclusion, our findings provide robust, triangulated evidence of a pervasive "decentralization illusion" within DeFi. Governance power is not distributed but is instead highly concentrated, replicating the plutocratic power dynamics of traditional finance. This concentration poses significant systemic risks and fundamentally challenges the core value proposition of the DeFi ecosystem.

Pricing Sustainability in Decentralized Finance: An Empirical Analysis of the ESG Premium in Digital Assets

Anies Fatmawati, Aylin Yermekova, Andi Fatihah Syahrir, Neva Dian Permana — Sun, 12 Oct 2025 07:15:35 +0000

The rapid expansion of digital assets has created a conflict between technological innovation and environmental, social, and governance (ESG) principles, particularly concerning the energy consumption of legacy consensus mechanisms. This has led to the emergence of "sustainable" cryptocurrencies, raising the critical question of whether the market financially rewards sustainability. This study quantitatively investigates the existence and magnitude of an "ESG premium" in the digital asset market. A quasi-longitudinal study was conducted on a panel dataset of 20 cryptocurrencies (10 sustainable, 10 traditional) from January 1, 2021, to December 31, 2024. A detailed, transparent composite ESG score was developed to measure sustainability. The primary analysis utilized a panel data fixed-effects regression model to assess the relationship between asset prices and ESG scores, controlling for market capitalization, trading volume, market-wide indices, and key technological factors like protocol age, scalability, and developer activity. To address endogeneity and validate causality, we employed models with lagged independent variables. Further robustness checks were performed across bull and bear market sub-periods. A GARCH (1,1) model was used to analyze differences in price volatility. The primary regression model reveals a statistically and economically significant positive relationship between ESG scores and cryptocurrency prices. A 10-point increase in the ESG score is associated with a 4.1% price premium (b=0.0041, p < 0.001), even after controlling for technological modernity. This finding remains robust in models using lagged variables and across different market cycles. GARCH analysis confirms that sustainable cryptocurrencies exhibit significantly lower price volatility. In conclusion, the findings provide strong, robust empirical evidence for a persistent ESG premium in the cryptocurrency market. This suggests that investors price in the perceived long-term viability, reduced risk profile, and ethical alignment of sustainable assets, signaling a maturation of the market where non-financial, sustainability-focused metrics are integral to asset valuation.