Pre-analysis commitment for a study of deposit rate sensitivity across U.S. bank size classes over the 2021 to 2024 tightening cycle, using FDIC Call Report data. The plan fixes the estimator, sample, comparison groups, controls, reported statistics, robustness variants, and the threshold for what counts as a finding. The file was written on August 25, 2026, before any data was retrieved. It was deposited here on August 27, 2026, after estimation had been carried out. This deposit therefore establishes the content and the deposit date. It does not independently verify that the file predates the estimation, and no claim to that effect is made. Departures from the plan are recorded in a deviation log accompanying the analysis. The work is funded by the Blockchain Association. The author retains the right to publish the findings regardless of what they show.
Baocheng Zeng, Jinhao Yang, Peilin Han, Kangnan He
Public cryptocurrency archives may appear usable when files exist, although factor research requires observations available and executable at each decision time. We audit public Binance BTCUSDT USD-M perpetual-futures data using event, publication, and availability times and separate proposal from deterministic auditing, evaluation, and holdout access. An initial gapless five-minute requirement for trade, mark, index, and open interest failed: the longest unrepaired intersection was 304.5729166666667 days. A disclosed revision made trade, mark, index, and realized funding the core streams and made open interest optional because its publication time was unverified. The revised mask retained 727 complete UTC days and supported a 436/145/146-day train, validation, and historical-holdout split. On 80 frozen known-rule templates, the auditor detected 40/40 violations and rejected 0/40 legal templates. Across ten null-signal paths, full auditing reduced mean false passes from 0.2910 to 0.0625. Under matched valid-candidate budgets, the audited adaptive agent tied random search and did not establish superiority. In the one-time historical holdout, all evaluated runs had positive IC but negative net Sharpe under primary costs. We therefore report a scoped negative result rather than a profitability or agent-superiority claim.
Abstract Dynamic exposure rules can appear effective simply because they reduce risky participation, not because they time exposure well. This study evaluates Pogi, a recursive FULL/PARTIAL/NONE controller that separates portfolio composition from total risky exposure and uses a non-executed shadow path to observe recovery during defensive states. Using daily cryptocurrency data from 2014–2026, eight chronological test folds, recursive transaction costs, and CRRA certainty-equivalent welfare, Pogi is compared with static scaling, volatility targeting, CPPI, drawdown throttling, moving-average control, fractional Kelly scaling, and an exact ex-post exposure-matched diagnostic. The analysis also tests initialization and memory sensitivity, timing nulls, search capacity, selection-aware inference, and external validation using U.S. industry portfolios and frozen cross-market transfer. The completed evaluation did not establish robust welfare superiority for Pogi or support a broader methodological contribution under the pre-specified evidence criteria. The results instead show why dynamic exposure rules should be judged against exposure-matched benchmarks, model-search controls, recursive-state diagnostics, and genuinely external validation.
Dwi Fitrizal Salim, Farida Titik Kristanti, Hosam Alden Riyadh, Mailinda Tri Wahyuni
Type of the article: Research ArticleAbstractThis study evaluates and compares risk measurement models for ten major cryptocurrencies: Bitcoin, Ethereum, Tether, Ripple, Dogecoin, Cardano, Binance Coin, Polkadot, Solana, and USD Coin. Using daily log-return data from January 2017 to October 2024, the analysis applies Modified Cornish-Fisher Value-at-Risk and standard, exponential, threshold, and Markov-switching generalized autoregressive conditional heteroskedasticity models. The main comparison is conducted at the 99% confidence level, while model reliability is assessed through out-of-sample backtesting using 500 observations and the Kupiec unconditional coverage and Christoffersen conditional coverage tests. The results reveal substantial heterogeneity in cryptocurrency risk. Modified Cornish-Fisher Value-at-Risk produces highly sensitive estimates for assets with extreme skewness and kurtosis, particularly Ripple, Cardano, and Dogecoin. However, no single model performs consistently better across all assets. Bitcoin is the only cryptocurrency for which all tested models pass both backtesting procedures. The Markov-switching specification provides acceptable coverage for Bitcoin, Ripple, and Dogecoin but does not consistently outperform conventional volatility models. Standard and asymmetric volatility models provide stronger support for Cardano, Binance Coin, and Polkadot, whereas Ethereum, Solana, and USD Coin remain difficult to model under the examined specifications. These findings demonstrate that cryptocurrency risk measurement requires asset-specific model selection based on both estimated loss magnitude and formal backtesting evidence.
The increasing demand for trustworthy and privacy-preserving credit reporting systems has exposed the limitations of both centralized and existing blockchain-based solutions, including scalability bottlenecks, weak privacy protection, and insufficient incentive mechanisms. To address these challenges, we propose LightCred, a novel consortium blockchain-based personal credit management framework that integrates lightweight nodes, Merkle proofs, multi-role smart contracts, and privacy-preserving cryptographic techniques. LightCred features a five-layer architecture that efficiently collects, verifies, stores, and serves credit data while ensuring data integrity, confidentiality, and regulatory compliance. Specifically, it (i) employs a low-cost and traceable data reduction mechanism through lightweight nodes and Merkle proofs to minimize storage and improve verifiability; (ii) introduces a multi-role smart contract model that enforces dynamic access control and fair incentive distribution based on participant reputations; and (iii) integrates zero-knowledge proofs and homomorphic encryption to support privacy-preserving credit scoring and querying. Experimental results demonstrate that LightCred achieves superior performance compared to five baseline methods, delivering up to 5% higher throughput, 3–5% lower privacy leakage, and 10–15% reduced storage costs, while maintaining competitive latency and auditability. These findings validate LightCred as a robust, scalable, and privacy-aware credit management solution, offering a viable alternative for modern credit reporting systems.