Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.23283

Market Efficiency in a Changing Asset Population: Mobility, Entry, and Survival over Cryptocurrency's Full History

Antonis Ballis·Christos Ioannidis
View original source ↗Read source document ↗Export metadata ↓

Abstract

Weak-form efficiency implies symmetry restrictions on the Markov chain governing movement across the cryptocurrency capitalisation distribution. We test them on the full listed population: 8,970 assets, 690 weekly cross-sections, 2013 to 2026, with entry, exit and survival observed rather than inferred. Symmetry is rejected in every era, but the asymmetry falls by seventy percent, and what converged is entry-driven compositional churn rather than coins' own valuations. The bottom decile earns 27.5% per week on paper and nothing among assets that trade, on capacity of $293,000. Compensation for neglect exists precisely where trading does not.

Explore related subjects

q-fin.STecon.GNq-fin.ECq-fin.PR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Antonis Ballis, Christos Ioannidis. 2026-10-16. Market Efficiency in a Changing Asset Population: Mobility, Entry, and Survival over Cryptocurrency's Full History. https://arxiv.org/abs/2610.23283

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

KEEP EXPLORING

Related papers

arXiv2026

Real-time identification of the onset of financial rogue waves

Extreme events in financial systems, often captured by indicators such as volatility, remain difficult to identify close to their onset. Volatility shares many statistical properties with other natural, complex systems which experience extreme events, which we explore in this manuscript. We extend the analogy between rogue waves in optical and hydrodynamical systems to financial volatility by identifying rogue-wave-like peaks with similar statistical properties. We use a Schrödinger equation where the potential follows the shape of a Kerr nonlinearity to examine the properties of financial volatility indices within a moving time window. We see evidence of Anderson localisation as a rogue peak approaches in the VIX, and show that the numerical gradient of the system's minimum eigenvalue reliably spikes at the onset of an extreme event. We adapt our methodology to simulate the real-time arrival of data, and show that all but one of the VIX's major peaks can be detected given a reasonable amount of history. We then perform two out-of-sample tests, one for the VXO index, and one for the VSTOXX index, and successfully replicate our initial results, identifying all but one major peak (87.5% or 7/8) in both cases. This method of analysis shows considerable promise as a tool for identifying potential financial crises, aiding in their mitigation.

q-fin.ST↗
arXiv2026

Where Backtests and Live Trading Part Ways: Six Defects from One Intraday System

Backtests of intraday strategies can drift away from what the live system actually does; in our case, the drift never showed in summary statistics. We report six defects found while running one intraday US equity system live and comparing its live days with their backtest replay. Three came from data: a default feed that carried a small fraction of real opening volume, historical prices adjusted for dividends that live orders never see, and data the live system downloaded but the backtest never did. Two came from code: a sizing step that exceeded a limit the live account enforces, and UTC timestamps read as New York time. One came from configuration: settings that were switched on but never reached the code that places orders. For each we give the symptom, the size of the error, the cause and the test or control that now guards against it. All six sat where two paths that should agree had drifted apart. We close with a ten-point checklist. We make no claim about whether the system is profitable.

q-fin.ST↗
arXiv2026

Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns

Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight. We build news topics from NoLBERT, an encoder pretrained only on text published between 1976 and 1995, adapted into a sentence encoder by contrastive fine-tuning on passages of the same article, with no labels or returns. We compare it with LDA, MiniLM and FinBERT readers on 1.26 million articles from 2012 to 2024, convert each reader's topics into attention shocks and stock exposures, and forecast the returns of the 1,000 largest US stocks with eight models at three rebalancing frequencies. Models are fitted on 2013-2019, choices are made on 2020-2021, and 2022-2024 is held out. The experiment with the best validation score loses money at every frequency. Measuring attention shocks against the past 63 trading days instead of 5 raises the test rank IC of every reader. NoLBERT gives the most stable topics and forecasts about as well as LDA. An equal-weight combination of all readers and models earns a monthly net Sharpe ratio of 1.12, significant against zero before adjusting for the search but not against a price-only benchmark.

q-fin.ST↗