arXiv · arXiv q-fin · 2026
A physically backed leveraged event position requires real credit: if collateral C receives leverage L, the protocol supplies (L-1)C and uses the combined amount to acquire recognized event exposure. This paper develops a venue-agnostic on-chain credit architecture for that capital layer and an endogenous model of its capital market. It separates traders, Senior Credit LPs, market makers, liquidators, and Liquidation…
Maksym Nechepurenko
arXiv · arXiv q-fin · 2026
Leverage does not create manipulation or informed trading in event markets, but it changes their economics. We separate four conduct channels: market-price manipulation, real-world outcome manipulation, resolution-process manipulation, and informed trading that exploits non-public information without changing the event or resolution rule. A capital-constrained amplification model shows that gross directional gains sc…
Maksym Nechepurenko
arXiv · arXiv q-fin · 2026
We consider two canonical market-microstructure regularities: the long-memory of trade signs and the square-root law of meta-order impact. The point is not to propose new empirical laws, but to separate the clocks on which existing laws are defined. The sign-memory law is an event-time statement about the ordering and fragmentation of hidden orders. The square-root impact law is an operational-time statement about fr…
Christopher Angstmann, Tim Gebbie
arXiv · arXiv · 2021
Convertible instruments are contracts, used in venture financing, which give investors the right to receive shares in the venture in certain circumstances. In liquidity events, investors may have the option to either receive back their principal investment, or to receive a proportional payment after conversion of the contract to a shareholding. In each case, the value of the payment may depend on the choices made by …
Ron van der Meyden
arXiv · arXiv · 2026
Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata. dexamine is a Python package that parses Uniswap v2 and v3 events on Ethereum. It converts transaction receipt logs into observations of trades and liquidity changes, with token quantities, pool state, transaction order, and gas inform…
Magnus Hansson
arXiv · arXiv · 2026
Leveraged event positions combine a repayable loan with an outcome claim that may become non-tradable before oracle payout is final. This paper specifies Axient, a physically backed margin layer for binary event markets that separates leverage maturity from claim maturity and makes the hard-flat decision under explicit execution uncertainty. The model distinguishes quoted book proceeds, matched proceeds, settled proc…
Maksym Nechepurenko
arXiv · arXiv · 2026
RED-2400 is a public benchmark of 6,660 algorithmically-rejected trading events from a live Solana decentralised-exchange filter stack, observed continuously over 22 calendar days (2026-04-10T21:10Z through 2026-05-02T21:48Z, UTC). Each rejection event is linked to its post-rejection price-and-liquidity trajectory. The deposit contains 169,123 forward-outcome observations and 1,837 graveyard-tracker lifecycle snapsho…
Arati U. Kamat
arXiv · arXiv · 2026
The label event-linked perpetual often conflates mathematically different contracts. We replace a flat product list with a four-axis taxonomy: underlying geometry, temporal structure, settlement structure, and venue-oracle composition. The taxonomy covers a single binary probability, conditional ratios, event spreads, baskets, path functionals, liquidity indices, rolling sequences, and flow-only swaps. We derive a co…
Maksym Nechepurenko
arXiv · arXiv · 2022
Prudent management of insurance investment portfolios requires competent asset pricing of fixed-income assets with time-to-event contingent cash flows, such as consumer asset-backed securities (ABS). Current market pricing techniques for these assets either rely on a non-random time-to-event model or may not utilize detailed asset-level data that is now available with most public transactions. We first establish a fr…
Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan
arXiv · arXiv · 2010
We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask prices. Our study reveals a linear relation between order flow imbalance and price changes, with a slop…
Rama Cont, Arseniy Kukanov, Sasha Stoikov
arXiv · arXiv · 2026
Kalshi's multivariate-event architecture produces market objects on demand from exact selected legs. Across a registered seven-day interval, 190 independently validated temporal shards yield 7,611,594 unique REST MVE market tickers after excluding 5,777 boundary-overlap observations; the population was created at an average rate of 1.087 million objects per day, with strong hourly burstiness. The hierarchy is sharply…
Maksym Nechepurenko
arXiv · arXiv · 2026
Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make fine-grained labelling feasible at corpus scale, but only if the labels can be t…
Rian Dolphin, Joe Dursun, Jarrett Blankenship, Katie Adams, Quinton Pike
arXiv · arXiv · 2026
Rare events in time series are critical to model but hard to learn due to data scarcity. Current generative models struggle with extreme values. We observe that rare events leave distinct topological fingerprints - transitions in Betti numbers from point-cloud embeddings - that are more stable and discriminative than statistical moments. We introduce PHINN, a flow-matching framework using dynamic Betti curves as cond…
Emre Yusuf, Ren Takahashi, Jayabrata Bhaduri
arXiv · arXiv · 2026
Short-dated index options make scheduled macro-announcement risk visible in market prices, but visibility does not imply identification: a flexible no-event surface fitted to event-spanning quotes can absorb event premia, while a jump calibrated without event-spanning quotes is unidentified. To separate the continuous surface from the scheduled jump, we model Federal Open Market Committee (FOMC) decisions, Consumer P…
Tenghan Zhong
arXiv · arXiv · 2025
Generative modeling of high-frequency limit order book (LOB) dynamics is a critical yet unsolved challenge in quantitative finance, essential for robust market simulation and strategy backtesting. Existing approaches are often constrained by simplifying stochastic assumptions or, in the case of modern deep learning models like Transformers, rely on tokenization schemes that affect the high-precision, numerical nature…
Yang Li, Zhi Chen
arXiv · arXiv · 2024
Limit order book (LOB) is a dynamic, event-driven system that records real-time market demand and supply for a financial asset in a stream flow. Event stream prediction in LOB refers to forecasting both the timing and the type of events. The challenge lies in modeling the time-event distribution to capture the interdependence between time and event type, which has traditionally relied on stochastic point processes. H…
Zetao Zheng, Guoan Li, Deqiang Ouyang, Decui Liang, Jie Shao
arXiv · arXiv · 2024
This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochastic volatility effects and seasonal components capturing time-…
Igor Martins, Hedibert Freitas Lopes
arXiv · arXiv · 2024
This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis clustering, and subsequently utilize CAR (Cumulative Abnormal…
Vahidin Jeleskovic, Yinan Wan