The frontier of quantitative finance, in one feed. The newest peer-review-bound research from arXiv’s q-fin archive — trading and market microstructure, portfolio management, risk, pricing, and machine learning in markets — with titles, authors, and abstracts, linked straight to source. Updated continuously.
Execution, market making, order books, and market impact.
Muqiao Huang, Ruodu Wang, Yiyun Wang
We study equilibria in a closed, fee-free constant-function market maker (CFMM) economy with two assets and two traders. An interior state is a unilateral no-trade equilibrium exactly when the CFMM marginal price equals both traders' marginal rates of substitution. For an interior initial state, individually rational unilateral equilibria…
Kazumi Li, Masataka Hayashi, Teruo Nakatsuma, Peter Romero
Tick-level trade-and-quote data for the Tokyo Stock Exchange is distributed through the Nikkei NEEDS service as thousands of zipped CSV archives spanning four data types with era-dependent schemas and Japanese-language layouts. We present tse_tick, an open-source Python library that converts these raw archives into clean, typed Polars Dat…
Jihwan Woo
The stability of markets hosting leveraged exchange-traded products is governed not by any single product's loop gain but by the spectral radius of a loop-gain matrix, and scalar per-product monitoring underestimates system feedback by construction. Recent work measures the self-reinforcement of a leveraged fund's daily close rebalancing …
Nadav A. Kitron, Jonathan M. Wengrowicz
At 15-minute horizons, directional mean reversion is far stronger and more pervasive in cryptocurrency markets than in US equities: scored under one matched, strictly out-of-sample protocol, 90% of 183 Binance pairs carry significant directional reversal against 2.7% of 187 US stocks and ETFs, in every focal coin-year since 2021. The sign…
Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro Sánchez-Betancourt +1
Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, such as UniswapV3, are now a well-established design. In these markets, liquidity providers (LPs) face a sequential decision problem: they must decide when to rebalance their positions and which price ran…
Patrick Cheridito, Moritz Weiss
We introduce a reinforcement learning framework for market making in a limit order book. Our algorithm aims to maximize trading revenue by dynamically submitting market and limit orders of varying sizes across multiple price levels while controlling inventory size. We use multivariate logistic-normal distributions to model order allocatio…
Ruichao Jiang, Michelle Yeo, Long Wen
We study the blockchain censorship attack introduced in [21], which shows that joining the attack is a dominant strategy. We show that, by introducing certain detectability threshold, joining the attack can lead to strictly less reward for whales, which are defined to be a small number of validators that hold significantly more voting pow…
Zhuohan Wang, Andreea Bacalum, Ollie Olby, Carmine Ventre +1
Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only p…
Donghwa Seo, Doohwi Cha, Seunghan Son, Juyeong Lee +2
Crypto-listed equity perpetuals trade while the primary cash market is closed, yet still need a mark for margin, funding, and liquidation. We model the closed-window mark as the fixed point of an oracle operator with two blocks: external anchoring and self/peer derivative reference. From marks and proxies alone the two are observationally…
Debraj Das
The daily return of a stock is often restricted to an exchange-imposed band to curb extreme fluctuations. Any attempted price movement beyond this band is clipped, leaving an unobserved excess. We introduce a minimal stochastic latent-state model in which a fraction of this hidden excess is retained for the next day. This retention genera…
Yingli Wang, Yinhao Wu, Lingjiong Zhu
Hawkes-based microstructural foundations for rough volatility, leverage, and rough Heston-type limits were developed by El Euch et al. (2018, Finance Stoch., 22(2), 241--280) and connected to the affine rough Heston framework of El Euch and Rosenbaum (2019, Math. Finance, 29(1), 3--38). The rough Hawkes--Heston model with common price--vo…
Peter Cotton
We consider a market maker who can only obtain and dispose of inventory by responding to a sequence of sealed-bid enquiries, and whose customers arrive with imbalanced intent: sellers more often than buyers, or the reverse. Under the assumption that the best competing response is exponentially distributed around a commonly discerned fair …
Alex Chen, Maria Hybinette
Intraday market manipulation is hard to detect because its footprint is brief, buried in millions of quotes, and statistically similar to ordinary volatility. Detectors reach high recall only by flagging so many other days that measured precision collapses, producing alerts no regulator can act on. We show that this manipulation leaves a …
Daojing Zhai
Informed traders are supposed to need anonymity: they profit by hiding among the uninformed. A decentralized exchange now publishes the counterparty. Every committed order, cancellation, rejection, and fill carries a persistent pseudonymous wallet address. We reconstruct the full-depth limit order book from a record of 17.1 billion messag…
Zachary Feinstein, Ionut Florescu, Sean O'Leary
Automated market makers (AMMs) are typically interpreted and evaluated as decentralized exchanges. Herein, we take the perspective envisioned by Balancer that an AMM can also be viewed as a portfolio technology that programmatically enforces an economic mandate. In particular, we follow the geometric mean market maker (G3M) invariant empl…
Yuki Sato, Shunta Fujiwara, Kiyoshi Kanazawa
We develop an exactly solvable nonlinear time-series model by incorporating the square-root price-impact law into the Lillo--Mike--Farmer (LMF) model to resolve the diffusive price-dynamics paradox under predictable market-order flow. In financial market microstructure, it is well established that the price dynamics are approximately desc…
Lucas Rabechini Amaral
At the scale of seconds the observed mid carries a stationary, mean-reverting error around a latent efficient price. We build an order book whose own flow produces that error and solve for the trading rule that maximises the long-run average profit rate net of the bid-ask spread. In a liquid large-tick asset the spread is one tick or two,…
Gbenga Ibikunle, Ben Moews, Dmitriy Muravyev, Khaladdin Rzayev
We introduce data-driven measures of high-frequency trading (HFT) that distinguish between liquidity-supplying and liquidity-demanding strategies. We train machine learning models on a proprietary dataset with observed HFT activity, then apply these models to public intraday data to generate HFT measures across all U.S. stocks during 2010…
Amin Izadyar
I revisit the exchange rate disconnect puzzle, first documented by Meese and Rogoff (1983), using generative artificial intelligence (AI) to forecast currency returns based on economic fundamentals. Using ChatGPT and DeepSeek, I analyze a comprehensive dataset of economic data releases for major currency pairs and measure the fundamental …
Maksym Nechepurenko
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 s…
Maksym Nechepurenko
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 uncertain…
Aditya Dutta
Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn. We introduce Shadow Before Swap (SBS), a deployment policy that warm-refits a challenger off the serving path, evaluates it against the maintained incumbent on …
Zane Shen, Xinli Xu, Guangyi Zhang, Jialong Chen +6
Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To over…
Alexander Barzykin, Robert Boyce, Eyal Neuman, Sturmius Tuschmann
We derive a mesoscopic model for optimal execution with limit orders that incorporates microstructural features of passive price impact. Our framework is based on two empirical observables: the approximately exponential decay of limit-order fill probabilities with distance from the midprice, and the short-term linear response of price cha…
Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to a market-agnostic, supervised learning paradigm that relies on limited, static and human-annotated datasets, and thus are incapable of adapti…
Haibo Wang, Lutfu Sua, Jaime Ortiz, Jun Huang +1
Design/methodology/approach A time-varying parameter vector autoregression (TVP-VAR) model is employed to quantify dynamic connectedness and directional volatility spillovers using daily data from May 1, 2013, to May 2, 2023. The study isolates the impact of extreme events by splitting the data into pre- and post-COVID-19 samples based on…
Jiahao Weng
This study develops an agent-based financial market model to explain stock-price momentum and reversal through the joint effects of local herding and delayed information diffusion. Investors form heterogeneous Gaussian beliefs about the next-period price, choose among buying, selling, and remaining inactive, and revise their action probab…
Reina Ke Xin Li, Andreas Park, Andreas Veneris, Srisht Fateh Singh
Most currency pairs lack a direct liquid market, so international foreign exchange relies on routing transactions through a dominant vehicle currency. Multi-currency automated market makers (AMMs) offer an alternative by sharing liquidity across many currency pairs, facilitating direct cross-currency trade while exploiting liquidity conso…
Gregory Young
OpenMarket began as an attempt to trade Polymarket's BTC 15-minute binary markets against Binance BTC/USDT order flow. The attempt did not produce a tradable edge: out-of-sample, a walk-forward logistic model over 43 microstructure features does not beat, and slightly underperforms, the probability already implied by Polymarket's own orde…
Weiye Xi, Ciamac C. Moallemi
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) tha…
Adam Darmanin
We test whether five widely promoted retail signal families - trend, oscillator, candlestick, volume, and calendar rules - deliver a positive, economically meaningful, net-of-cost, and survivable edge. Practical viability is the conjunction of three predeclared gates: statistical edge after multiplicity correction, economic viability afte…
Ayoub Jadouli
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-int…
Dominik Feil, Max Nendel
Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentall…
Ciamac C. Moallemi, Dan Robinson, Brian Zhu
Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price st…
Jan Novotny
An order-book market whose liquidity provision is anchored to a fundamental value carries a restoring force: the price mean-reverts to value and the book refills after a shock. We show this restoring force is a robust intrinsic stabiliser and identify it causally-dialling the anchor down removes the mean-reversion, and a leverage-driven f…
Mikhail Perepelitsa
In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove t…
Jin Choi, Kasper Larsen
Foster and Viswanathan (1996) extend the discrete-time setting of Kyle (1985) to multiple informed traders who have partial information about the stock's terminal dividend. We resolve two long-standing open problems in this literature. First, we prove that an equilibrium exists in the setting of Foster and Viswanathan (1996). Second, as t…
Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived fr…
Ibrahim Ekren, Evangelos A. Nikitopoulos, Lu Vy
We develop a variational formulation of Kyle's model of informed trading that accommodates stochastic liquidity and multiple traded assets. The main equilibrium result is stated first: under a martingale dual condition, a matrix-valued martingale depth process generates a linear-Gaussian equilibrium with stochastic matrix-valued price imp…
Chan Kim, Peter Reinhard Hansen
Cryptocurrency markets exhibit periodic bursts in volatility and volume at one-minute, five-minute, and quarter-hour marks. Using trade data for six Binance perpetual contracts, we link these bursts to algorithmic participation: trade-size roundness declines sharply during them. The Autocorrelation Map, a clock-phase-resolved display, rev…
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