Curated items for this scope are still being collected. Below are the latest related updates; items meeting the curation criteria will be marked separately.
This study formulates and solves a stochastic control problem for Ethena protocol's core yield strategy, which generates returns through a delta-neutral combination of long stETH positions and short ETH perpetual contracts of equal size. The model takes the rate of simultaneously buying stETH and shorting perpetual contracts as the control variable, incorporating both permanent price impact and temporary execution slippage. The study solves both the infinite-horizon discounted problem and the finite-horizon problem with terminal liquidation costs, obtaining explicit optimal control solutions in both cases.
The study addresses market-making challenges in Polymarket's thin order books, using the JaxMARL-HFT framework to train cooperative multi-agent market-making models on 417 real markets. The market-making agents are split into quoters and risk managers, trained using recurrent independent proximal policy optimization (IPPO) combined with RNN policies.
Testing showed that the improved single-agent market-making model quoted more frequently and had smaller out-of-sample losses, but none of the models achieved positive average portfolio value in out-of-sample evaluation, with only single-seed training completed.
This arXiv paper tests the degree to which 4 AI trading agents adhere to their own strategies and risk rules, based on 2627 closed trades from February 12 to August 27, 2026. 18% of the trades were live orders on Hyperliquid, while the rest were simulated at real-time prices, with a uniform round-trip fee of 0.09%.
This study introduces LOBGrad, a conditional DDPM framework for probabilistic order book forecasting. The framework combines an autoregressive RNN encoder with a diffusion-based generator, and establishes an excess-risk oracle inequality for the single-step DDPM noise prediction objective under exponential mixing and regularity assumptions.
The study proposes an Alpha-stable Lévy diffusion predictor called SHADE, which achieves distribution-level shrinkage through a residual stable kernel by reading outputs at non-zero depths, addressing the problem of overly narrow predictive distributions and overconfidence when Gaussian diffusion models forecast heavy-tailed time series.
Quantocracy has compiled the latest quant research-related links as of October 18, 2026, covering topics including trend following, futures continuous contract construction, in-sample to out-of-sample Sharpe ratio decay, the impact of liquidity on stock returns and option pricing, and cryptocurrency cross-sectional trend factors. One study based on 12 commodity futures found that the expected retention ratio of in-sample Sharpe ratios from a 10-year backtest on new data is 30%.
The research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.
This study proposes a framework for identifying and quantifying arbitrage opportunities between logically related prediction market contracts, demonstrating that arbitrage exists when prices fall outside the convex hull of feasible return vectors, and solving for the optimal arbitrage portfolio via integer programming. The study recovered 1.9 million logical relationships through semantic analysis of platform metadata and settlement rules, constructing a constraint system covering more than 255,000 contracts.
This 16-page paper, accepted by MARBLE 2026, argues that crypto-exchange stock-like perpetual contracts calculate funding rates from the real-time basis and cannot replicate shareholder dividend cash flows for traders entering or exiting at arbitrary times. The authors derive a fair funding-rate identity, convert point-in-time fairness into an optimal stopping condition, and use measure theory to show that predictable dividends are atomic events. Absolutely continuous funding-rate paths cannot replicate this atom over a holding interval and must be matched with discrete event credits.
Antonis Ballis and Christos Ioannidis released a working paper on arXiv, using a sample of all 8,970 crypto assets and 690 weekly cross-sections from 2013 to 2026 to directly observe asset entry, exit, and survival, and to test the weak-form efficiency symmetry constraints of the Markov chain for the crypto asset market capitalization distribution.
The Bank for International Settlements released Working Paper No. 1385, which compares the total costs of three cross-border payment channels: traditional correspondent banking and SWIFT-based bank transfers, fintech remittance operators such as Wise and Remitly, and stablecoin transfers. The study combines intraday fintech pricing, World Bank remittance data, bank transfer transactions, and crypto market data to construct comparable cost indicators across currencies and remittance sizes, distinguishing between foreign exchange markups and explicit fees.
