arXiv · arXiv q-fin · 2026
We study seven major crypto-perpetual liquidation cascades (2022-2025), and in the largest of them we can watch the mechanism directly. From the on-chain fill log of a fully transparent venue we measure the branching ratio of that event -- the October 2025 crash, the largest on record -- in flight, with both of its factors observed and no free constants. It ran deeply subcritical: the structural ratio and the amplifi…
Ramon Marc Garcia Seuma
arXiv · arXiv · 2026
We develop a theoretical model that endogenizes the regulator's decision to impose short-selling bans to prevent large stock price declines. Empirically, we test the model's predictions using the cross-sectional variation in short-selling restrictions implemented across European countries in 2020. Consistent with our model, we find that bans had a detrimental effect on liquidity and failed to support the average pric…
Pasquale Della Corte, Robert Kosowski, Dimitris Papadimitriou, Nikolaos P. Rapanos
arXiv · arXiv · 2025
This study investigates the pre-trained RNN attention models with the mainstream attention mechanisms, such as additive attention, Luong's three attentions, global self-attention and sliding window sparse attention, for the empirical asset pricing research on the top 420 large-cap US stocks. This is the first paper on the large-scale state-of-the-art (SOTA) attention mechanisms applied in the asset pricing context. T…
Shanyan Lai
arXiv · arXiv · 2025
In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position as a result of a margin call to meet financial obligations. The simple act of buying or selling in the market causes a price impact and there is a cost described as liquidity risk. For example, when selling a large order, there is market sli…
Perukrishnen Vytelingum, Rory Baggott, Namid Stillman, Jianfei Zhang, Dingqiu Zhu
arXiv · arXiv · 2026
This paper proposes a simple and parsimonious discrete-time simulation model to describe the endogenous formation and periodic collapse of financial bubbles. While existing literature has extensively explored the statistical properties of locally explosive bubble dynamics, capturing the micro-level interplay of investor herd behavior and panic selling within a unified framework remains a challenge. Our model addresse…
Naohiro Yoshida
arXiv · arXiv · 2024
Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these strategies are becoming impotent. The DQN and A2C models have previously outperformed eminent humans in game-playing and robotics. In our work, we propose a reinforced portfolio manager offering assistan…
Ashish Anil Pawar, Vishnureddy Prashant Muskawar, Ritesh Tiku
arXiv · arXiv · 2026
Trader-facing dynamic fees are increasingly proposed for automated market makers (AMMs), but historical data do not identify how order flow would respond: trader-facing fees do not vary, trader types are latent, and a replayed tape is not a sequential decision environment. We therefore construct a minimal closed-loop simulator in which the missing signal exists by construction: two constant-product pools repriced by …
Wen-Ting Wang
arXiv · arXiv · 2026
We study permissionless spot--perpetual basis trading in decentralized finance as a collateral control problem. The strategy holds spot inventory, hedges directional exposure with a short perpetual, and allocates capital between spot inventory and derivative margin under on-chain liquidity and execution frictions. The paper delivers three results. First, it solves a static control problem for the collateral share and…
Anatoly Krestenko, Mikhail Butov, Rostislav Berezovskiy, Danila Bolotin
arXiv · arXiv · 2026
The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty. Current paradigms, such as Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), frequently induce model sycophancy, while execution-based environments suffer from adversarial "Test Evasion" by unconstrained agents. In this paper, we introduce an objective alignment pa…
Kun Liu, Liqun Chen
arXiv · arXiv · 2026
We reformulate the neutral wealth tax framework of Froeseth (2026; arXiv:2603.05264) in the language of stochastic dynamics and statistical physics. Individual wealth under geometric Brownian motion satisfies a Langevin equation with multiplicative noise; the probability density of wealth across a population then evolves according to a Fokker-Planck equation. A proportional wealth tax at market value enters as a unif…
Anders G Frøseth
arXiv · arXiv · 2025
Digital Asset Treasury (DAT) companies, public firms that hold large crypto reserves as a core strategy, deliver levered exposure to digital assets but face acute downside risk when equity premia over net asset value multiples (mNAV) compress in bear markets. This paper develops a survival framework that couples conservative treasury policy with an operating line that monetizes holdings independent of mark-to-market …
Hongzhe Wen
arXiv · arXiv · 2025
This paper investigates an infinite horizon, discounted, consumption-portfolio problem in a market with one bond, one liquid risky asset, and one illiquid risky asset with proportional transaction costs. We consider an agent with liquidity preference, modeled by a Cobb-Douglas utility function that includes the liquid wealth. We analyze the properties of the value function and divide the solvency region into three re…
Guohui Guan, Jiaqi Hu, Zongxia Liang
arXiv · arXiv · 2024
We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the…
Ravi Kashyap
arXiv · arXiv · 2023
Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contrac…
Ioannis Giagkiozis, Emilio Said
arXiv · arXiv · 2020
With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) por…
Gang Huang, Xiaohua Zhou, Qingyang Song
arXiv · arXiv · 2020
Inspired by the developments in deep generative models, we propose a model-based RL approach, coined Reinforced Deep Markov Model (RDMM), designed to integrate desirable properties of a reinforcement learning algorithm acting as an automatic trading system. The network architecture allows for the possibility that market dynamics are partially visible and are potentially modified by the agent's actions. The RDMM filte…
Tadeu A. Ferreira
arXiv · arXiv · 2020
Most insurance contracts are inherently linked to financial markets, be it via interest rates, or -- as hybrid products like equity-linked life insurance and variable annuities -- directly to stocks or indices. However, insurance contracts are not for trade except sometimes as surrender to the selling office. This excludes the situation of arbitrage by buying and selling insurance contracts at different prices. Furth…
Philippe Artzner, Karl-Theodor Eisele, Thorsten Schmidt
arXiv · arXiv · 2016
We show that typical behaviors of market participants at the high frequency scale generate leverage effect and rough volatility. To do so, we build a simple microscopic model for the price of an asset based on Hawkes processes. We encode in this model some of the main features of market microstructure in the context of high frequency trading: high degree of endogeneity of market, no-arbitrage property, buying/selling…
El Euch Omar, Fukasawa Masaaki, Rosenbaum Mathieu