arXiv · arXiv · 2026
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a g…
Daniele Maria Di Nosse, Fabrizio Lillo
arXiv · arXiv · 2022
Solving portfolio management problems using deep reinforcement learning has been getting much attention in finance for a few years. We have proposed a new method using experts signals and historical price data to feed into our reinforcement learning framework. Although experts signals have been used in previous works in the field of finance, as far as we know, it is the first time this method, in tandem with deep RL,…
MohammadAmin Fazli, Mahdi Lashkari, Hamed Taherkhani, Jafar Habibi
arXiv · arXiv · 2020
Although the CML (Capital Market Line), the Intertemporal-CAPM, the CAPM/SML (Security Market Line) and the Intertemporal Arbitrage Pricing Theory (IAPT) are widely used in portfolio management, valuation and capital markets financing; these theories are inaccurate and can adversely affect risk management and portfolio management processes. This article introduces several empirically testable financial theories that …
Michael Nwogugu
arXiv · arXiv · 2016
In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris - Euronext, London and Frankfurt - Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancements to the basic procedure that, depending on the time period and the exchange, are shown to be signi…
Ioane Muni Toke
arXiv · arXiv · 2026
Price-level limit order book (L2) data reveal aggregate liquidity but not the ordered queue required by price--time priority. Passive-execution backtests can therefore depend on an unobserved cancellation-allocation rule even when observed prices, quantities, and trades are held fixed. We frame recovery of market-by-order histories from aggregate snapshots as a conditional partial identification problem: multiple his…
Riya Danait, Yuliana Zamora, Ioana Boier
arXiv · arXiv · 2026
Cryptocurrency markets exceed USD 3 trillion in capitalisation, yet practitioners lack an interpretable, channel-decomposed composite for characterising crypto-native systemic stress. We introduce the Aggregated Systemic Risk Index (ASRI), built from four weighted sub-indices -- Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%, implemented as a TradFi-stress proxy), and Regulatory O…
Murad Farzulla, Andrew Maksakov
arXiv · arXiv · 2025
Stablecoins have emerged as a significant component of global financial infrastructure, with aggregate market capitalization surpassing USD250 billion in 2025. Their increasing integration into payment and settlement systems has simultaneously introduced novel channels of systemic exposure, particularly liquidity risk during periods of market stress. This study develops a hybrid monetary architecture that embeds fiat…
Hongzhe Wen, R. S. M. Lau
arXiv · arXiv · 2022
Emerging markets such as India provide investors with returns far greater than those in developed markets; taking the average returns from the period 1995 to 2014 the returns are 4.714% to 3.276% of the developed market. The majority of emerging markets commenced joining with the capital market of the world, thus allowing a huge inflow of capital which in turn paved the path for economic growth. Even though the emerg…
Ganapathy G Gangadharan, N. Suresh
arXiv · arXiv · 2026
Retail proprietary-trading firms sell a two-stage product: a paid evaluation that must reach a profit target before breaching a trailing drawdown, then a funded account that must survive a minimum window and a consistency rule before a payout. We show the geometry of this contract creates incentives that differ by stage and make passing a poor standalone signal of skill. Under end-of-day trailing the evaluation rewar…
Nicholas Hall
OpenAlex · Review of Financial Studies · 2003 · cites 1020
We investigate whether the volatility risk premium is negative by examining the statistical properties of delta-hedged option portfolios (buy the option and hedge with stock). Within a stochastic volatility framework, we demonstrate a correspondence between the sign and magnitude of the volatility risk premium and the mean delta-hedged portfolio returns. Using a sample of S&P 500 index options, we provide empirical t…
Gurdip Bakshi, Nikunj Kapadia
arXiv · arXiv · 2026
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1,027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning,…
Junyi Ye, Gargi Vijay Borde
arXiv · arXiv · 2026
This paper studies public-private partnerships that delegate access-to-credit programs to private equity and venture-capital intermediaries. The public sector seeks to relax credit rationing and expand lending to socially valuable firms, while delegated monitors screen applicants, allocate subsidized loans, and bear agency costs. The paper develops a mechanism-design model showing that the same delegated intermediati…
G. Charles-Cadogan
arXiv · arXiv · 2026
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 the same next week of delayed labels, and promotes it only after a fixed paired …
Aditya Dutta
arXiv · arXiv · 2026
This paper studies Relief-Gated Relative Rotation (RGRR), a two-ETF rule that allocates between QQQ and DIA by mapping screened relative and macro states into a continuous QQQ weight. RGRR is economic rather than mechanical: it rotates between a growth-heavy sleeve and a Dow/value-heavy sleeve only when QQQ-DIA relative states are confirmed by rate, volatility, credit, or broad-market relief conditions. Candidate mai…
Zheli Xiong
arXiv · arXiv · 2026
This paper studies a singular dividend control problem for a firm with heterogeneous shareholders whose discount rates follow a given distribution. The central planner aggregates expected discounted payoffs using an ambiguity aggregation function $phi$, which captures shareholder heterogeneity and ambiguity attitudes but also leads to time inconsistency. To address this issue, we seek a time-homogeneous equilibrium d…
Yue Cao, Guohui Guan, Zongxia Liang, Xiaodong Luo
arXiv · arXiv · 2026
Large language models (LLMs) are increasingly used as components of agentic systems that observe, plan, and act. In finance, even "assistive" systems become decision-relevant once their outputs are used to size trades or allocate risk. A key failure mode is the confidence--competence gap: a model that is only slightly better than chance but consistently overconfident will, under typical bet-sizing rules, generate neg…
Rishab Ghosh, Vinay Devarakonda
arXiv · arXiv · 2026
The distribution of income and wealth in developed economies exhibits a robust two-class structure: an exponential (Boltzmann--Gibbs) bulk covering $\sim\!97\%$ of the population, and a power-law (Pareto) tail in the upper $\sim\!3\%$. We derive this structure from first principles via an explicit mechanistic chain: Gibrat's law for firm growth implies a Zipf firm-size distribution; maximum entropy applied to within-…
Robert T. Nachtrieb
arXiv · arXiv · 2025
This study investigates the efficacy of Conditional Restricted Boltzmann Machines (CRBMs) for modeling high-dimensional financial time series and detecting systemic risk regimes. We extend the classical application of static Restricted Boltzmann Machines (RBMs) by incorporating autoregressive conditioning and utilizing Persistent Contrastive Divergence (PCD) to incorporate complex temporal dependency structures. Comp…
Siddhartha Srinivas Rentala