arXiv · arXiv q-fin · 2026
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 employed by that protocol in order to enforce a target-weighted portfolio. We introd…
Zachary Feinstein, Ionut Florescu, Sean O'Leary
arXiv · arXiv q-fin · 2024
We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negat…
Michele Azzone, Emilio Barucci, Davide Stocco
arXiv · arXiv q-fin · 2026
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This p…
Giovanni Dispoto, Marcello Restelli, Carmine Ventre
arXiv · arXiv q-fin · 2026
This paper introduces a transformative framework for managing path-dependent financial risk by shifting from traditional distribution-centric models to a geometry-based approach. We propose the SigSwap as a new regulatory instrument that allows market participants to decompose complex risk into terminal price law and the underlying texture of the price path. By utilising the mathematical properties of the path-signat…
Daniel Bloch
arXiv · arXiv q-fin · 2026
The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming solver (Gurobi), and a model-free deep reinforcement learning agent (SAC). Evaluating these pipelines on the Jensen-Kelly-Pedersen 13-factor equity library acros…
Nirvik Sahoo, Chyng Wen Tee, Paul Robert Griffin
arXiv · arXiv q-fin · 2026
Portfolio optimization under strict cardinality constraints is a combinatorial challenge that defies classical convex optimization techniques, particularly in the context of "Direct Indexing" and ESG-constrained mandates. In the Noisy Intermediate-Scale Quantum (NISQ) era, the Quantum Approximate Optimization Algorithm (QAOA) offers a promising hybrid approach. However, standard QAOA implementations utilizing transve…
Javier Mancilla, Theodoros D. Bouloumis, Frederic Goguikian
arXiv · arXiv q-fin · 2026
AI agents can select tools, counterparties, and transaction parameters, yet inference should not itself confer authority to execute a financial action. This study develops and evaluates Authority-Inference Separation (AIS), an intent-centered architecture for bounded agentic finance. AIS treats a financial action intent as the control object: a machine-generated proposal can receive temporary executable authority onl…
Hui Gong, Michail Samawi, Francesca Medda
arXiv · arXiv q-fin · 2025
Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (…
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen
arXiv · arXiv q-fin · 2025
We develop a dynamic portfolio-choice framework in which investors target the region of the payoff distribution that the portfolio is designed to improve. Out of sample, the estimated policies form an ordered frontier: the policy focused on the downside delivers the strongest left-tail protection and the highest Sharpe ratio, while the policy focused on the upper quantile earns the highest mean return. The gains over…
Jozef Barunik, Lukas Janasek, Attila Sarkany
arXiv · arXiv q-fin · 2025
Loss Given Default (LGD) is a key risk parameter in determining a bank's regulatory capital. During LGD-estimation, realised recovery cash flows are to be discounted at an appropriate rate. Regulatory guidance mandates that this rate should allow for the time value of money, as well as include a risk premium that reflects the "undiversifiable risk" within these recoveries. Having extensively reviewed earlier methods …
Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, Helgard Raubenheimer
arXiv · arXiv q-fin · 2024
This paper examines the pivotal role central banks play in advancing sustainable finance, a crucial component in addressing global environmental and social challenges. As supervisors of financial stability and economic growth, central banks have dominance over the financial system to influence how a country moves towards sustainable economy. The chapter explores how central banks integrate sustainability into their m…
A T M Omor Faruq, Md Toufiqul Huq
arXiv · arXiv q-fin · 2023
For vanilla derivatives that constitute the bulk of investment banks' hedging portfolios, central clearing through central counterparties (CCPs) has become hegemonic. A key mandate of a CCP is to provide an efficient and proper clearing member default resolution procedure. When a clearing member defaults, the CCP can hedge and auction or liquidate its positions. The counterparty credit risk cost of auctioning has bee…
Dorinel Bastide, Stéphane Crépey, Samuel Drapeau, Mekonnen Tadese
arXiv · arXiv q-fin · 2022
Most applications of machine learning for finance are related to forecasting tasks for investment decisions. Instead, we aim to promote a better understanding of financial markets with machine learning techniques. Leveraging the tremendous progress in deep learning models for natural language processing, we construct a hierarchical Reformer ([15]) model capable of processing a large document level dataset, SEDAR, fro…
Francois Mercier, Makesh Narsimhan
arXiv · arXiv q-fin · 2022
Individual trade orders are often bunched into a block order for processing efficiency, where in post execution, they are allocated into individual accounts. Since Regulators have not mandated any specific post trade allocation practice or methodology, entities try to rigorously follow internal policies and procedures to meet the minimum Regulatory ask of being procedurally fair and equitable. However, as many have f…
Ali Hirsa, Massoud Heidari
arXiv · arXiv q-fin · 2017
Over-the-counter markets are at the center of the postcrisis global reform of the financial system. We show how the size and structure of such markets can undergo rapid and extensive changes when participants engage in portfolio compression, a post-trade netting technology. Tightly-knit and concentrated trading structures, as featured by many large over-the-counter markets, are especially susceptible to reductions of…
Marco D'Errico, Tarik Roukny