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Results for “industrial demand” · papers 18 · wiki 2
Academic Papers · 18arXiv q-fin live 1 · desk corpus 69
arXiv · arXiv q-fin · 2024

The aftermath of the Covid pandemic in the forest sector: new opportunities for emerging wood products

Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and d

Mojtaba Houballah, Jean-Yves Courtonne, Henri Cuny, Antoine Colin, Mathieu Fortin
OpenAlex · The Journal of Finance · 2001 · cites 2189

The Determinants of Credit Spread Changes

ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly cross‐correlated, and principal components analysis implies they are mostly driven by a single common factor. Although

Pierre Collin-Dufresn, Robert S. Goldstein, J. Spencer Martin
arXiv · arXiv · 2026

When large trades are not (automatically) news: liquidity tail risk and price discovery

We examine how heavy-tailed liquidity demand changes price discovery in a sequential limit order book with asymmetric information. In our setting, liquidity suppliers observe aggregate order flow, not its decomposition into informed demand and uninformed liquidity shocks. With heavy-tailed uninformed aggregated order flow, large trades remain plausibly uninformed over a wider range of depths, flattening price impact

Umut Çetin, Mingwei Lin, Giulia Livieri
arXiv · arXiv · 2026

Liquidity-Based Audit of Algorithmic Trading Strategies

We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under a

Irene Aldridge
arXiv · arXiv · 2026

Quality-Adjusted Hit-Ratio Targeting in Corporate Bond Market Making

Hit ratio is a common service metric for electronic corporate bond market making, but raw hit-ratio targets can be economically misleading when client flow has heterogeneous adverse-selection content. This paper extends a stochastic-control framework for OTC bond RFQ market making with hit-ratio constraints by replacing raw hit ratio with a residual-quality-adjusted hit ratio. The key modelling distinction is that ad

Bouna Niang
arXiv · arXiv · 2026

Directional Liquidity and Geometric Shear in Pregeometric Order Books

We introduce a structural framework for the geometry of financial order books in which liquidity, supply, and demand are treated as emergent observables rather than primitive market variables. The market is modeled as a relational substrate without assumed metric, temporal, or price coordinates. Observable quantities arise only through observation, implemented here as a reduction of relational degrees of freedom foll

João P. da Cruz
arXiv · arXiv · 2026

Pregeometric Origins of Liquidity Geometry in Financial Order Books

We propose a structural framework for the geometry of financial order books in which liquidity, supply, and demand are treated as emergent observables rather than primitive economic variables. The market is modeled as an inflationary relational system without assumed metric, temporal, or price coordinates. Observable quantities arise only through projection, implemented here via spectral embeddings of the graph Lapla

João P. da Cruz
arXiv · arXiv · 2025

Equilibrium Liquidity and Risk Offsetting in Decentralised Markets

We study the economic viability of liquidity provision in decentralised exchanges (DEXs) within a structural framework in which market outcomes are endogenous. We formulate strategic interactions as a sequential game: a risk-averse liquidity provider (LP) sets the supply of liquidity in the DEX and a costly dynamic replication strategy in a centralised exchange (CEX), price-sensitive traders determine trading volumes

Fayçal Drissi, Xuchen Wu, Sebastian Jaimungal
arXiv · arXiv · 2025

Bootstrapping Liquidity in BTC-Denominated Prediction Markets

Prediction markets have gained adoption as on-chain mechanisms for aggregating information, with platforms such as Polymarket demonstrating demand for stablecoin-denominated markets. However, denominating in non-interest-bearing stablecoins introduces inefficiencies: participants face opportunity costs relative to the fiat risk-free rate, and Bitcoin holders in particular lose exposure to BTC appreciation when conver

Fedor Shabashev
arXiv · arXiv · 2024

Price-Aware Automated Market Makers: Models Beyond Brownian Prices and Static Liquidity

In this paper, we introduce a suite of models for price-aware automated market making platforms willing to optimize their quotes. These models incorporate advanced price dynamics, including stochastic volatility, jumps, and microstructural price models based on Hawkes processes. Additionally, we address the variability in demand from liquidity takers through models that employ either Hawkes or Markov-modulated Poisso

Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant, Julien Guilbert
arXiv · arXiv · 2023

Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools

Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential to train profitable agents for PM through interacting with financial markets. However, existing work mostly focuses on fixed stock pools, which is inconsistent with investors' pr

Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie
arXiv · arXiv · 2023

Deep Reinforcement Learning for ESG financial portfolio management

This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The study inclu

Eduardo C. Garrido-Merchán, Sol Mora-Figueroa-Cruz-Guzmán, María Coronado-Vaca
arXiv · arXiv · 2023

An axiomatic approach to default risk and model uncertainty in rating systems

In this paper, we deal with an axiomatic approach to default risk. We introduce the notion of a default risk measure, which generalizes the classical probability of default (PD), and allows to incorporate model risk in various forms. We discuss different properties and representations of default risk measures via monetary risk measures, families of related tail risk measures, and Choquet capacities. In a second step,

Max Nendel, Jan Streicher
arXiv · arXiv · 2023

A Myersonian Framework for Optimal Liquidity Provision in Automated Market Makers

In decentralized finance ("DeFi"), automated market makers (AMMs) enable traders to programmatically exchange one asset for another. Such trades are enabled by the assets deposited by liquidity providers (LPs). The goal of this paper is to characterize and interpret the optimal (i.e., profit-maximizing) strategy of a monopolist liquidity provider, as a function of that LP's beliefs about asset prices and trader behav

Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden
arXiv · arXiv · 2021

WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management

The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be made over time with the consideration of many factors such as investors preferences, trading environments, and market conditions. In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL)that can exploit more eff

Saeed Marzban, Erick Delage, Jonathan Yumeng Li, Jeremie Desgagne-Bouchard, Carl Dussault
arXiv · arXiv · 2020

DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency

We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, inte

Lewis Gudgeon, Sam M. Werner, Daniel Perez, William J. Knottenbelt
arXiv · arXiv · 2019

Unveiling the relation between herding and liquidity with trader lead-lag networks

We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative "opinion" to all agents about whether the traded asset price will go up or down. This opinion is reflect

Carlo Campajola, Fabrizio Lillo, Daniele Tantari
arXiv · arXiv · 2018

Liquidity in Competitive Dealer Markets

We study a continuous-time version of the intermediation model of Grossman and Miller (1988). To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an exogenous open market with finite liquidity. This endogenously leads to transient price impact in the dealer market. Smoot

Peter Bank, Ibrahim Ekren, Johannes Muhle-Karbe
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