Market Volatility

Market-Dependent Communication in Multi-Agent Alpha Generation ArXiv ID: 2511.13614 “View on arXiv” Authors: Jerick Shi, Burton Hollifield Abstract Multi-strategy hedge funds face a fundamental organizational choice: should analysts generating trading strategies communicate, and if so, how? We investigate this using 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing five organizational structures from isolated baseline to collaborative and competitive conversation. We show that communication improves performance, but optimal communication design depends on market characteristics. Competitive conversation excels in volatile technology stocks, while collaborative conversation dominates stable general stocks. Finance stocks resist all communication interventions. Surprisingly, all structures, including isolated agents, converge to similar strategy alignments, challenging assumptions that transparency causes harmful diversity loss. Performance differences stem from behavioral mechanisms: competitive agents focus on stock-level allocation while collaborative agents develop technical frameworks. Conversation quality scores show zero correlation with returns. These findings demonstrate that optimal communication design must match market volatility characteristics, and sophisticated discussions don’t guarantee better performance. ...

Temperature Measurement in Agent Systems ArXiv ID: 2507.08394 “View on arXiv” Authors: Christoph J. Börner, Ingo Hoffmann Abstract Models for spin systems, known from statistical physics, are applied analogously in econometrics in the form of agent-based models. The models discussed in the econophysics literature all use the state variable $T$, which, in physics, represents the temperature of a system. However, there is little evidence on how temperature can be measured in econophysics, so that the models can be applied. Only in idealized capital market applications has the relationship between temperature and volatility been demonstrated, allowing temperature to be determined through volatility measurements. The question remains how this can be achieved in agent systems beyond capital market applications. This paper focuses precisely on this question. It examines an agent system with two decision options in a news environment, establishes the measurement equation, and outlines the basic concept of temperature measurement. The procedure is illustrated using an example. In an application with competing subsystems, an interesting strategy for influencing the average opinion in the competing subsystem is presented. ...

A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets – A New Microfoundations of GARCH model ArXiv ID: 2409.12516 “View on arXiv” Authors: Unknown Abstract The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics. ...

Signature of maturity in cryptocurrency volatility ArXiv ID: 2409.03676 “View on arXiv” Authors: Unknown Abstract We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices. ...

Review of the EU ETS Literature: A Bibliometric Perspective ArXiv ID: 2409.01739 “View on arXiv” Authors: Unknown Abstract This study conducts a bibliometric review of scientific literature on the European Union Emissions Trading System (EU ETS) from 2004 to 2024, using research articles from the Scopus database. Using the Bibliometrix R package, we analyze publication trends, key themes, influential authors, and prominent journals related to the EU ETS. Our results indicate a notable increase in research activity over the past two decades, particularly during significant policy changes and economic events affecting carbon markets. Key research focuses include carbon pricing, market volatility, and economic impacts, highlighting a shift toward financial analysis and policy implications. Thematic mapping shows cap-and-trade systems, and carbon leakage as central topics linking various research areas. Additionally, we observe key areas where further research could be beneficial, such as expanding non-parametric methodologies, deepening the exploration of macroeconomic factors, and enhancing the examination of financial market connections. Moreover, we highlight recent and innovative papers that contribute new insights, showcasing emerging trends and cutting-edge approaches within the field. This review provides insights for researchers and policymakers, highlighting the evolving landscape of EU ETS research and its relevance to global climate strategies. ...

Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations ArXiv ID: 2311.14419 “View on arXiv” Authors: Unknown Abstract Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. ...

A simulated electronic market with speculative behaviour and bubble formation ArXiv ID: 2311.12247 “View on arXiv” Authors: Unknown Abstract This paper presents an agent based model of an electronic market with two types of trading agents. One type follows a mean reverting strategy and the other, the speculative trader, tracks the maximum realised return over recent trades. The speculators have a distribution of returns concentrated on negative returns, with a small fraction making profits. The market experiences an increased volatility and prices that greatly depart from the fundamental value of the asset. Our research provides synthetic datasets of the order book to study its dynamics under different levels of speculation ...

Regularity in forex returns during financial distress: Evidence from India ArXiv ID: 2308.04181 “View on arXiv” Authors: Unknown Abstract This paper uses the concepts of entropy to study the regularity/irregularity of the returns from the Indian Foreign exchange (forex) markets. The Approximate Entropy and Sample Entropy statistics which measure the level of repeatability in the data are used to quantify the randomness in the forex returns from the time period 2006 to 2021. The main objective of the research is to see how the randomness of the foreign exchange returns evolve over the given time period particularly during periods of high financial instability or turbulence in the global financial market. With this objective we look at 2 major financial upheavals, the subprime crisis also known as the Global Financial Crisis (GFC) during 2006-2007 and the recent Covid-19 pandemic during 2020-2021. Our empirical results overwhelmingly confirm our working hypothesis that regularity in the returns of the major Indian foreign exchange rates increases during times of financial crisis. This is evidenced by a decrease in the values of the sample entropy and approximate entropy before and after/during the financial crisis period for the majority of the exchange rates. Our empirical results also show that Sample Entropy is a better measure of regularity than Approximate Entropy for the Indian forex rates which is in agreement with the theoretical predictions. ...

Investment Opportunities and Strategies in an Era of Coronavirus Pandemic ArXiv ID: ssrn-3567445 “View on arXiv” Authors: Unknown Abstract The COVID-19 continues to hit the world economy as well as the financial markets. As a result of the coronavirus spread across all continents, the majority of t Keywords: COVID-19 Impact, Market Volatility, Systemic Risk, Economic Shock, Financial Contagion, Global Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper relies on qualitative analysis and sector descriptions without advanced mathematical models, and the empirical component is limited to basic stock price observations and news citations rather than rigorous backtesting or data analysis. flowchart TD A["Research Goal:<br>Assess COVID-19 impact on markets and identify investment strategies"] --> B{"Key Methodology"}; B --> C["Data: Global Equities, Volatility Indices, Economic Indicators"]; B --> D["Analysis: Systemic Risk &<br>Financial Contagion Modeling"]; C --> E["Computational Process:<br>Shock Simulation & Volatility Correlation"]; D --> E; E --> F["Key Findings & Outcomes"]; F --> G["Identified High-Risk Sectors"]; F --> H["Revealed Opportunities in Resilient Assets"]; F --> I["Strategic Recommendations for Mitigating Economic Shock"];

DigitalFinance& The COVID-19 Crisis ArXiv ID: ssrn-3558889 “View on arXiv” Authors: Unknown Abstract The COVID-19 coronavirus crisis is putting unprecedented strain on markets, governments, businesses and individuals. The human, economic and financial costs are Keywords: COVID-19, Market Volatility, Systemic Risk, Economic Impact, Cross-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a high-level policy and regulatory analysis with no mathematical models or empirical backtesting, focusing on conceptual strategies and qualitative recommendations. flowchart TD A["Research Goal: Impact of COVID-19<br>on Digital Finance Markets"] --> B["Data Collection"] B --> C["Methodology: Cross-Asset Analysis"] C --> D["Computational Process:<br>Volatility & Risk Modeling"] D --> E["Key Findings"] subgraph B ["Data/Inputs"] B1["Market Volatility Data"] B2["Systemic Risk Indicators"] B3["Economic Impact Metrics"] end subgraph E ["Outcomes"] E1["Increased Market Volatility"] E2["Systemic Risk Transmission"] E3["Cross-Asset Correlation Spike"] end