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Results for “decoding” · papers 15 · wiki 1
Academic Papers · 15arXiv q-fin live 10 · desk corpus 8
arXiv · arXiv q-fin · 2024

Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics

The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of governmen

Alicia Vidler, Toby Walsh
arXiv · arXiv q-fin · 2022

Bridging the Gap: Decoding the Intrinsic Nature of Time in Market Data

Intrinsic time is an example of an event-based conception of time, used to analyze financial time series. Here, for the first time, we reveal the connection between intrinsic time and physical time. In detail, we present an analytic relationship which links the two different time paradigms. Central to this discovery are the emergence of scaling laws. Indeed, a novel empirical scaling law is presented, relating to the

James B. Glattfelder, Anton Golub
arXiv · arXiv · 2021

Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach

Volatility of financial stock is referring to the degree of uncertainty or risk embedded within a stock's dynamics. Such risk has been received huge amounts of attention from diverse financial researchers. By following the concept of regime-switching model, we proposed a non-parametric approach, named encoding-and-decoding, to discover multiple volatility states embedded within a discrete time series of stock returns

Xiaodong Wang, Fushing Hsieh
arXiv · arXiv q-fin · 2025

Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach

Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study in

Amarendra Mohan, Ameer Tamoor Khan, Shuai Li, Xinwei Cao, Zhibin Li
arXiv · arXiv q-fin · 2026

AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation toward interactive trading simulations. However, existing frameworks for evaluating real-time trading largely overlook a critical failure mode: the severe behavioral instability of LLMs in sequential decision-making under financial uncertainty. Through extensive experiments, we s

Wentao Zhang, Mingxuan Zhao, Jincheng Gao, Jieshun You, Huaiyu Jia
arXiv · arXiv q-fin · 2023

Artificial Intelligence-based Analysis of Change in Public Finance between US and International Markets

Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of t

Kapil Panda
arXiv · arXiv · 2025

Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference

Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quanti

Junliang Luo, Katrin Tinn, Samuel Ferreira Duran, Di Wu, Xue Liu
arXiv · arXiv · 2025

Choices or constraints: decoding financial empowerment among women entrepreneurs in France

This research examines the empowerment of women entrepreneurs in the context of entrepreneurial financing in France. It explores the factors that allow some women entrepreneurs to access certain categories of external finance more easily. The theoretical framework used is based on the concept of empowerment, explored through its personal and relational dimensions. The study relies on a quantitative approach, using da

Jonathan Labbé, Typhaine Lebègue, Abdel Malik Ola
arXiv · arXiv · 2014

Decoding Stock Market Behavior with the Topological Quantum Computer

A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcrossings and undercrossings. The braid of stocks prices has a remarkable connection with the topologica

Ovidiu Racorean
arXiv · arXiv · 2025

PEARL: Private Equity Accessibility Reimagined with Liquidity

In this work, we introduce PEARL (Private Equity Accessibility Reimagined with Liquidity), an AI-powered framework designed to replicate and decode private equity funds using liquid, cost-effective assets. Relying on previous research methods such as Erik Stafford's single stock selection (Stafford) and Thomson Reuters - Refinitiv's sector approach (TR), our approach incorporates an additional asymmetry to capture th

E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk, T. Jacquot
arXiv · arXiv q-fin · 2025

Building Trust in Illiquid Markets: an AI-Powered Replication of Private Equity Funds

In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor trust and systemic stability, particularly in periods of height

E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk, T. Jacquot
arXiv · arXiv q-fin · 2021

DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities

Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relat

Shuo Sun, Wanqi Xue, Rundong Wang, Xu He, Junlei Zhu
arXiv · arXiv q-fin · 2023

Benchmarking Large Language Model Volatility

The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentiment analysis, we uncover substantial variability in sentence-level sentiment classification results, underscoring the innate volatility of LLM outputs. These uncertainties cascade downstream, leading t

Boyang Yu
arXiv · arXiv q-fin · 2022

Portfolio Transformer for Attention-Based Asset Allocation

Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Transformer (PT) network, introduced here, circumvents the need to predict asset returns and instead dire

Damian Kisiel, Denise Gorse
arXiv · arXiv q-fin · 2020

QuantNet: Transferring Learning Across Systematic Trading Strategies

Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from multiple markets, current approaches in trading rely mainly on learning trading strategies per individual market. In this paper, we take a step towards developing fully end-to-end global trading strategies that leverage systematic trends t

Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg, Nick Firoozye, Philip Treleaven
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