arXiv · arXiv · 2026
Hierarchical Risk Parity (De Pardo) and the Schur-complement generalization of Cotton are among the most widely adopted regularised portfolio construction methods, yet both are signal-blind: they solve only the minimum-variance problem and cannot accommodate an arbitrary expected-return forecast. This paper introduces three methods that incorporate alpha signals into hierarchical and regularised portfolio constructio…
Bernd Johannes Wuebben
arXiv · arXiv q-fin · 2025
Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints. While deep learning approaches show predictive capability, their opacity limits practical adoption, and single large language model (LLM) agents struggle to process the bread…
Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca, Yang Liu
arXiv · arXiv q-fin · 2024
Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant feature of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies …
Bruno de Melo, Jamiel Sheikh
arXiv · arXiv q-fin · 2026
Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extremely illiquid and volatile nature, the strategy exposes an exploitable attack surface: adversaries deploy manipulative bots to front-run trades, conceal positions, and fabricate sentiment, systematically extracting value from naïve copiers at scale. Despite its prevalence, bot-driven manipulation remains largel…
Yichen Luo, Yebo Feng, Jiahua Xu, Yang Liu
arXiv · arXiv q-fin · 2026
We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments. TradeArena, an auditable trading-agent testbed with risk reports, execution simulation, memory, and replayable trajectories, lets us analyze how rationales, positions, and interventions evolve under market stress. Code and data artifacts are available through the \href{https://github.com/…
Weicheng Xue
arXiv · arXiv q-fin · 2026
Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-density information and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing multimodal benchmarks. To address this gap, we propose UniFinEval, the first unified multimodal benchmark designed for high-information-density…
Zhi Yang, Lingfeng Zeng, Fangqi Lou, Qi Qi, Wei Zhang
arXiv · arXiv q-fin · 2025
Financial advisors and investors struggle with information overload from financial news, where irrelevant content and noise obscure key market signals and hinder timely investment decisions. To address this, we propose a novel Chain-of-Thought (CoT) summarization framework that condenses financial news into concise, event-driven summaries. The framework integrates user-specified keywords to generate personalized outp…
Tianyi Zhang, Mu Chen
arXiv · arXiv q-fin · 2024
As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant attention in this field, existing AI solutions often fall short due to their narrow focus on technical factors and limited capacity for discretionary judgment. These limita…
Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang
arXiv · arXiv q-fin · 2024
As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community's ability to enhance financial tasks effectively. Acknowledging financial analysis's critical role, we aim to devise financial…
Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang
arXiv · arXiv q-fin · 2024
Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of…
Shuoling Liu, Gaoguo Jia, Yuhang Jiang, Liyuan Chen, Qiang Yang
arXiv · arXiv q-fin · 2023
Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the financial reasoning capabilities of LLMs. We leverage mock exam questions of the Chartered Financial Analyst (CFA) Program to conduct a comprehensive evaluation of ChatGPT and GPT-4 in fi…
Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou, Yulong Pei, Mathieu Sibue
arXiv · arXiv q-fin · 2022
The study focuses on the Impact of Employment Benefit Cots on the Profitability of Companies listed in the National Stock Exchange. The study has considered the Amount spent on Employment Benefit Cots as an Independent variable and Profit after tax, Total Assets, Return on Equity, and Return on Asset and Debt equity Ration as the Dependent variable. The present study is to analyses the relationship between Employment…
Anil S, Sudharani R, Suresh N
arXiv · arXiv · 2012
The use of the trading halts is a practice common to all markets. However, the advantages and the disadvantages of the measurements are regularly discussed. The partisans think that the trading suspensions or the price limits make it possible to the investors to have time to react to the new information. The detractors think that the trading halts are barriers with the trade. A theorical debate thus continued with an…
Karine Michalon
arXiv · arXiv · 2022
A direct method for calculating default rates by industry and target corporate segments is not possible given the lack of statistical data. The proposed paper considers a model for filtering the dynamics of the probability of default of corporate companies and other borrowers based on indirect data on the dynamics of overdue debt supplied by the Bank of Russia. The model is based on the equation of the balance of tot…
Mikhail Pomazanov
arXiv · arXiv · 2026
We propose a new approach to model rainfall by combining heterogeneous data sources at different time scales. Continuous arrivals of rain cells are incorporated into a Hawkes process formalism that encompasses the classical Bartlett-Lewis and Neyman-Scott models, thereby providing a more flexible representation of clustering. Analysis of high frequency rainfall data (at the minute scale over several years) indicates …
Thomas Deschatre, Marc Hoffmann, Mathieu Rosenbaum