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Results for “mining” · papers 18 · wiki 3
Academic Papers · 18arXiv q-fin live 17 · desk corpus 1
arXiv · arXiv q-fin · 2026

FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining

Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mining now spans genetic programming, reinforcement learning, generative models, and large language model agents. Yet it remains unclear whether advances across these paradigms yield more generalizable, distinct, and economically useful financial signals. We introduce Factor…

Zhuohan Wang, Carmine Ventre
arXiv · arXiv q-fin · 2024

Text mining arXiv: a look through quantitative finance papers

This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. We extract and analyze crucial information from the entire documents, includin…

Michele Leonardo Bianchi
arXiv · arXiv q-fin · 2021

Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

Mining companies to properly manage their operations and be ready to make business decisions, are required to analyze potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to un…

Łukasz Bielak, Aleksandra Grzesiek, Joanna Janczura, Agnieszka Wyłomańska
arXiv · arXiv q-fin · 2016

Modeling and Simulation of the Economics of Mining in the Bitcoin Market

In January 3, 2009, Satoshi Nakamoto gave rise to the "Bitcoin Block Chain" creating the first block of the chain hashing on his computers central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to this increasing difficulty. Three generations of mining hardware have followed the CPU's generation. T…

Luisanna Cocco, Michele Marchesi
arXiv · arXiv q-fin · 2019

Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network

The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question.By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample,we first divide t…

Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen, Yuren Zhou
arXiv · arXiv q-fin · 2012

Mining the Web for the Voice of the Herd to Track Stock Market Bubbles

We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we show that week-to-week changes in power-law distributions reflect market movements of the Dow Jones I…

Aaron Gerow, Mark Keane
arXiv · arXiv q-fin · 2026

Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation

Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback. This survey reviews agentic quantitative trading across five stages: factor mining, signal discovery, portfolio construction, order execution, and risk management. We further examine agentic quant trading systems through architecture, coordination, and adaptation, while compa…

Fengrui Hua, Hengyi Yang, Xinlei Hao, Haohan Zhang, Bokai Cao
arXiv · arXiv q-fin · 2026

Agent-to-Agent Finance: Blockchain Payments and Trust Infrastructure for Autonomous AI Agents

Autonomous artificial intelligence (AI) agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financial markets: if software agents can act economically un…

Hui Gong
arXiv · arXiv q-fin · 2025

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization

Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this pap…

Yuante Li, Xu Yang, Xiao Yang, Minrui Xu, Xisen Wang
arXiv · arXiv q-fin · 2024

The Impact of Implicit Government Guarantee on Credit Rating of Municipal Investment Bonds

One type of bond with the most implicit government guarantee is municipal investment bonds. In recent years, there have been an increasing number of downgrades in the credit ratings of municipal bonds, which has led some people to question whether the implicit government guarantee may affect the objectivity of the bond ratings? This paper uses text mining methods to mine relevant policy documents related to municipal…

Yan Zhang, Yixiang Tian, Lin Chen
arXiv · arXiv q-fin · 2023

Exploiting Unfair Advantages: Investigating Opportunistic Trading in the NFT Market

As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT), staking and mining protocols have emerged. A financial ecosystem built on top of a blockchain is supposed to be fair and transparent for each participating actor. Yet, there are sophisticated actors who turn their domain knowledge and market inefficiencies to their…

Priyanka Bose, Dipanjan Das, Fabio Gritti, Nicola Ruaro, Christopher Kruegel
arXiv · arXiv q-fin · 2023

From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance

In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech…

Abha Naik, Esra Yeniaras, Gerhard Hellstern, Grishma Prasad, Sanjay Kumar Lalta Prasad Vishwakarma
arXiv · arXiv q-fin · 2018

A Big data analytical framework for portfolio optimization

With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework has been developed to incorporate both structured and unstructured data for portfolio optimization. …

Dhanya Jothimani, Ravi Shankar, Surendra S. Yadav
arXiv · arXiv q-fin · 2014

Using an Artificial Financial Market for studying a Cryptocurrency Market

This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strat…

Luisanna Cocco, Giulio Concas, Michele Marchesi
arXiv · arXiv q-fin · 2012

Online Portfolio Selection: A Survey

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural understanding of published online portfolio selection techniques. From an online machine learning perspec…

Bin Li, Steven C. H. Hoi
arXiv · arXiv · 2023

Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose…

Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
arXiv · arXiv q-fin · 2020

Topological Data Analysis for Portfolio Management of Cryptocurrencies

Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. This work presents a method to build an investment portfolio consisting of more than 1500 cryptocurrencies covering 6 years of market data. It is centred around Topological Data Analysis (TDA), a recent approach to analyze data sets from the…

Rodrigo Rivera-Castro, Polina Pilyugina, Evgeny Burnaev
arXiv · arXiv q-fin · 2026

CAST: A Cross-Asset State-Space Trading System for Drawdown Control in Stock Markets

Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management. However, mainstream stock forecasting methods predominantly optimize returns or Sharpe ratios under the independent and identically distributed (i.i.d.) assumption. Real markets do not follow this assumption, triggering catastrophic drawdowns. We propose a cros…

Yu Peng, Matloob Khushi, Josiah Poon
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