Auditing, Earnings Management, Governance

Últimos artículos

• A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models

  Yingzhu Zhao, Vlad Pandelea, Han Yuan, Bo Hu, Wuqiong Luo, Li Zhang, Zheng Ma · 2 de octubre de 2026

  Large language models (LLMs) have been increasingly used for financial document analysis, including earnings call transcripts (ECTs). Beyond generating standalone claims, users increasingly prefer grounded analyses that pair claims with verifiable citations from source documents to enable independen…

• Resolving the Missing Financial Data Crisis: A Generative AI Pipeline for SEC 10-K Extraction

  Prisha Nair, Roee Shraga · 1 de octubre de 2026

  SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem affecting over 70% of firms and half of total market capitalization. This can disproportionately bias quantitative analysis against smaller firms, whic…

• MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting

  Dong Shu, Yanguang Liu, Huopu Zhang, Saisai Hu, Haiyan Zhao, Hekun Huang, Mengnan Du · 1 de octubre de 2026

  Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle communication signals. However, existing financial benchmarks are often limited to unimodal inputs or single-task settings, making it difficult to evalua…

• Better Deck or Different Judge? Evaluating Agentic Harness Gains in Corporate and Investment Banking

  Ludovic Gibert, Matis Despujols, Andre-Louis Rochet · 1 de octubre de 2026

  Corporate and investment banking teams use presentations to support credit decisions and advise clients on financing and transactions. Producing these decks requires reconciling financial data, tracing sources and turning analysis into a recommendation. We retrospectively study the development of an…

• Same Text, Different Numbers: The Divergence of LLM-Based Measures

  Hamid Boustanifar, Sasan Mansouri · 28 de septiembre de 2026

  Researchers increasingly use generative large language models (LLMs) to convert corporate text into empirical variables. We examine the extent to which LLM-based textual measures are invariant to model choice using thirteen measures, including sentiment, management clarity, uncertainty, answer speci…

• CatchBench: When Can an Agent Failure Be Caught?

  Yue Zhao, Mengyuan Li, Ruolin Li, Prince Zizhuang Wang, Shuli Jiang, Linsey Pang, Xiongye Xiao, Xiyang Hu · 18 de septiembre de 2026

  When can an agent failure be caught? A weak audit score alone cannot identify whether the record or the method is limiting. CatchBench therefore puts one auditor's question to three information states: the declared configuration before a run (PRE), a growing prefix of its trace (LIVE), and the finis…

• Same Day, Same Story; One Day Ahead, a Different Signal: The Dual Validity of Financial Sentiment

  AS Aravinthkakshan, Laven Srivastava, Harsh Nandwani · 11 de septiembre de 2026

  Financial NLP has a standard workflow: validate a sentiment tool against human labels, then trust it to extract market signal. This assumes the two evaluations measure the same thing. We test that assumption in a setting where both can be measured at once: a corpus of securities class actions (2002-…

• A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings

  Jean-Fran\c{c}ois Delpech · 11 de septiembre de 2026

  High-dimensional dense text embeddings and large language models face real obstacles in financial-disclosure analysis: context-window limits, hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across independently trained models. We present a training-free, alig…

• A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls

  Mirae Kim, Seonghun Jeong, Youngjun Kwak · 31 de agosto de 2026

  Existing approaches to evasion detection in earnings calls focus on textual transcripts, treating evasion as a single-dimensional phenomenon. We argue that evasion in spoken communication is inherently multidimensional: beyond what executives say, how they say it carries independent and complementar…

• Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows

  Miao Liu, Zhizhe Liu · 26 de agosto de 2026

  Large language models (LLMs) are increasingly deployed as AI analysts to process financial disclosures and support AI-assisted investment decisions. Yet such systems are usually evaluated by what they can retrieve, not whether retrieved information affects their judgments. We identify a retrieval-in…

• AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure

  Qi Wang · 31 de julio de 2026

  Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-use. This study develops AWARE-FX, an auditable AI/NLP decision-support system that converts report text into traceable firm-year hedging-disclosure m…

• Capital Markets LLM Reliability Score (CM-LRS): From Plausible to Bankable

  Prerit Ahuja · 24 de julio de 2026

  In capital-markets workflows the question is rarely whether a large language model can produce a fluent draft, but whether the draft is bankable: defensible in front of a counter-party or a regulator, with the documents in hand. Existing methods address parts of that gap: open-domain QA benchmarks r…

• HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks

  Haozhe Jia · 22 de julio de 2026

  Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks. Existence is settled; what users lack is a cheap way to audit a given model for it. We present HindsightBench, a black-box behavioral audit protocol that profiles parametric hindsight in an…

• What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification

  Guosheng Li, Fenghui Ren, Bin Liu, Chuan Yu, Kaiying Ji, Lin Yue, Jun Shen, Sasa Qian · 21 de julio de 2026

  Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources -- annual reports, press releases, and earnings calls -- that differ in length, purpose, and writing style. Existing evaluations are mostly condu…

• Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market

  Xiao Han, Yao Xiao, Zhen Zhang, Moxuan Zheng · 21 de julio de 2026

  We present an interpretable machine learning pipeline to decompose cross-sectional equity return predictability into auditable factor contributions. We apply an XGBoost model with TreeSHAP attribution and conduct stress testing on 3,632 Chinese A-share stocks from 2009 until 2019. On prediction, usi…

• Financial Audit Assistance using Misinformation Detection and Explanation

  Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte, Sachin Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar · 21 de julio de 2026

  Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc.…

• DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods

  Jens Frankenreiter · 20 de julio de 2026

  Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally relied on human coding of documents such as charters and bylaws, a process that is costly, difficult to scale, and often…

• FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality

  Beidi Luan, Rui Sun, Sinuo Wang, Yan Gu, Chao Li, Zhenliang Xiong, Jing Li, Zuo Bai · 15 de julio de 2026

  Deep research agents are increasingly used to produce long-form financial reports, yet large-scale evaluation remains bottlenecked by the need for human experts to define and execute high-quality rubrics. We address this problem by proposing a scalable pipeline for generating high-quality rubrics wi…

• Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy

  Rian Dolphin, Joe Dursun, Jarrett Blankenship, Katie Adams, Quinton Pike · 10 de julio de 2026

  Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a cat…

• Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach

  Nobuhiro Aikawa, Mitsuo Yoshida · 26 de junio de 2026

  While corporate narrative disclosures provide crucial information to capital markets, comprehensively evaluating their qualitative changes over time remains challenging. Narrative text is inherently multidimensional, meaning that an improvement in one textual dimension often occurs alongside changes…

• FinBalance: A Multi-Document Accounting Reconciliation Benchmark

  Sasank Tumpati, Devansh Agarwal, Ayush Kedia, Arjun Neekhra, Murari Mandal, Krishna Garg, Yash Sinha, Suman Gupta, Dhruv Kumar · 16 de junio de 2026

  Existing financial-NLP benchmarks mostly evaluate prepared artifacts such as filings, tables, or extracted values. Real accounting begins earlier: source documents must be reconciled into cited journal entries, aggregated into a balance sheet, and checked for contradictions. We introduce FinBalance,…

• Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling

  Yuhan Wang, Manqing Wang, Yixuan Lu, Zhaoyue Peng, Shengda Lin · 16 de junio de 2026

  Audit risk assessment increasingly benefits from combining heterogeneous evidence sources, yet existing approaches typically produce point predictions without quantifying how well different evidence streams agree. We propose UMAR (Uncertainty-Aware Multi-Agent Risk Assessment), a framework that empl…

• Limited Marginal Benefit of Reasoning-Heavy LLM Deployment in ESG Narrative Scoring: A 4-Model Consensus Study on Japanese Listed Firms

  Hiroyuki Kokubu · 15 de junio de 2026

  Automated scoring of ESG narrative disclosures with large language models (LLMs) is gaining traction, yet whether reasoning-heavy frontier models add value commensurate with their cost remains empirically unsettled. We evaluate this question on a corpus of ten Japanese listed firms across three rubr…

• IPO-Mine: A Toolkit and Dataset for Section-Structured Analysis of Long, Multimodal IPO Documents

  Michael Galarnyk, Siddharth Lohani, Vidhyakshaya Kannan, Sagnik Nandi, Aman Patel, Liqin Ye, Arnav Hiray, Rutwik Routu, Prasun Banerjee, Siddhartha Somani, Sudheer Chava · 28 de mayo de 2026

  An Initial Public Offering (IPO) filing is a document released when a private firm goes public, allowing individual (retail) investors to purchase its shares. These filings describe a firm's business, financials, and risks and are long, multimodal documents with narrative text and images. Despite th…

• What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema

  Mahdi Naser Moghadasi (BrightMind AI, Texas Tech University), Faezeh Ghaderi (University of Texas at Arlington) · 21 de mayo de 2026

  We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and …