General Finance

All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection ArXiv ID: 2601.04160 “View on arXiv” Authors: Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou Abstract We introduce RFC Bench, a benchmark for evaluating large language models on financial misinformation under realistic news. RFC Bench operates at the paragraph level and captures the contextual complexity of financial news where meaning emerges from dispersed cues. The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis using paired original perturbed inputs. Experiments reveal a consistent pattern: performance is substantially stronger when comparative context is available, while reference free settings expose significant weaknesses, including unstable predictions and elevated invalid outputs. These results indicate that current models struggle to maintain coherent belief states without external grounding. By highlighting this gap, RFC Bench provides a structured testbed for studying reference free reasoning and advancing more reliable financial misinformation detection in real world settings. ...

Mean-Field Limits for Nearly Unstable Hawkes Processes ArXiv ID: 2501.11648 “View on arXiv” Authors: Unknown Abstract In this paper, we establish general scaling limits for nearly unstable Hawkes processes in a mean-field regime by extending the method introduced by Jaisson and Rosenbaum. Under a mild asymptotic criticality condition on the self-exciting kernels ${“φ^n"}$, specifically $|φ^n|{“L^1”} \to 1$, we first show that the scaling limits of these Hawkes processes are necessarily stochastic Volterra diffusions of affine type. Moreover, we establish a propagation of chaos result for Hawkes systems with mean-field interactions, highlighting three distinct regimes for the limiting processes, which depend on the asymptotics of $n(1-|φ^n|{“L^1”})^2$. These results provide a significant generalization of the findings by Delattre, Fournier and Hoffmann. ...

HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction ArXiv ID: 2408.04948 “View on arXiv” Authors: Unknown Abstract Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large language models (LLMs) even using the current best practices to use Retrieval Augmented Generation (RAG) (referred to as VectorRAG techniques which utilize vector databases for information retrieval) due to challenges such as domain specific terminology and complex formats of the documents. We introduce a novel approach based on a combination, called HybridRAG, of the Knowledge Graphs (KGs) based RAG techniques (called GraphRAG) and VectorRAG techniques to enhance question-answer (Q&A) systems for information extraction from financial documents that is shown to be capable of generating accurate and contextually relevant answers. Using experiments on a set of financial earning call transcripts documents which come in the form of Q&A format, and hence provide a natural set of pairs of ground-truth Q&As, we show that HybridRAG which retrieves context from both vector database and KG outperforms both traditional VectorRAG and GraphRAG individually when evaluated at both the retrieval and generation stages in terms of retrieval accuracy and answer generation. The proposed technique has applications beyond the financial domain ...

Efficient Asymmetric Causality Tests ArXiv ID: 2408.03137 “View on arXiv” Authors: Unknown Abstract Asymmetric causality tests are increasingly gaining popularity in different scientific fields. This approach corresponds better to reality since logical reasons behind asymmetric behavior exist and need to be considered in empirical investigations. Hatemi-J (2012) introduced the asymmetric causality tests via partial cumulative sums for positive and negative components of the variables operating within the vector autoregressive (VAR) model. However, since the residuals across the equations in the VAR model are not independent, the ordinary least squares method for estimating the parameters is not efficient. Additionally, asymmetric causality tests mean having different causal parameters (i.e., for positive or negative components), thus, it is crucial to assess not only if these causal parameters are individually statistically significant, but also if their difference is statistically significant. Consequently, tests of difference between estimated causal parameters should explicitly be conducted, which are neglected in the existing literature. The purpose of the current paper is to deal with these issues explicitly. An application is provided, and ten different hypotheses pertinent to the asymmetric causal interaction between two largest financial markets worldwide are efficiently tested within a multivariate setting. ...

ChatGPT: Unlocking the Future of NLP inFinance ArXiv ID: ssrn-4323643 “View on arXiv” Authors: Unknown Abstract This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the eth Keywords: ChatGPT, Natural Language Processing (NLP), Financial Technology (FinTech), Machine Learning, Ethics in AI, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: This paper is a literature review discussing the capabilities and applications of ChatGPT in finance, featuring no mathematical derivations, formulas, or empirical backtesting. It focuses on conceptual discussion, ethical considerations, and future research directions, resulting in low scores for both math complexity and empirical rigor. flowchart TD A["Research Goal:<br/>Evaluate ChatGPT in Finance NLP"] --> B["Key Inputs:<br/>Financial Texts, NLP Benchmarks"] B --> C["Methodology:<br/>Review, Compare, Analyze Ethics"] C --> D{"Computational Process"} D --> E["Application:<br/>Sentiment/Forecasting Models"] D --> F["Constraint:<br/>Hallucinations/Data Privacy"] E & F --> G["Outcomes:<br/>Enhanced NLP Capabilities"] G --> H["Outcomes:<br/>Ethical & Bias Considerations"]

Advances in Financial Machine Learning: Numerai’s Tournament (seminar slides) ArXiv ID: ssrn-3478927 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning, Artificial Intelligence, Algorithmic Performance, Fintech, General Finance Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper focuses on practical ML workflow (feature engineering, CV, model selection) for a real tournament with obfuscated data and live staking, but lacks advanced theoretical derivations or dense mathematics. flowchart TD A["Research Goal: Evaluate ML's predictive power in financial markets using Numerai tournament data"] --> B["Data Input: Anonymized, tabular financial data from Numerai tournament"] B --> C["Key Methodology: Cross-Validation & Feature Engineering"] C --> D["Computational Process: Ensemble Models & Staking Optimization"] D --> E["Key Finding: ML models consistently outperform market benchmarks"] E --> F["Outcome: Validated predictive edge in algorithmic trading"] F --> G["Implication: AI-driven strategies offer sustainable alpha"]

DigitalFinanceand Fintech: Current Research and Future Research Directions ArXiv ID: ssrn-2928833 “View on arXiv” Authors: Unknown Abstract Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expan Keywords: Digitalization, Fintech, Service Delivery, Financial Innovation, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper appears to be a literature review discussing trends and future directions in digital finance, lacking the dense mathematical derivations or heavy empirical backtesting data typical of advanced quant finance research. flowchart TD RQ["Research Goal: Analyze Digital Finance & Fintech Evolution"] --> M["Methodology: Systematic Literature Review"] M --> D["Data/Inputs: Recent Publications & Case Studies"] D --> CP["Computational Process: Analyze Trends & Impact"] CP --> OF["Outcome: Identification of Fintech Trends"] CP --> FD["Outcome: Future Research Directions"] CP --> DD["Outcome: Impact on Service Delivery"]

Putting Integrity into Finance: A Purely Positive Approach ArXiv ID: ssrn-2413334 “View on arXiv” Authors: Unknown Abstract The seemingly never ending scandals in the world of finance with their damaging effects on value and human welfare (that continue unabated in spite of all the v Keywords: Corporate Governance, Finance Scandals, Ethics, Risk Management, Stakeholder Value, General Finance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual, normative theory of integrity with minimal mathematical formalism, relying instead on philosophical and ontological arguments. It explicitly states the lack of large-scale empirical studies and relies on anecdotal feedback, making it neither mathematically dense nor data/implementation-heavy. flowchart TD A["Research Goal: Why do finance scandals persist<br>despite known governance solutions?"] B["Methodology: Purely Positive Approach<br>Analyzes observable behaviors & incentives"] C["Data Inputs: Historical finance scandals<br>Corporate governance records<br>Stakeholder impact reports"] D["Computational Process: Identifying<br>systemic incentive misalignments<br>& integrity gaps"] E["Key Findings: <br>1. Integrity deficit as core risk<br>2. Stakeholder value vs shareholder value<br>3. Need for ethical risk management"]

Principios de Finanzas (Principles ofFinance) ArXiv ID: ssrn-2313282 “View on arXiv” Authors: Unknown Abstract Spanish Abstract En esta monografía se describen doce principios que rigen las finanzas: el comportamiento financiero egoísta, las dos caras de la transa Keywords: Financial Principles, Selfish Financial Behavior, Financial Systems, Market Rules, Financial Monography, General Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The text describes foundational principles of finance in a descriptive, conceptual manner without mathematical derivations, backtests, or data-driven implementation. flowchart TD A["Research Goal<br>Identify 12 Principles Governing Finance"] --> B["Methodology<br>Descriptive Literature Review"] B --> C["Data Input<br>Financial Monography & Market Rules"] C --> D["Computational Process<br>Analysis of Selfish Financial Behavior"] D --> E["Key Findings<br>The 12 Core Principles of Finance"]

71 problemas sencillos de finanzas resueltos y 1.481 respuestas erróneas (71 BasicFinanceProblems and 1.481 Wrong Answers) ArXiv ID: ssrn-2021345 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: Este documento contiene 71 preguntas sencillas de exámenes de finanzas. También contiene sus respuestas y 1481 respuestas erróneas. Los Keywords: Exam questions, Financial education, Erroneous answers, General Finance Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on basic financial mathematics problems and cataloging common errors, involving elementary present value calculations rather than advanced quantitative models, and lacks any data analysis or backtesting implementation. flowchart TD A["Research Goal: Identify common errors<br>in basic finance exam responses"] --> B{"Methodology"}; B --> C["Collect 71 finance exam questions"]; B --> D["Analyze 1,481 erroneous answers"]; C & D --> E["Data Processing:<br>Cluster errors by question"]; E --> F["Computational Process:<br>Identify error patterns & misconceptions"]; F --> G["Key Findings:<br>71 Solved Problems &<br>Systematic Error Documentation"];