Financial Modeling

AI-Powered (Finance) Scholarship ArXiv ID: ssrn-5103553 “View on arXiv” Authors: Unknown Abstract This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process’ efficacy by Keywords: Generative AI, Large Language Models (LLMs), Automated Research, Financial Modeling, NLP, Technology Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper focuses on the process of using LLMs to generate academic content, lacking advanced mathematical derivations, while showing minimal evidence of backtesting or implementation-heavy data analysis. flowchart TD A["Research Goal<br>Automate Finance Paper Generation"] --> B["Inputs<br>Financial Data + LLM Prompts"] B --> C{"Methodology<br>Multi-Step Chain-of-Thought"} C --> D["Computational Process<br>LLM Synthesis & Modeling"] D --> E{"Evaluation<br>Human Expert Review"} E --> F["Outcomes<br>High-Quality Finance Papers"] E --> G["Outcomes<br>Validation of LLM Efficacy"] F --> H["Final Result<br>AI-Powered Scholarship Pipeline"] G --> H

Six Levels of Privacy: A Framework for Financial Synthetic Data ArXiv ID: 2403.14724 “View on arXiv” Authors: Unknown Abstract Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well. Such data may arise from client information, business information, or other proprietary sources that must be protected. Even though the process by which Synthetic Data is generated serves to obscure the original data to some degree, the extent to which privacy is preserved is hard to assess. Accordingly, we introduce a hierarchy of levels'' of privacy that are useful for categorizing Synthetic Data generation methods and the progressively improved protections they offer. While the six levels were devised in the context of financial applications, they may also be appropriate for other industries as well. Our paper includes: A brief overview of Financial Synthetic Data, how it can be used, how its value can be assessed, privacy risks, and privacy attacks. We close with details of the Six Levels’’ that include defenses against those attacks. ...

Deep Learning and Financial Stability ArXiv ID: ssrn-3723132 “View on arXiv” Authors: Unknown Abstract The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation. flowchart TD A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"] B --> C["Computational Processes"] C --> D["Key Findings & Outcomes"] B --> B1["Multi-Asset Data"] B --> B2["NLP on Financial Text"] B --> B3["Alternative Data Sources"] C --> C1["Deep Learning Models"] C --> C2["Financial Stability Metrics"] C --> C3["Risk Assessment Algorithms"] D --> D1["Enhanced Risk Prediction"] D --> D2["Systemic Stability Insights"] D --> D3["Fintech Innovation Pathways"] style A fill:#e1f5fe style D fill:#e8f5e8

Traditional vs. BehavioralFinance ArXiv ID: ssrn-1596888 “View on arXiv” Authors: Unknown Abstract The traditional finance researcher sees financial settings populated not by the error-prone and emotional Homo sapiens, but by the awesome Homo economicus. The Keywords: Homo economicus, behavioral finance, rational expectations, financial modeling, psychology, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual discussion comparing traditional vs. behavioral finance paradigms without presenting mathematical models or empirical backtesting data. flowchart TD A["Research Question:<br/>Traditional vs. Behavioral Finance"] --> B{"Methodology"} B --> C["Key Input:<br/>Homo Economicus"] B --> D["Key Input:<br/>Homo Sapiens"] C --> E["Computational Model:<br/>Rational Expectations"] D --> F["Computational Model:<br/>Psychology & Emotions"] E --> G["Outcome:<br/>Efficient Markets"] F --> H["Outcome:<br/>Multi-Asset Anomalies"]

Review of Discrete and Continuous Processes inFinance: Theory and Applications ArXiv ID: ssrn-1373102 “View on arXiv” Authors: Unknown Abstract We review the main processes used to model financial variables. We emphasize the parallel between discrete-time processes, mainly used by econometricians for ri Keywords: Financial Modeling, Stochastic Processes, Time Series Econometrics, Discrete-time Processes, Econometrics Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper is dense with advanced mathematics like stochastic calculus, PDEs, and detailed derivations of processes (e.g., Ornstein-Uhlenbeck, fractional Brownian motion). However, it lacks backtesting, code examples beyond mention, or empirical datasets, focusing instead on theoretical review and intuition. flowchart TD A["Research Goal:\nReview & Compare Discrete vs. Continuous\nFinancial Processes"] --> B{"Methodology"} B --> C["Literature Review"] B --> D["Theoretical Analysis"] C --> E["Data/Inputs:\nEconometric Theory\nFinancial Models\nStochastic Processes"] D --> E E --> F["Computational Process:\nParallel Comparison of\nDiscrete-time vs. Continuous-time\nModeling Frameworks"] F --> G["Key Findings:\n1. Discrete-time: Preferred for Econometrics\n2. Continuous-time: Preferred for Derivatives\n3. Bridging the gap improves forecasting"]