Automation

AI-Powered (Finance) Scholarship ArXiv ID: ssrn-5060022 “View on arXiv” Authors: Unknown Abstract Keywords: Generative AI, Large Language Models (LLMs), Academic Research, Natural Language Processing, Automation, Technology Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on the conceptual process of using LLMs to generate academic papers, rather than presenting complex mathematical models or empirical backtesting results. flowchart TD A["Research Goal<br>Automate Academic Paper Generation"] --> B{"Methodology"} B --> C["Data/Input<br>LLM & Financial Datasets"] B --> D["Data/Input<br>Research Questions"] C --> E["Computational Process<br>LLM Content Generation"] D --> E E --> F["Key Findings<br>Successful Paper Automation"] E --> G["Key Findings<br>Validated Methodology"]

The Structure of Financial Equity Research Reports – Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4 ArXiv ID: 2407.18327 “View on arXiv” Authors: Unknown Abstract This research dissects financial equity research reports (ERRs) by mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions’ potential for automation. Answers were labeled “text-extractable” if the answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question consist of 48.2% text-extractable (suited to processing by large language models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that recent advances in language generation and information extraction enable the automation of approximately 80% of the statements in ERRs. Surprisingly, the models complement each other’s strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be made available online after peer review. ...

Advances in Financial Machine Learning: Lecture 2/10 (seminar slides) ArXiv ID: ssrn-3257415 “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, Data science, Automation, Technology Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The excerpt introduces concepts like high-dimensional spaces and non-linear relationships but is devoid of advanced formulas, focusing instead on conceptual discussions and examples. It lacks data, backtests, code, or specific implementation metrics, making it more of a high-level overview than an empirical or technical paper. flowchart TD Q["Research Goal: Applying ML to Finance"] --> D["Data: Financial Market Data"] D --> M["Methodology: ML Algorithms"] M --> C["Computational Process: Pattern Recognition"] C --> F["Outcome: Task Automation"] F --> O["Key Finding: Expert-Level Performance"]