North America 2024
AI for Finance Summit ’24
The 1st Workshop on LLMs and Generative AI for
Finance — ICAIF ’24
November 14th (Thu), 2024
3:00 - 17:30 EST
Brooklyn, NY
Check-in
Please note: This summit serves as a conduit between academic research and real-world deployment of LLMs in finance, creating a space where theory meets execution.
North America 2024
AI for Finance Summit ’24
The 1st Workshop on LLMs and Generative AI for Finance — ICAIF ’24
November 14th (Thu), 2024
3:00 - 17:30 EST
Brooklyn, NY
Check-in
Please note: This is a closed-door session.
No recording, no press, and conducted under the Chatham House Rule to encourage open and honest discussion among participants.
Panelists
Panelists
Naftali Cohen
Naftali Cohen
Schonfeld
Schonfeld
Andrew Chin
Andrew Chin
AllianceBernstein
AllianceBernstein
Yu Yu
Yu Yu
BlackRock
BlackRock
Ben Wellington
Two Sigma
Ben Wellington
Ben Wellington
Two Sigma
Two Sigma
Edward Tong
Edward Tong
Millennium
Millennium
Hojun Choi
Hojun Choi
LinqAlpha
LinqAlpha
Speakers
Speakers
Keynote
Prof. Valeri Nikolaev
Prof. Valeri Nikolaev
University of Chicago,
Booth School of Business
University of Chicago, Booth School of Business
Keynote
Prof. Andrew Lo
Prof. Andrew Lo
MIT, Sloan School of Management
MIT, Sloan School of Management
Keynote
Prof. Ralph Koijen
Prof. Ralph Koijen
University of Chicago,
Booth School of Business
University of Chicago, Booth School of Business
Opening Remarks
Prof. Yoon Kim
Prof. Yoon Kim
MIT, Electrical Engineering and
Computer Science
MIT, Electrical Engineering and Computer Science
Short talk
Prof. Sean Cao
Prof. Sean Cao
University of Maryland,
Robert H. Smith School of Business
University of Maryland,
Robert H. Smith School of Business
Short talk
Prof. Miao Liu
Prof. Miao Liu
Boston College, Carroll School of Management
Boston College, Carroll School of Management
Short talk
Armineh Nourbakhsh
Armineh Nourbakhsh
J.P. Morgan AI Research
Executive Director
J.P. Morgan AI Research Executive Director
Organizing Committee
Organizing Committee
Prof. Yoon Kim
MIT, Electrical Engineering
and Computer Science
Dr. Jacob
Chanyeol Choi
LinqAlpha
Dr. Yu Yu
BlackRock
Alex Kim
PhD Candidate,
University of Chicago
Booth School of Business
Dr. Georgios
Papaioannou
Qube Research & Technologies
David Kim
PhD Candidate,
MIT Sloan School of Management
Sangwon Yoon, Esq
Seoul National University
Prof. Jy-yong Sohn
Yonsei University,
Department of Applied Statistics
Prof. Elizabeth
Blankespoor
University of Washington,
Foster School of Business
Prof. Alejandro
Lopez Lira
University of Florida, Warrington College of Business
Prof. Eric So
MIT, Sloan School of Management
Prof. Valeri Nikolaev
University of Chicago, Booth School of Business
Prof. Andrew Lo
MIT, Sloan School of Management
The goal of the workshop is
to study how large language models (LLMs) and generative AI can be used for finance and accounting applications.
The goal of the workshop is
to study how large language models (LLMs) and generative AI can be used for finance and accounting applications.
About
the workshop
About the workshop
About the workshop
The rapid advancements in large language models (LLMs) and generative AI more broadly have significantly impacted the interpretation and utilization of unstructured financial data, enabling a wide range of applications in finance and accounting. While LLMs have unlocked new possibilities in this domain, NLP techniques more broadly remain vital for various applications such as financial analysis, case studies, price forecasting, portfolio optimization, and analyzing financial reports and alternative data. They are also crucial in financial risk modeling, such as credit assessment, bankruptcy detection, and M&A target prediction using news sentiment and topic detection.
However, LLMs and NLP techniques face challenges in the financial domain, including the limited context window of LLMs, the complexities of converting unstructured data into structured formats, hallucinations, and the need for interpretability in financial applications. This workshop aims to bridge the gap between technological advancements in computer science and the specific needs of finance and accounting. By focusing on methodologies such as fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and NLP, we aim to showcase how these techniques can improve the accuracy and relevance of insights derived from financial and accounting data. The ultimate goal is to foster interdisciplinary collaboration, enhance industry solutions, and contribute to academic research in finance and accounting.
The workshop will be held in conjunction with the ICAIF ’24 and is exclusively an in-person event.
The rapid advancements in large language models (LLMs) and generative AI more broadly have significantly impacted the interpretation and utilization of unstructured financial data, enabling a wide range of applications in finance and accounting. While LLMs have unlocked new possibilities in this domain, NLP techniques more broadly remain vital for various applications such as financial analysis, case studies, price forecasting, portfolio optimization, and analyzing financial reports and alternative data. They are also crucial in financial risk modeling, such as credit assessment, bankruptcy detection, and M&A target prediction using news sentiment and topic detection.
However, LLMs and NLP techniques face challenges in the financial domain, including the limited context window of LLMs, the complexities of converting unstructured data into structured formats, hallucinations, and the need for interpretability in financial applications. This workshop aims to bridge the gap between technological advancements in computer science and the specific needs of finance and accounting. By focusing on methodologies such as fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and NLP, we aim to showcase how these techniques can improve the accuracy and relevance of insights derived from financial and accounting data. The ultimate goal is to foster interdisciplinary collaboration, enhance industry solutions, and contribute to academic research in finance and accounting.
The workshop will be held in conjunction with the ICAIF ’24 and is exclusively an in-person event.
The rapid advancements in large language models (LLMs) and generative AI more broadly have significantly impacted the interpretation and utilization of unstructured financial data, enabling a wide range of applications in finance and accounting. While LLMs have unlocked new possibilities in this domain, NLP techniques more broadly remain vital for various applications such as financial analysis, case studies, price forecasting, portfolio optimization, and analyzing financial reports and alternative data. They are also crucial in financial risk modeling, such as credit assessment, bankruptcy detection, and M&A target prediction using news sentiment and topic detection.
However, LLMs and NLP techniques face challenges in the financial domain, including the limited context window of LLMs, the complexities of converting unstructured data into structured formats, hallucinations, and the need for interpretability in financial applications. This workshop aims to bridge the gap between technological advancements in computer science and the specific needs of finance and accounting. By focusing on methodologies such as fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and NLP, we aim to showcase how these techniques can improve the accuracy and relevance of insights derived from financial and accounting data. The ultimate goal is to foster interdisciplinary collaboration, enhance industry solutions, and contribute to academic research in finance and accounting.
The workshop will be held in conjunction with the ICAIF ’24 and is exclusively an in-person event.
Call for papers
Submit
We invite papers addressing domain-specific problems in finance and accounting, utilizing AI. While we encourage applications that involve the use of LLMs, we also invite papers that use generative AI technologies (broadly construed) to address problems in finance and accounting. Potential topics include but are not limited to:
Methodologies for Processing Unstructured Data
Techniques to construct structured data from unstructured financial data using LLMs and NLP.
Technical Challenges of LLMs and NLP
Addressing issues such as hallucination, interpretability, and the integration of NLP methods in financial applications.
Prompt Engineering
Optimizing LLM prompts to extract relevant financial insights.
Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Modeling
Applying LLMs and AI for credit assessment, price forecasting, bankruptcy detection, and other financial modeling scenarios.
Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.
We invite papers addressing domain-specific tasks in finance and accounting, utilizing techniques in LLMs and NLP. While the use of LLMs is recommended, submissions are not limited to LLMs and may include broader NLP methodologies. Potential topics include but are not limited to:
Call for papers
Submit
Methodologies for Processing Unstructured Data
Techniques to construct structured data from unstructured financial data using LLMs and NLP.
Technical Challenges of LLMs and NLP
Addressing issues such as hallucination, interpretability, and the integration of NLP methods in financial applications.
Prompt Engineering
Optimizing LLM prompts to extract relevant financial insights.
Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.
Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.
Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.
Submission
Guidelines
Submission Guidelines
Submission Guidelines
There is no length limit or strict format requirement. While formatting according to ACM guidelines is recommended (see below), we encourage submissions in an open format to allow the authors to focus on content with formatting restrictions. Submissions should be in PDF format and can be directly submitted as they are.
The workshop is non-archival and will not have official proceedings.
There is no length limit or strict format requirement. While formatting according to ACM guidelines is recommended (see below), we encourage submissions in an open format to allow the authors to focus on content with formatting restrictions. Submissions should be in PDF format and can be directly submitted as they are.
The workshop is non-archival and will not have official proceedings.
Recommended Formatting:
Recommended Formatting:
Additional Requirements
For each submission, at least one author must agree to serve as a reviewer and deliver their reviews on time.
The author list cannot be modified after the initial submission, except before printing.
Any changes after acceptance will be communicated directly with the authors.
Call for papers
We invite papers addressing domain-specific problems in finance and accounting, utilizing AI. While we encourage applications that involve the use of LLMs, we also invite papers that use generative AI technologies (broadly construed) to address problems in finance and accounting. Potential topics include but are not limited to:
Submit
Key Dates
Submission Deadline
October 16th, 2024 (Anywhere on Earth)
October 16th, 2024 (Anywhere on Earth)
Author Notification
October 30th, 2024
October 30th, 2024
Workshop
November 14th, 2024 (Thursday, 13:00 - 17:30 Eastern Time)
November 14th, 2024 (Thursday, 13:00 - 17:30 Eastern Time)
Submission Process
Submissions are to be made through the OpenReview platform (link to be updated: https://openreview.net/). Authors must submit the paper content (PDF document), title, author names, contact details, and a brief abstract electronically through the workshop’s submission site. At least one author of each accepted paper is required to attend the conference to present their work.
Review Process
This workshop will follow a single-blind review process, where authors’ identities are known to the reviewers, but reviewers’ identities are not disclosed to the authors. There will be no rebuttal period, and all submissions will be treated with strict confidentiality.
Accepted Papers
All accepted papers will be invited to participate in the poster session. Participants are required to print and bring their own posters to the event.
Detailed specifications on poster format and requirements will be provided by the organizers after acceptance.
Selected papers may also be given the opportunity for an oral presentation, subject to scheduling constraints.
Please note that the workshop is non-archival and will not have official proceedings. Only the author names and titles of accepted papers will appear on the website, with no disclosure of any content.
The workshop is non-archival and will not have official proceedings. Only the author names and titles of accepted papers will appear on the website, with no disclosure of any content.
Accepted Papers
Oral Presentation
Scaling Core Earnings Measurement with Large Language Models
Matthew Shaffer
Climate solutions, transition risk, and stock returns
Shirley Lu, Edward Riedl, George Serafeim, Simon Xu
Mental Models and Financial Forecasts
Francesca Bastaniello, Paul Decaire, Marius Guenzel
FinRobot: AI Agent for Equity Research and Valuation with Large Language Models
Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang
AlphaAgents: Large Language Model based Multi-Agents to Build Better Equity Portfolios
(In-person constrained)
Tianjiao Zhao, Jingrao Lyu, Harrison Garber, Stokes Jones, Stefano Pasquali, Dhagash Mehta
Generative AI and Asset Management
(In-person constrained)
Jinfei Sheng, Zheng Sun, Baozhong Yang, Alan Zhang
Poster Presentation
Responsible Evaluation by Design: Measuring AI’s Total Impact in Financial Applications
Jayeeta Putatunda, Daniela Muhaj
Topic Labeling Using Large Language Models
Olga Bogachek
LLMs for the categorisation of SME bank transactions
Brandi Jess, Pietro Alessandro Aluffi, Marya Bazzi, Matt Arderne, Daniel Rodrigues, Kate Kennedy, Martin Lotz
Beyond the Fundamentals: How Media-Driven Narratives Influence Cross-Border Capital Flows
Isha Agarwal, Wentong Chen, Eswar Prasad
AI Democratization, Return Predictability, and Trading Inequality
Anne Chang, Xi Dong, Xiumin Martin, Changyun Zhou
Quantformer: from attention to profit with a quantitative transformer trading strategy
Zhaofeng Zhang, Banghao Chen, Shengxin Zhu, Nicolas Langrené
Enhancing Portfolio Rebalancing Timing Using GPT: A Macroeconomic Indicator Approach
Hyeong jin son, Woo Been Back, Kyeong Soo Shin
Evaluating Financial Sentiment Analysis with Annotators’ Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy
A M Muntasir Rahman, Ajim Uddin, Guiling Wang
Combining Financial Data and News Articles for Stock Price Movement Prediction by Prompting Large Language Models
Ali Elahi, Fatameh Taghvaei
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
Suyeol Yun
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike
Leveraging NLP, LLM’s, and Knowledge Graphs for Investor Relations
Odemuno Ogelohwohor, Boyi Qian, Saloni Parekh, Pavitra Kadiyala, Kedi Xu, Cehong Wang, Christopher Policastro, Michael Hunger, Abhay Navale
A Conformal Inference-Based Approach to Credit Score Modeling with Large Language Models
Yuxi Chen, Suwei Ma, Tony Dear
Voting Rationale
Irene Yi, Roni Michaely, Silvina Rubio
AI Exposure without Labor Data: The Expected Impact of AI in Forward-Looking Firm Communications
Jiacheng Liu
Quantifying Qualitative Insights: Leveraging LLMs to Market Predict
Hoyoung Lee, Youngsoo Choi, Yuhee Kwon
The Value of Information from Sell-side Analysts
Linying Lv
Large Language Model Evaluation on Financial Benchmarks
Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Yada Zhu
Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements
G. Nathan Dong
Simulating the Survey of Professional Forecasters
Sophia Kazinnik, Anne Lundgaard Hansen
Under Pressure: Strategic Signaling in Bank Earnings Calls
Sophia Kazinnik, Anne Lundgaard Hansen, Thomas R. Cook, Peter McAdam
Transmission Bias in Financial News
Khaled Obaid, Kuntara Pukthuanthong
Using Large Language Models for Financial Advice
Christian Fieberg, Lars Hornuf, Maximilian Meiler, David Streich
Are Memes a Sideshow: Evidence from WallStreetBets
Bill Qiao, Dexin Zhou
Producing AI Innovation and Its Value Implications
Ambrus Kecskes, Phuong-Anh Nguyen, Roni Michaely, Ali Ahmadi
How Much Should We Trust Large Language Model-Based Measures For Accounting And Finance Research?
Minji Yoo
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
Shuaiyu Chen, Clifton Green, Huseyin Gulen, Dexin Zhou
Wisdom or Whims? Decoding Investor Trading Strategies with Large Language Models
Shuaiyu Chen, Lin Peng, Dexin Zhou
Efficiently computing volatility surfaces with minimal arbitrage violations using GANs
Andrew S Na, Meixin Zhang, Justin Wan
An Anatomy of Subjective Expectation
Barry Ke
Quantifying Uncertainty: A New Era of Measurement through Large Language Models
Jessica Gentner, Francesco Audrino, Simon Stalder
Small Language Models for the Democratization of Financial Literacy: Challenges and Opportunities
Tagore Rao Kosireddy, Jeffrey David Wall, Evan Lucas, Xin Li, Jun Dai
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges
Aleksandr Simonyan
Climate AI for Corporate Decarbonization Metrics Extraction
Aditya Dave, Mengchen Zhu, Dapeng Hu, Sachin Tiwari
Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?
Bruno de Melo
Forecasting Option Returns with News
Jie Cao, Bing Han, Gang Li, Ruijing Yang, Xintong Zhan
Fed Transparency and Policy Expectation Errors: A Text Analysis Approach
Eric Fischer, Rebecca McCaughrin, Saketh Prazad, Mark Vandergon
Prudential Regulation Embedding Transformer (PRET) a domain-adapted model for prudential supervision
Dragos Gorduza, Adam Muhtar
CovenantAI - New Insights into Covenant Violations
(In-person constrained)
Anthony Saunders, Sascha Steffen, Paulina M Verhoff
Counterfactual Analysis via Large Language Models
Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang
Visual Information and AI Divide: Evidence from Corporate Executive Presentations
Sean Cao, Yichen Cheng, Meng Wang, Baozhong Yang, Yusen Xia
One Hundred and Thirty Years of Corporate Responsibility
(In-person constrained)
Joel F. Houston, Sehoon Kim, Boyuan Li
Central Bank Digital Currencies (CBDC) Sentiment Score Analysis of News using Chat GPT-4.0 from January 2018 to June 2024
(In-person constrained)
Renata Ayumi Hokama Penteado Alves
Schedule
11/14 (Thu)
The 1st Workshop on LLMs and Generative AI for Finance
Opening Remarks
1:00 PM - 1:05 PM
Prof. Yoon Kim
MIT, Electrical Engineering and Computer Science
Keynote
1:05 PM - 1:40 PM
Prof. Valeri Nikolaev
University of Chicago,Booth School of Business
“Future of Generative AI in Financial Markets”
Short Talk
1:40 PM - 1:55 PM
Prof. Sean Cao
University of Maryland,Robert H. Smith School of Business
“Applied AI in Accounting and Finance: Industry and Academic Applications”
Panel
1:55 PM - 2:30 PM
“Practical Applications of Large Language Models in Finance and Accounting Workflows”
Coffee Break
2:30 PM - 2:40 PM
Keynote
2:40 PM - 3:15 PM
Andrew Lo
MIT, Sloan School of Management
“Generative AI and the Rise of Quantamental Investing”
Short Talk
3:15 PM - 3:30 PM
Armineh Nourbakhsh
J.P. Morgan AI Research Executive Director
“Document AI Workflows and the Challenge of Grounded Generation”
Oral session 1
3:30 PM - 4:00 PM
Matthew Shaffer
“Scaling Core Earnings Measurement with Large Language Models”
Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang
“FinRobot: AI Agent for Equity Research and Valuation with Large Language Models”
Coffee Break
4:00 PM - 4:10 PM
Keynote
4:10 PM - 4:45 PM
Ralph Koijen
University of Chicago, Booth School of Business
“Asset Embeddings”
Short Talk
4:45 PM - 5:00 PM
Miao Liu
Boston College, Carroll School of Management
“Executives vs. Chatbots: Unmasking Insights through Human-AI Differences in Earnings Conference Q&A”
Oral session 2
5:00 PM - 5:30 PM
Francesca Bastaniello, Paul Decaire, Marius Guenzel
“Mental Models and Financial Forecasts”
Shirley Lu, Edward Riedl, George Serafeim, Simon Xu
“Climate Solutions, Transition Risk, and Stock Returns”
Networking &Poster session
5:30 PM -
Organized by Members of the Following Institutions
Sponsored Exclusively by Qube Research & Technologies
Contact
For further details and submission guidelines, please contact the workshop organizers at
For further details and submission guidelines, please contact the workshop organizers at
Accepted Papers
Oral Presentation
Scaling Core Earnings Measurement with Large Language Models
Matthew Shaffer
Climate solutions, transition risk, and stock returns
Shirley Lu, Edward Riedl, George Serafeim, Simon Xu
Mental Models and Financial Forecasts
Francesca Bastaniello, Paul Decaire, Marius Guenzel
FinRobot: AI Agent for Equity Research and Valuation with Large Language Models
Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang
AlphaAgents: Large Language Model based Multi-Agents to Build Better Equity Portfolios
(In-person constrained)
Tianjiao Zhao, Jingrao Lyu, Harrison Garber, Stokes Jones, Stefano Pasquali, Dhagash Mehta
Generative AI and Asset Management
(In-person constrained)
Jinfei Sheng, Zheng Sun, Baozhong Yang, Alan Zhang
Poster Presentation
Responsible Evaluation by Design: Measuring AI’s Total Impact in Financial Applications
Jayeeta Putatunda, Daniela Muhaj
Topic Labeling Using Large Language Models
Olga Bogachek
LLMs for the categorisation of SME bank transactions
Brandi Jess, Pietro Alessandro Aluffi, Marya Bazzi, Matt Arderne, Daniel Rodrigues, Kate Kennedy, Martin Lotz
Beyond the Fundamentals: How Media-Driven Narratives Influence Cross-Border Capital Flows
Isha Agarwal, Wentong Chen, Eswar Prasad
AI Democratization, Return Predictability, and Trading Inequality
Anne Chang, Xi Dong, Xiumin Martin, Changyun Zhou
Quantformer: from attention to profit with a quantitative transformer trading strategy
Zhaofeng Zhang, Banghao Chen, Shengxin Zhu, Nicolas Langrené
Enhancing Portfolio Rebalancing Timing Using GPT: A Macroeconomic Indicator Approach
Hyeong jin son, Woo Been Back, Kyeong Soo Shin
Evaluating Financial Sentiment Analysis with Annotators’ Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy
A M Muntasir Rahman, Ajim Uddin, Guiling Wang
Combining Financial Data and News Articles for Stock Price Movement Prediction by Prompting Large Language Models
Ali Elahi, Fatameh Taghvaei
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
Suyeol Yun
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike
Leveraging NLP, LLM’s, and Knowledge Graphs for Investor Relations
Odemuno Ogelohwohor, Boyi Qian, Saloni Parekh, Pavitra Kadiyala, Kedi Xu, Cehong Wang, Christopher Policastro, Michael Hunger, Abhay Navale
A Conformal Inference-Based Approach to Credit Score Modeling with Large Language Models
Yuxi Chen, Suwei Ma, Tony Dear
Voting Rationale
Irene Yi, Roni Michaely, Silvina Rubio
AI Exposure without Labor Data: The Expected Impact of AI in Forward-Looking Firm Communications
Jiacheng Liu
Quantifying Qualitative Insights: Leveraging LLMs to Market Predict
Hoyoung Lee, Youngsoo Choi, Yuhee Kwon
The Value of Information from Sell-side Analysts
Linying Lv
Large Language Model Evaluation on Financial Benchmarks
Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Yada Zhu
Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements
G. Nathan Dong
Simulating the Survey of Professional Forecasters
Sophia Kazinnik, Anne Lundgaard Hansen
Under Pressure: Strategic Signaling in Bank Earnings Calls
Sophia Kazinnik, Anne Lundgaard Hansen, Thomas R. Cook, Peter McAdam
Transmission Bias in Financial News
Khaled Obaid, Kuntara Pukthuanthong
Using Large Language Models for Financial Advice
Christian Fieberg, Lars Hornuf, Maximilian Meiler, David Streich
Are Memes a Sideshow: Evidence from WallStreetBets
Bill Qiao, Dexin Zhou
Producing AI Innovation and Its Value Implications
Ambrus Kecskes, Phuong-Anh Nguyen, Roni Michaely, Ali Ahmadi
How Much Should We Trust Large Language Model-Based Measures For Accounting And Finance Research?
Minji Yoo
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
Shuaiyu Chen, Clifton Green, Huseyin Gulen, Dexin Zhou
Wisdom or Whims? Decoding Investor Trading Strategies with Large Language Models
Shuaiyu Chen, Lin Peng, Dexin Zhou
Efficiently computing volatility surfaces with minimal arbitrage violations using GANs
Andrew S Na, Meixin Zhang, Justin Wan
An Anatomy of Subjective Expectation
Barry Ke
Quantifying Uncertainty: A New Era of Measurement through Large Language Models
Jessica Gentner, Francesco Audrino, Simon Stalder
Small Language Models for the Democratization of Financial Literacy: Challenges and Opportunities
Tagore Rao Kosireddy, Jeffrey David Wall, Evan Lucas, Xin Li, Jun Dai
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges
Aleksandr Simonyan
Climate AI for Corporate Decarbonization Metrics Extraction
Aditya Dave, Mengchen Zhu, Dapeng Hu, Sachin Tiwari
Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?
Bruno de Melo
Forecasting Option Returns with News
Jie Cao, Bing Han, Gang Li, Ruijing Yang, Xintong Zhan
Fed Transparency and Policy Expectation Errors: A Text Analysis Approach
Eric Fischer, Rebecca McCaughrin, Saketh Prazad, Mark Vandergon
Prudential Regulation Embedding Transformer (PRET) a domain-adapted model for prudential supervision
Dragos Gorduza, Adam Muhtar
CovenantAI - New Insights into Covenant Violations
(In-person constrained)
Anthony Saunders, Sascha Steffen, Paulina M Verhoff
Counterfactual Analysis via Large Language Models
Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang
Visual Information and AI Divide: Evidence from Corporate Executive Presentations
Sean Cao, Yichen Cheng, Meng Wang, Baozhong Yang, Yusen Xia
One Hundred and Thirty Years of Corporate Responsibility
(In-person constrained)
Joel F. Houston, Sehoon Kim, Boyuan Li
Central Bank Digital Currencies (CBDC) Sentiment Score Analysis of News using Chat GPT-4.0 from January 2018 to June 2024
(In-person constrained)
Renata Ayumi Hokama Penteado Alves
Schedule
11/14 (Thu)
The 1st Workshop on LLMs and Generative AI for Finance
Opening Remarks
1:00 PM - 1:05 PM
Prof. Yoon Kim
MIT, Electrical Engineering and Computer Science
Keynote
1:05 PM - 1:40 PM
Prof. Valeri Nikolaev
University of Chicago, Booth School of Business
“Future of Generative AI in Financial Markets”
Short Talk
1:40 PM - 1:55 PM
Prof. Sean Cao
University of Maryland, Robert H. Smith School of Business
“Applied AI in Accounting and Finance: Industry and Academic Applications”
Panel
1:55 PM - 2:30 PM
“Practical Applications of Large Language Models in Finance and Accounting Workflows”
Coffee Break
2:30 PM - 2:40 PM
Keynote
2:40 PM - 3:15 PM
Andrew Lo
MIT, Sloan School of Management
“Generative AI and the Rise of Quantamental Investing”
Short Talk
3:15 PM - 3:30 PM
Armineh Nourbakhsh
J.P. Morgan AI Research Executive Director
“Document AI Workflows and the Challenge of Grounded Generation”
Oral session 1
3:30 PM - 4:00 PM
Matthew Shaffer
“Scaling Core Earnings Measurement with Large Language Models”
Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang
“FinRobot: AI Agent for Equity Research and Valuation with Large Language Models”
Coffee Break
4:00 PM - 4:10 PM
Keynote
4:10 PM - 4:45 PM
Ralph Koijen
University of Chicago, Booth School of Business
“Asset Embeddings”
Short Talk
4:45 PM - 5:00 PM
Miao Liu
Boston College, Carroll School of Management
“Executives vs. Chatbots: Unmasking Insights through Human-AI Differences in Earnings Conference Q&A”
Oral session 2
5:00 PM - 5:30 PM
Francesca Bastaniello, Paul Decaire, Marius Guenzel
“Mental Models and Financial Forecasts”
Shirley Lu, Edward Riedl, George Serafeim, Simon Xu
“Climate Solutions, Transition Risk, and Stock Returns”
Networking &
Poster session
5:30 PM -
Organized by Members of the Following Institutions
Contact
For further details and submission guidelines,
please contact the workshop organizers at
North America 2024
AI for Finance Summit
The 1st Workshop on LLMs
and Generative AI for Finance
— ICAIF ’24
November 14th (Thu), 2024
3:00 - 17:30 EST
Brooklyn, NY
Check-in
Please note: This is a closed-door session.
No recording, no press, and conducted under the Chatham House Rule to encourage open and honest discussion among participants.
North America 2024
AI for Finance Summit
The 1st Workshop on LLMs
and Generative AI for Finance
— ICAIF ’24
November 14th (Thu), 2024
3:00 - 17:30 EST
Brooklyn, NY
Check-in
Please note: This is a closed-door session.
No recording, no press, and conducted under the Chatham House Rule to encourage open and honest discussion among participants.