The Smith AI Initiative for Capital Market Research

View Annual Update Slides and Reports

Discussion at the Securities and Exchange Commission (SEC): Financial Market Regulation Conference.

Talks at SGA Investment Company board meetings (including academic colleague Richard Sloan).

AI and Alternative-data research for Disclosure, Investment, and Corporate Finance 

Cao, S., Jiang, W., Wang, Junbo and Yang, B. From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , accepted at Journal of Financial Economics

• American Association of Individual Investors (AAII) Best paper award winner at 2022 Midwest Finance Association, Investments and Asset Pricing, Best paper award winner at 2022 Annual Conference in Digital Economics, ACDE
• Presented at the NBER Economics of AI Conference, the Stanford Engineering AI and Big Data in Finance Research Forum (ABFR) webinar, AFA, Alliance Bernstein, Bank of Japan, Baruch, Case Western, CICF, CFEA, CFRC, CFTRC, CKGSB, Copenhagen Business School, CUHK-Shenzhen, Econometric Society China Meetings, ESSEC, FARS, Fordham, Frankfurt School of Finance and Management, Fintech: Innovation, Inclusion and Risks Conference, HARC, ITAM, Louisiana State University, MIT Sloan, National University of Singapore, Northern Illinois University, Stockholm School of Economics AI conference, Texas A&M, Tsinghua PBC, University of Cambridge, University of Florida Machine learning Conference, University of Mississippi, University of New Orleans, the Annual Conference on Digital Economics, the Quantitative Work Alliance at Boston (QWAFAFEW), the 28th Finance Forum at the Nova School of Business and Economics, NEOMA, the Midwest Finance Annual Conference, the Northern Finance Association conference, Penn State Empirical Accounting Conference, 3rd Shanghai Financial Forefront Symposium, and the UT Dallas Finance Conference.

Cao, S, Gong, G. Shi, H (Former PhD student), Kim, Y and Wang, A. Site Visits and Corporate Investment Efficiency ,accepted by Management Science

• Presented at Baruch College, Fudan University, Georgia State University, Nanjing University, New York University, Peking University, Shanghai University of Finance and Economics, University of Connecticut, and Tsinghua University

Cao, S, Jiang W., Lei, L., and Zhou, Q. Applied AI for finance and accounting: Alternative data  and opportunities , Forthcoming by Pacific-Basin Finance Journal

• This literature review on AI studies serves as the lead article for the special issue titled "Artificial Intelligence and Machine Learning in Corporate Finance." It is an abridged version derived from the forthcoming textbook titled "Analytics for Finance and Accounting: Data Structures and Applied AI." For more detailed data structure and applied AI use case, please kindly refer to the textbook .

Cao, S, Jiang, W., Yang, B. and Zhang, L. (Former PhD Student). 2023, How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI , Review of Financial Studies, 36 (9),3603-3642

• Accepted by NBER-Big data Conference in 2020
• Best paper award winner at CAPANA Research Conference
• Presented at Columbia, ECB, EDHEC, Emory, Georgia State, Harvard, University of Hong Kong, London Business School, Maryland, Michigan, Michigan State, Peking University, Q-group, the Pacific Center for Asset Management, Renmin University, Stockholm Business School, Toronto, Utah, Washington, the NBER Economics of Artificial Intelligence Conference, the NBER Big Data and Securities Markets Conference, AFA 2022, the SOAR Symposium at Singapore Management University, the Third Bergen FinTech Conference at the NHH Norwegian School of Economics, Machine Learning and Business Conference at University of Miami, RCFS Winter Conference 2021, 11th Financial Markets and Corporate Governance Conference, the China FinTech Research Conference 2021, the Adam Smith Workshop 2021, the Conference on Financial Innovation at Stevens Institute of Technology, FIRS 2021, the Cambridge Alternative Finance Sixth Annual Conference, CAPANA Research Conference 2021, CICF 2021, and NFA 2021

Campbell, John, Cao, S., Chang, H.S. and Chiorean, R. 2023, The implications of firms' derivative usage on the frequency and usefulness of management earnings forecasts , Contemporary Accounting Research,40 (4), 2409 -2445

• Presented at the SMU-NCCU Joint Accounting Conference, Singapore Management University (SMU) Accounting Symposium, European Accounting Association (EAA) Annual Meeting, Southeast Summer Accounting Research Conference (SESARC) at the University of Georgia, and American Accounting Association (AAA) Annual Meeting, Georgia State University, Lehigh University, and Tsinghua University.

Cao, S., Li, Y.H. and Ma, G. 2022, Labor Market Benefit of Disaggregated Disclosure , Contemporary Accounting Research,39(3),1726-1757

• Presented at Chinese University of Hong Kong, Florida Atlantic University, George State University, Hong Kong Baptist University, Hong Kong University of Science and Technology, Shanghai Lixin University of Accounting and Finance, Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University, University of Alberta, and University of North Texas

Cao, S, Fang, Vivian. and Lei, L. 2021. Negative Peer Disclosure Journal of Financial Economics, 140 (3), 815-837 .

• Presented at Tsinghua University, the University of Houston, the US Securities and Exchange Commission, and the Applied Econ Workshop Series at the University of Minnesota, the 2020 MFA meeting, the 2020 FARS Meeting, the 2020 CAFR Fintech Research Workshop at the ASSA Meeting, the 3rd Conference on Intelligent Information Retrieval in Accounting and Finance, the 2019 UVA Darden Accounting Mini-Conference, and the 2019 Stanford Accounting Summer Camp

Cao, S, Du K., Yang, B and Zhang, L. (Former PhD Student). 2021. Copycat Skills and Disclosure Costs: Evidence from Peer Companies’ Digital Footprints , Journal of Accounting Research,59 (4), 1261-1302.

• Presented at Bentley University, 2019 China International Conference in Finance, Chinese University of Hong Kong, 2019 Financial Management Association Annual Meeting, Georgia State University, 2019 Georgia Tech SESARC, Hong Kong University of Science and Technology, Louisiana State University, 2019 Michigan State GMARS Research Symposium, 2020 Midwest Finance Association Annual Meeting, Nanyang Technological University, Pennsylvania State University, and PNC Finance Conference at the University of Kentucky

Cao, S, Cong, L., Han, M., Hou, Q. and Yang, B. 2020. Blockchain Architecture for Auditing Automation and Trust-building in Public Markets . IEEE Computer,53(7), 20-28 

• Impact factor: 3.564. IEEE Computer is the flagship peer-reviewed publication of the IEEE Computer Society for both researchers and practitioners in computer science. See https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2 
• Highlights: we implement a pilot blockchain-based distributive ledger ecosystem that automates partial auditing processes and financial reporting with efficacy and scalability (around 0.01 seconds per transaction). Grant Thornton CEO and CMO, has personally made inquiries into this work. 

Cao, S., Tao, M. and Wan, C. 2019. Do Local Investors Always Know Better? Evidence from China's Market Segmentation.Accounting Horizons,33(1), 17–37.

• Presented at 2017 Accounting Horizons Conference, University of South Carolina, Georgia State University, University of Massachusetts Boston, and Washington University in St. Louis

Cao, S., Ma, G., Tucker J. and Wan, C. 2018. Technological Peer Pressure and Product Disclosure. The Accounting Review, 93(6), 95–126.

• Presented at 2017 AAA FARS Midyear Meeting, National University of Singapore, Nanyang Technological University, Singapore Management University, Georgia State University, Erasmus University Rotterdam, The University of Melbourne, and The University of North Carolina at Charlotte.

Accounting Fundamentals for Disclosure and Investment Research 

Cao, S, Wang Z. (Former PhD student) and Yeung, E. 2022. Skin in the Game: Operating Growth, Firm Performance, and Future Stock Returns. Journal of Financial and Quantitative Analysis, 57(7), 2559-2590.

• Presented at the 2019 China International Conference in Finance, 2019 MFA Annual Meeting, Temple Accounting 100th Anniversary Conference, the 31st Australasian Finance and Banking Conference, 2019 AAA Financial Accounting and Reporting Section Mid-Year Meeting, Cornell University, Georgia State University, and Southwest Jiaotong University 

Cao, S. 2016. Reexamining Growth Effects: Are All Types of Asset Growth the Same? Contemporary Accounting Research,33(4), 1518-1548

• Presented at University of Virginia; College of William and Mary, Rutgers University Business School, Georgia State University, University of Illinois at Urbana – Champaign, and Korean Advanced Institute of Science and Technology (KAIST)

Cao, S. and Narayanamoorthy, G. 2012. Earnings volatility, post–earnings announcement drift, and trading frictions. Journal of Accounting Research, 50(1), 41-74

• Presented at State Street Boston Headquarters, University of Illinois at Urbana Champaign, and AAA Annual Meeting

Regulatory or Advisory

• SEC Conference on Financial Market Regulation link
• PCAOB/JAR conference link
• Federal Reserve Bank Conference for Financial Stability link
• National Bureau of Economic Research (NBER) Digest link
• National Bureau of Economic Research (NBER) Big Data conference link
• National Bureau of Economic Research (NBER) Artificial Intelligence Conference 2020 and 2021 link
• National Bureau of Economic Research (NBER) Blockchain conference link

TV Interview

• Interviewed by CNBC 11 Alive News (Aired June 4, 2018)
• Commentary invited by Thinkrealty.com

CNBC: https://goo.gl/ApS5Dc

Digital Media and Blogs

• IR Magazine link
  • Title: “From man versus machine to man plus machine: The art and AI of stock analysis”
• Financial Times link
  • Title: “Robo-surveillance shifts tone of CEO earnings calls”
• CNBC link
  • Title: “Corporate execs are talking differently on earnings calls to please the machines”
• Bloomberg link and Yahoo Finance link
  • Title: “Sweet-Talking CEOs Are Starting to Outsmart the Robot Analysts”
• The Guardian  link
• Title: “Companies are now writing reports tailored for AI readers – and it should worry us”
• Quartz link
  • Title: “Reshaping the language of corporate reporting”
• Columbia University Law School Blog on corporations and capital markets link
  • Title: “How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI”
• University of Oxford Business Law Blog link 
  • Title: “How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI”
• Duke University Global Financial Markets Center link
  • Title: “Blockchains and Collaborative Auditing”
• University of Chicago Booth Review link
  • Title: “Blockchain could improve corporate auditing”
• Multiple commentaries invited by Wallethub.com

Machine Learning and AI, including ChatGPT, showcase extensive applicability across diverse domains. Nevertheless, domain knowledge is pivotal for the effective application of AI tools. Our textbook, Analytics for Finance and Accounting: Data Structures and Applied AI,  is crafted to assist readers in comprehending domain-specific financial text and image data, which includes annual reports, press releases, conference calls, corporate images, corporate social media, and mutual fund disclosures. The book offers insights into data structures, textual content, features, and potential use cases, and incorporates textual analysis tools, including ChatGPT.
 

The textbook has gained recognition and adoption in universities worldwide, serving as a valuable resource for undergraduate, master's, and PH.D. courses. Notable institutions include the University of Maryland (scheduled), Lancaster University, HKUST, University of Turku, Tsinghua University, and Renmin University.
 

Download the Textbook: Analytics for Finance and Accounting: Data Structures and Applied AI

• Each chapter has tutorial videos for instructors and students found in the textbook's table of contents.

Additional Resources

• Tutorial Slides
• Python Tutorial for web crawling and fundamental textual analysis
  • Crawling SEC Edgar
  • Parsing 10-K filings
  • Applying GPT: analyzing conference call transcripts using both API and web interface
• Recorded related lectures: The objective of these lectures is to enable non-computer-science scholars to integrate machine learning into finance and accounting research smoothly.
  • Video 1: Outlook of Fintech and AI research 
  • Video 2: Textual analytic tools in Accounting and Finance
  • Video 3: ML literature review, unsupervised and transfer learning, and Google BERT
  • Video 4: Supervised and ensemble learning, Grid search for model tuning, ML examples in academic research
  •  Video 5: Introduction to Knowledge Graph
     

More tutorials can be found here on YouTube or Chinese versions here on Bilibili.
 

The new course is scheduled to premiere in the Fall 2024 Term B.
Course Title: AI Literacy: Business Data and AI Applications
Course Number: BUAC 758X

Course Description
Business students with a keen interest in applying artificial intelligence (AI)-based tools in research often encounter a challenge: the need to enroll in separate programming courses that operate independently from their core curriculum. This creates an educational void, compelling our students to juggle the amalgamation of these two disciplines. This course addresses this gap by equipping students with the necessary skills to integrate applied AI with domain-specific data and knowledge in the fields of accounting and finance.

Unlike conventional approaches that commence with programming training, this course begins by acquainting students with domain data, textual features, and use cases associated with these data. Relevant emerging technologies are then introduced along with use cases. Upon completing the course, students could be expected to apply AI and machine learning tools to generate and analyze unstructured financial data, such as conference call transcripts, press releases, annual reports, ESG, or other social media disclosures, product/operational images, and fund managers' disclosures. To enhance
the learning experience, the textbook is complemented by a video library, featuring educational videos corresponding to each chapter, including tutorials on how to use the GPT API.
 

Award recipient of Deloitte Initiative for AI and Learning for developing trustworthy AI for social equity and climate change

Michael J. Brennan Award for the best paper published in Review of Financial Studies (with coauthors), How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI

PanAgora Asset Management’s Dr. Richard A. Crowell Memorial Prize (with coauthors), Visual Information and AI Divide: Evidence from Corporate Executive Presentations.

• For those intrigued by the application of AI in extracting trading signals from corporate production pictures, we invite you to explore this research. Additionally, our paper delves into documenting the potential AI divide among various investor groups.

American Association of Individual Investors (AAII) Best paper award winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , 2022 Midwest Finance Association

Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , 2022 Global AI Finance Conference

Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , 2022 CFRC Conference, PBC School of Finance, Tsinghua University

Best Paper Award Winner (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , 2022 Annual Conference in Digital Economics, ACDE

Best Paper Award Winner in Asset Pricing (with coauthors), From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses , 2022 SFS Cavalcade Asia-Pacific Conference

Winner of the AMTD FinTech Centre Prize (with coauthors), How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI ,2022 Asian Finance Association Conference

Best Paper Award Winner (with coauthors), How to Talk When Machines are Listening: Corporate Disclosure in the Age of AI, 2021CAPANA Research Conference 

FMA 2021 Best Paper Semi-finalist in FinTech (with coauthors), Can Machines Understand Human Decisions: Dissecting Stock Forecasting Skill?

Research Grant from Smith Internal Grants Program 2022

Teaching Grant from Smith Teaching/Learning Innovation Grants (STIGs) 2022

Conference organizations:

Conference track co-chair, Maryland Climate Finance Symposium, Climate Reporting Session

Conference track Chair,“Big Data, Fintech, and Blockchain” of 2023 American Accounting Association

January 23, 2024: AI Symposium on Design & Governance

Industry leaders including Head of Investment Solutions, Alliance Bernstein and Vice Chairman of Wolfe Investment Research are sharing their insights and experiences regarding the AI opportunities within their respective domains. The symposium attracted a full house of 200 participants from both the academic and industry sectors.

Invited Keynotes

• Keynote Speaker for Pacific-Basin Finance Journal (PBFJ) special issue conference on “Artificial Intelligence and Machine Learning in Corporate Finance”, December 2023. 
• Keynote Speaker for “Machine Learning, AI and FinTech in Capital Market” at Stock Exchange of Thailand, Bangkok, Thailand, July 2023 
  • See video broadcasted on July 19, 2023
• Keynote and Panel Speaker for “Latest NLP models in capital market research” at Technical University of Munich (Technische Universität München), Germany, Nov 18 - 19, 2021 
• Panel Speaker at the Academy of Management conference for “Textual and Voice Analysis for CEO Traits and Emotion: Challenges and Future Research Directions”, August 2022
• Panel Speaker at “2023 AAAI Conference on Artificial Intelligence” (also three papers presented at this leading computer science conference)
• Panel Speaker at “Toronto Rotman CPA Conference” (with 2,000 FinTech professionals and industry participants), Canada
• Keynote Speaker “2022 Innovation in Management Theory and Practice in the Age of Digital Intelligence” 
  • See video broadcasted on December 4, 2022

Invited Talks

for Industry and Regulatory Institutions

Central Bank of Japan, Central Bank of Thailand, Stock Exchange of Thailand, Alliance Bernstein (AB), Balyasny Asset Management (BAM), State Street Boston Headquarters, Wolfe Investment Research, Accenture, Grant Thornton, NIV Asset, and Founder Securities 

for Academic

2025 University of Wisconsin Madison

2024 University of California Davis (accounting and finance), University of Colorado Boulder, University of Illinois Chicago, Lancaster University, Santa Clara University

2023 Carnegie Mellon University (Tepper), Columbia Burton Accounting Conference, Penn State Accounting Research Conference 2023, 2023 Colorado Boulder Summer Conference on household, University of Texas Dallas (accounting and finance), University of Utah, Florida International University, University of Waterloo

2023(International): London School of Business Accounting Symposium, University of Cambridge, One-week research visit and workshops at Technical University of Munich (Finance, two campuses), Goethe University Frankfurt, Erasmus University, NHH Norwegian School of Economics, IE Business School Spain, Lancaster University (Finance), University of Manchester, One-week visit and workshops in The University of New South Wales (Finance), University of Technology Sydney, The University of Sydney (Finance), Australian National University (Finance), Macquarie University (Finance), University of Auckland (Finance and Accounting), Chinese University of Hong Kong (multiple departments), Hong Kong University, MIT Asian Accounting Conference

2022 MIT Sloan, University of Kentucky (Finance), Fordham University (Finance), Kent State University (Finance), University of Calgary, Stockholm School of Economics AI conference, University of Florida Machine Learning Conference, Naresuan University, Thailand

2021 Harvard University, University of Michigan, University of California Irvine, University of Washington, Texas A&M, Baruch College CUNY, University of Maryland, Emory University, University of Miami Winter Research Conference on Machine Learning and Business

2021 (International): Frankfurt School of Finance & Management, ESSEC (Finance), Copenhagen Business School, University of Amsterdam, University of Turku (Finance), NEOMA, Stockholm Business School