2343777
Project Grant
Overview
Grant Description
Sttr phase I: A technology for learning to infer from unlabeled financial data -the broader/commercial impact of this small business technology transfer (STTR) phase I project is to enable long-term, above-average profit returns from investing into the U.S. stock market. Currently, investors rely on the financial models that can forecast the future business performance of company only by looking through a rear-view mirror, and, consequently, create risks for the speculative stock trading that does not deliver any actual goods or services.
If successful, this STTR project will make an important social impact that is controlling speculation by discouraging stock trading of company without material changes to its valuation. The research and educational impact of the current project is that selected results from the proposed studies beneficial to the fundamental research will be made available to the U.S research community for analysis, data mining, and search and will be also used to enhance contents of undergraduate and graduate courses.
The economic impact is that the proposed efforts will create new financial technology related jobs in the North Alabama region. This small business technology transfer phase I project will develop and validate innovative technology capable of learning to infer from time series financial data in a resource-scarce environment. This technology will address the following technical hurdles: (a) well-documented deficiencies of machine reasoning of the qualitative parts of financial reports and earning call transcripts containing information that is much richer than just the financial ratios; (b) current reliance of the language-based models including ChatGPT on human annotation in resource-scarce environment, (c) difficulties with transfer learning for extensive, specialized documents, and (d) scarcity of labeled financial text.
The technology will be based on innovative algorithms that use language-based models to augment original unlabeled data and utilize such augmented data for predicting the company valuation far ahead of the existing models. The feasibility of the proposed technology will be conducted in collaboration with academic and industrial partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.- Subawards are planned for this award.
If successful, this STTR project will make an important social impact that is controlling speculation by discouraging stock trading of company without material changes to its valuation. The research and educational impact of the current project is that selected results from the proposed studies beneficial to the fundamental research will be made available to the U.S research community for analysis, data mining, and search and will be also used to enhance contents of undergraduate and graduate courses.
The economic impact is that the proposed efforts will create new financial technology related jobs in the North Alabama region. This small business technology transfer phase I project will develop and validate innovative technology capable of learning to infer from time series financial data in a resource-scarce environment. This technology will address the following technical hurdles: (a) well-documented deficiencies of machine reasoning of the qualitative parts of financial reports and earning call transcripts containing information that is much richer than just the financial ratios; (b) current reliance of the language-based models including ChatGPT on human annotation in resource-scarce environment, (c) difficulties with transfer learning for extensive, specialized documents, and (d) scarcity of labeled financial text.
The technology will be based on innovative algorithms that use language-based models to augment original unlabeled data and utilize such augmented data for predicting the company valuation far ahead of the existing models. The feasibility of the proposed technology will be conducted in collaboration with academic and industrial partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.- Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Huntsville,
Alabama
35801-5325
United States
Geographic Scope
Single Zip Code
Mathinvestments was awarded
Project Grant 2343777
worth $274,936
from National Science Foundation in July 2024 with work to be completed primarily in Huntsville Alabama United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: A Technology for Learning to Infer from Unlabeled Financial Data
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to enable long-term, above-average profit returns from investing into the U.S. stock market. Currently, investors rely on the financial models that can forecast the future business performance of company only by looking through a rear-view mirror, and, consequently, create risks for the speculative stock trading that does not deliver any actual goods or services. If successful, this STTR project will make an important social impact that is controlling speculation by discouraging stock trading of company without material changes to its valuation. The research and educational impact of the current project is that selected results from the proposed studies beneficial to the fundamental research will be made available to the U.S research community for analysis, data mining, and search and will be also used to enhance contents of undergraduate and graduate courses. The economic impact is that the proposed efforts will create new financial technology related jobs in the North Alabama region.
This Small Business Technology Transfer Phase I project will develop and validate innovative technology capable of learning to infer from time series financial data in a resource-scarce environment. This technology will address the following technical hurdles: (a) well-documented deficiencies of machine reasoning of the qualitative parts of financial reports and earning call transcripts containing information that is much richer than just the financial ratios; (b) current reliance of the language-based models including ChatGPT on human annotation in resource-scarce environment, (c) difficulties with transfer learning for extensive, specialized documents, and (d) scarcity of labeled financial text. The technology will be based on innovative algorithms that use language-based models to augment original unlabeled data and utilize such augmented data for predicting the company valuation far ahead of the existing models. The feasibility of the proposed technology will be conducted in collaboration with academic and industrial partners.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 7/8/24
Period of Performance
7/1/24
Start Date
6/30/25
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2343777
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
ZTNBALB8TY44
Awardee CAGE
None
Performance District
AL-05
Senators
Tommy Tuberville
Katie Britt
Katie Britt
Modified: 7/8/24