Tugce Karatas
Decision Sciences · Purdue University West Lafayette
Publications
15
Citations
35
Est. group size
—
Recurring co-author estimate
Active years
10
Publishing since 2016
Tugce Karatas works at the intersection of finance and machine learning, applying deep neural networks and other computational techniques to problems like predicting mergers and acquisitions outcomes, pricing financial options, forecasting cash flows for hard-to-trade (illiquid) assets, and building strategies for rotating investments across market sectors. Earlier work also touched on experimental and numerical studies of heat transfer in boiling processes, suggesting a background that spans engineering and quantitative finance. Prospective students would likely engage with projects combining machine learning methods with financial modeling and forecasting.
Publication output has been irregular over the last decade, with a notable spike in 2021 (7 publications) followed by sparser activity, averaging less than one publication per year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Predicting status of pre- and post-M&A deals using machine learning and deep learning techniques
Digital Finance · 2025
- Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
WORLD SCIENTIFIC eBooks · 2023
- Achieving Equity and Excellence: A Multilevel Modeling of the Relationships among School Climate, Students’ Motivation, and Achievement with TIMSS 2019
2023
- Supervised Neural Networks for Illiquid Alternative Asset Cash Flow\n Forecasting
arXiv (Cornell University) · 2021
- Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques
arXiv (Cornell University) · 2021
- Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques.
arXiv (Cornell University) · 2021
- Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques
SSRN Electronic Journal · 2021
- Supervised Neural Networks for Illiquid Alternative Asset Cash Flow Forecasting
arXiv (Cornell University) · 2021
- Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques.
arXiv (Cornell University) · 2021
- Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques
arXiv (Cornell University) · 2021
- Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of\n Vanilla/Exotic Options Under Various Different Processes
arXiv (Cornell University) · 2019
- Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
arXiv (Cornell University) · 2019
- Experimental Investigation of Effect of Pore Diameter on Nucleate Boiling Heat Transfer in Reentrant Tunnel Structured Surfaces
2017
- Numerical Investigatinon Of Pool Boiling On Flat And Patterned Surface
Istanbul Technical University Academic Open Archive (Istanbul Technical University) · 2016
- arXiv (Cornell University)×8
- Journal of College Student Retention Research Theory & Practice×1
- Digital Finance×1
- WORLD SCIENTIFIC eBooks×1
- SSRN Electronic Journal×1
- Benjamin Jiang
Decision Sciences · The Ohio State University
- Andreas Neuhierl
Economics, Econometrics and Finance · Purdue University West Lafayette
- Svetlana Bryzgalova
Economics, Econometrics and Finance · Indiana University
- Russell Rhoads
Economics, Econometrics and Finance · Indiana University
- Adem Atmaz
Economics, Econometrics and Finance · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile