| Dynamic replication and hedging: A reinforcement learning approach PN Kolm, G Ritter The Journal of Financial Data Science 1 (1), 159-171, 2019 | 87 | 2019 |
| Modern perspectives on reinforcement learning in finance PN Kolm, G Ritter Modern Perspectives on Reinforcement Learning in Finance (September 6, 2019 …, 2020 | 77 | 2020 |
| Machine learning for trading G Ritter Available at SSRN 3015609, 2017 | 60 | 2017 |
| Quantum field theory on curved backgrounds. I. The Euclidean functional integral A Jaffe, G Ritter Communications in mathematical physics 270, 545-572, 2007 | 50 | 2007 |
| The multistage model of cancer development: some implications G Ritter, R Wilson, F Pompei, D Burmistrov Toxicology and industrial health 19 (7-10), 125-145, 2003 | 47 | 2003 |
| On the bayesian interpretation of black–litterman P Kolm, G Ritter European Journal of Operational Research 258 (2), 564-572, 2017 | 43 | 2017 |
| Reflection positivity and monotonicity A Jaffe, G Ritter Journal of mathematical physics 49 (5), 052301, 2008 | 35 | 2008 |
| Abstract simplicity of complete Kac–Moody groups over finite fields L Carbone, M Ershov, G Ritter Journal of Pure and Applied Algebra 212 (10), 2147-2162, 2008 | 33 | 2008 |
| Deep reinforcement learning for option replication and hedging J Du, M Jin, PN Kolm, G Ritter, Y Wang, B Zhang The Journal of Financial Data Science 2 (4), 44-57, 2020 | 31 | 2020 |
| Black–litterman and beyond: The bayesian paradigm in investment management PN Kolm, G Ritter, J Simonian The Journal of Portfolio Management 47 (5), 91-113, 2021 | 30 | 2021 |
| Multiperiod portfolio selection and bayesian dynamic models PN Kolm, G Ritter Risk 28 (3), 50-54, 2014 | 26 | 2014 |
| Quantum field theory on curved backgrounds. II. Spacetime symmetries A Jaffe, G Ritter arXiv preprint arXiv:0704.0052, 2007 | 20 | 2007 |
| Factor investing with Black–Litterman–Bayes: incorporating factor views and priors in portfolio construction PN Kolm, G Ritter The Journal of Portfolio Management 47 (2), 113-126, 2020 | 7 | 2020 |
| Reinforcement learning in finance G Ritter Big Data and Machine Learning in Quantitative Investment, 225-50, 2018 | 4 | 2018 |
| Optimal Turnover, Liquidity, and Autocorrelation B Baldacci, J Benveniste, G Ritter arXiv preprint arXiv:2110.03810, 2021 | 3 | 2021 |
| Stable linear-time optimization in arbitrage pricing theory models G Ritter Risk Magazine, 2016 | 3 | 2016 |
| Optimal microstructure trading with a long-term utility function E Benveniste, G Ritter Available at SSRN 3057570, 2017 | 2 | 2017 |
| A new copula for modeling tail dependence J Holman, G Ritter Available at SSRN 1665977, 2010 | 2 | 2010 |
| Practical Applications of Deep Reinforcement Learning for Option Replication and Hedging JN Du, M Jin, PN Kolm, G Ritter, Y Wang, B Zhang Practical Applications 9 (1), 1-8, 2021 | 1 | 2021 |
| A Hardy–Ramanujan Formula for Lie Algebras G Ritter Experimental Mathematics 16 (3), 375-384, 2007 | 1 | 2007 |
Petter KolmNYU Courant Institute of Mathematical SciencesVerified email at nyu.edu
