Gordon Ritter

New York University (NYU) - Courant Institute of Mathematical Sciences

Adjunct Professor

New York University

251 Mercer Street

New York, NY 10012

United States

City University of New York - Weissman School of Arts and Sciences

One Bernard Baruch Way

New York, NY 10010

United States

Rutgers, The State University of New Jersey - Financial Statistics & Risk Management

Professor of Practice

110 Frelinghuysen Road

479 Hill Center, Busch Campus

Piscataway, NJ 08854

United States

New York University (NYU) - NYU Tandon School of Engineering

6 MetroTech Center

Brooklyn, NY 11201

United States

SCHOLARLY PAPERS

8

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13,579

SSRN CITATIONS
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SSRN RANKINGS

Top 43,957

in Total Papers Citations

3

CROSSREF CITATIONS

9

Scholarly Papers (8)

1.

Machine Learning for Trading

Number of pages: 19 Posted: 14 Aug 2017 Last Revised: 04 Dec 2017
Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 8,442 (631)
Citation 6

Abstract:

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Finance, Investment Analysis, Machine Learning, Portfolio Optimization

2.

Multiperiod Portfolio Selection and Bayesian Dynamic Models

Risk, Vol. 28, Issue 3, p 50-54, March 2015
Number of pages: 8 Posted: 28 Jul 2014 Last Revised: 04 Dec 2017
Petter N. Kolm and Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences and New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 1,953 (7,846)
Citation 1

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Portfolio optimization, Transaction costs, Bayesian statistics, Markov models

3.

Modern Perspectives on Reinforcement Learning in Finance

The Journal of Machine Learning in Finance, Vol. 1, No. 1, 2019.
Number of pages: 28 Posted: 16 Sep 2019
Petter N. Kolm and Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences and New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 938 (24,575)
Citation 1

Abstract:

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Dynamic programming, Finance, Hedging, Intertemporal choice; Investment analysis, Machine learning, Optimal control, Options, Portfolio optimization, Reinforcement learning

4.

On the Bayesian Interpretation of Black-Litterman

European Journal of Operational Research, Volume 258, Issue 2, 16 April 2017, Pages 564-572
Number of pages: 22 Posted: 17 Oct 2016 Last Revised: 01 Dec 2017
Petter N. Kolm and Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences and New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 844 (28,565)

Abstract:

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Black-Litterman, Portfolio Optimization, APT, Bayesian statistics

5.

Optimal Microstructure Trading with a Long-Term Utility Function

Number of pages: 26 Posted: 24 Oct 2017 Last Revised: 03 Dec 2017
Elie Benveniste and Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences and New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 550 (50,789)
Citation 1

Abstract:

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Finance, Investment Analysis, Optimal Execution, Market Microstructure

6.

Stable Linear-Time Optimization in Arbitrage Pricing Theory Models

Risk Magazine, 2016
Number of pages: 9 Posted: 12 Aug 2016
Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 517 (54,999)

Abstract:

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Portfolio Optimization

7.

A New Copula for Modeling Tail Dependence

Number of pages: 29 Posted: 26 Aug 2010 Last Revised: 15 Nov 2010
Jeff Holman and Gordon Ritter
Highbridge Capital Management and New York University (NYU) - Courant Institute of Mathematical Sciences
Downloads 335 (92,234)

Abstract:

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8.

Dynamic Replication and Hedging: A Reinforcement Learning Approach

Kolm, Petter N. and Gordon Ritter. "Dynamic Replication and Hedging: A Reinforcement Learning Approach." The Journal of Financial Data Science 1.1 (2019).
Posted: 05 Dec 2018 Last Revised: 12 Nov 2019
Petter N. Kolm and Gordon Ritter
New York University (NYU) - Courant Institute of Mathematical Sciences and New York University (NYU) - Courant Institute of Mathematical Sciences

Abstract:

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Finance; Hedging; Investment analysis; Machine learning; Optimal control; Options; Portfolio optimization; Reinforcement learning