Nick Polson

3.1k total citations · 2 hit papers
33 papers, 1.8k citations indexed

About

Nick Polson is a scholar working on Finance, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Nick Polson has authored 33 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Finance, 10 papers in Artificial Intelligence and 9 papers in Management Science and Operations Research. Recurrent topics in Nick Polson's work include Financial Markets and Investment Strategies (9 papers), Financial Risk and Volatility Modeling (7 papers) and Stochastic processes and financial applications (6 papers). Nick Polson is often cited by papers focused on Financial Markets and Investment Strategies (9 papers), Financial Risk and Volatility Modeling (7 papers) and Stochastic processes and financial applications (6 papers). Nick Polson collaborates with scholars based in United States, Hong Kong and United Kingdom. Nick Polson's co-authors include James G. Scott, Carla M. Carvalho, Michael Johannes, J.B. Heaton, J. H. Witte, Bjørn Eraker, Arthur G. Korteweg, Jonathan Stroud, Ravi Kannan and Alan Frieze and has published in prestigious journals such as Biometrika, The Annals of Applied Probability and Entropy.

In The Last Decade

Nick Polson

28 papers receiving 1.7k citations

Hit Papers

The horseshoe estimator for sparse signals 2010 2026 2015 2020 2010 2016 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The horseshoe estimator for sparse signals
    2010 689 citations Carla M. Carvalho, Nick Polson et al. Biometrika profile →
  2. Deep learning for finance: deep portfolios
    2016 398 citations J.B. Heaton, Nick Polson et al. Applied Stochastic Models in Business and Industry profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nick Polson United States 15 680 466 460 453 357 33 1.8k
Rolf–Dieter Reiss Germany 15 1.1k 1.6× 939 2.0× 377 0.8× 401 0.9× 358 1.0× 57 2.3k
Bing‐Yi Jing Hong Kong 27 657 1.0× 1.2k 2.6× 458 1.0× 338 0.7× 426 1.2× 102 2.3k
Chris Carter Australia 12 636 0.9× 504 1.1× 654 1.4× 831 1.8× 256 0.7× 29 2.2k
Yuan Liao United States 14 490 0.7× 696 1.5× 192 0.4× 713 1.6× 152 0.4× 48 1.7k
Lijian Yang United States 29 416 0.6× 1.1k 2.4× 379 0.8× 365 0.8× 194 0.5× 105 2.1k
Ritei Shibata Japan 14 290 0.4× 891 1.9× 504 1.1× 299 0.7× 267 0.7× 32 2.1k
Jeffrey D. Hart United States 29 472 0.7× 1.8k 4.0× 506 1.1× 319 0.7× 288 0.8× 88 2.9k
Juan Romo Spain 16 239 0.4× 596 1.3× 254 0.6× 188 0.4× 169 0.5× 58 1.2k
Kengo Kato Japan 23 248 0.4× 1.1k 2.3× 286 0.6× 397 0.9× 127 0.4× 71 1.8k
Richard Gerlach Australia 26 903 1.3× 355 0.8× 207 0.5× 764 1.7× 216 0.6× 91 1.6k

Countries citing papers authored by Nick Polson

Since Specialization
Citations

This map shows the geographic impact of Nick Polson's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nick Polson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nick Polson more than expected).

Fields of papers citing papers by Nick Polson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nick Polson. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nick Polson. The network helps show where Nick Polson may publish in the future.

Co-authorship network of co-authors of Nick Polson

This figure shows the co-authorship network connecting the top 25 collaborators of Nick Polson. A scholar is included among the top collaborators of Nick Polson based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nick Polson. Nick Polson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Polson, Nick, Fabrizio Ruggeri, & Vadim Sokolov. (2024). Generative Bayesian Computation for Maximum Expected Utility. Entropy. 26(12). 1076–1076.
2.
Polson, Nick, et al.. (2024). Generative Modeling: A Review. arXiv (Cornell University).
3.
Polson, Nick, et al.. (2023). Quantum Bayesian computation. Applied Stochastic Models in Business and Industry. 39(6). 869–883.
4.
Dixon, Matthew, et al.. (2022). Deep Partial Least Squares for Empirical Asset Pricing. SSRN Electronic Journal. 1 indexed citations
5.
Polson, Nick, et al.. (2022). Gambits: Theory and evidence. Applied Stochastic Models in Business and Industry. 38(4). 572–589.
6.
Dixon, Matthew & Nick Polson. (2020). Short Communication: Deep Fundamental Factor Models. SIAM Journal on Financial Mathematics. 11(3). SC26–SC37. 10 indexed citations
7.
Feng, Guanhao, et al.. (2018). Deep Learning Factor Alpha. SSRN Electronic Journal. 19 indexed citations
8.
Heaton, J.B., Nick Polson, & J. H. Witte. (2017). Why indexing works. Applied Stochastic Models in Business and Industry. 33(6). 690–693. 11 indexed citations
9.
Heaton, J.B., Nick Polson, & J. H. Witte. (2016). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry. 33(1). 3–12. 398 indexed citations breakdown →
10.
Polson, Nick, et al.. (2015). A Bellman View of Jesse Livermore. CHANCE. 28(1). 27–31. 3 indexed citations
11.
Polson, Nick & James G. Scott. (2013). Data augmentation for non-Gaussian regression models using variance-mean mixtures. Biometrika. 100(2). 459–471. 22 indexed citations
12.
Carvalho, Carla M., Nick Polson, & James G. Scott. (2010). The horseshoe estimator for sparse signals. Biometrika. 97(2). 465–480. 689 indexed citations breakdown →
13.
Johannes, Michael, Nick Polson, & Jonathan Stroud. (2009). Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices. SSRN Electronic Journal. 5 indexed citations
14.
Johannes, Michael, et al.. (2009). Quantile Filtering and Learning. SSRN Electronic Journal. 3 indexed citations
15.
Johannes, Michael, et al.. (2009). Sequential Inference for Nonlinear Models using Slice Variables. SSRN Electronic Journal. 2 indexed citations
16.
Johannes, Michael, Nick Polson, & Jonathan Stroud. (2002). Sequential Optimal Portfolio Performance: Market and Volatility Timing. SSRN Electronic Journal. 57 indexed citations
17.
Johannes, Michael, Nick Polson, & Jonathan Stroud. (2002). Nonlinear Filtering of Stochastic Differential Equations with Jumps. SSRN Electronic Journal. 17 indexed citations
18.
Johannes, Michael, Bjørn Eraker, & Nick Polson. (2001). The Impact of Jumps in Volatility and Returns. SSRN Electronic Journal. 314 indexed citations
19.
Polson, Nick & Larry Wasserman. (1990). Prior distributions for the bivariate binomial. Biometrika. 77(4). 901–904. 15 indexed citations
20.
Polson, Nick & Larry Wasserman. (1990). Prior Distributions for the Bivariate Binomial. Biometrika. 77(4). 901–901. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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