Brian Weller

Duke University - Department of Economics

Durham, NC

United States

http://sites.google.com/site/brianmweller/

  • Duke University - Department of Economics

SCHOLARLY PAPERS

5

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Top 19,112

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2,611

SSRN CITATIONS
Rank 32,512

SSRN RANKINGS

Top 32,512

in Total Papers Citations

11

CROSSREF CITATIONS

9

Scholarly Papers (5)

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1.

Does Algorithmic Trading Deter Information Acquisition?

Number of pages: 55 Posted: 19 Sep 2015 Last Revised: 31 Aug 2018
Brian Weller
Duke University - Department of Economics
Downloads 979 (23,341)
Citation 15

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Information Acquisition, Price Discovery, Algorithmic Trading, SEC MIDAS

2.

What You See Is Not What You Get: The Costs of Trading Market Anomalies

Journal of Financial Economics (JFE), Forthcoming, Economic Research Initiatives at Duke (ERID) Working Paper No. 255
Number of pages: 79 Posted: 12 Sep 2017 Last Revised: 04 May 2019
Andrew J. Patton and Brian Weller
Duke University - Department of Economics and Duke University - Department of Economics
Downloads 863 (27,966)
Citation 6

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Trading Costs, Performance Evaluation, Mutual Funds, Market Efficiency

3.

Measuring Tail Risks at High Frequency

Number of pages: 61 Posted: 01 Apr 2015 Last Revised: 27 Oct 2018
Brian Weller
Duke University - Department of Economics
Downloads 499 (58,025)

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Tail Risks, High-Frequency Market Making, Bid-Ask Spreads

4.

Risk Price Variation: The Missing Half of Empirical Asset Pricing

Economic Research Initiatives at Duke (ERID) Working Paper No. 274
Number of pages: 62 Posted: 21 Jun 2018 Last Revised: 27 May 2019
Andrew J. Patton and Brian Weller
Duke University - Department of Economics and Duke University - Department of Economics
Downloads 250 (127,373)

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Risk Premia, Market Segmentation, Clustering, Expectation Maximization

5.

Testing for Unobserved Heterogeneity via K-Means Clustering

Number of pages: 37 Posted: 18 Jul 2019
Andrew J. Patton and Brian Weller
Duke University - Department of Economics and Duke University - Department of Economics
Downloads 20 (541,995)

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model selection, overfitting, machine learning, classification methods