Brian Weller

Duke University - Department of Economics

SCHOLARLY PAPERS

5

DOWNLOADS
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Top 19,138

in Total Papers Downloads

2,768

SSRN CITATIONS
Rank 26,429

SSRN RANKINGS

Top 26,429

in Total Papers Citations

22

CROSSREF CITATIONS

9

Scholarly Papers (5)

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 1,004 (23,914)
Citation 23

Abstract:

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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 932 (26,568)
Citation 7

Abstract:

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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 516 (58,863)
Citation 1

Abstract:

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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 287 (116,280)

Abstract:

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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 29 (513,751)

Abstract:

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model selection, overfitting, machine learning, classification methods