Jan Witte

University College London - Department of Mathematics

Gower Street

London, WC1E 6BT

United Kingdom

SCHOLARLY PAPERS

4

DOWNLOADS
Rank 2,266

SSRN RANKINGS

Top 2,266

in Total Papers Downloads

15,784

SSRN CITATIONS

14

CROSSREF CITATIONS

8

Ideas:
“  My academic research interests focus on numerical mathematical finance. I have a particular interest in deep learning (DL) and artificial intelligence (AI) in finance, as well as the superiority of passive/index investing.  ”

Scholarly Papers (4)

1.

Deep Learning for Finance: Deep Portfolios

Applied Stochastic Models in Business and Industry 33 (1), 3-12.
Number of pages: 15 Posted: 14 Sep 2016 Last Revised: 29 Apr 2019
J.B. Heaton, Nick Polson and Jan Witte
One Hat Research LLC, University of Chicago - Booth School of Business and University College London - Department of Mathematics
Downloads 8,038 (735)
Citation 28

Abstract:

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Deep Learning, Machine Learning, Big Data, Artificial Intelligence, Finance, Asset Pricing, Volatility, Deep Frontier

2.

Why Indexing Works

Applied Stochastic Models in Business and Industry 33 (6), 690-693.
Number of pages: 7 Posted: 14 Oct 2015 Last Revised: 29 Apr 2019
J.B. Heaton, Nick Polson and Jan Witte
One Hat Research LLC, University of Chicago - Booth School of Business and University College London - Department of Mathematics
Downloads 6,257 (1,124)
Citation 2

Abstract:

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Indexing, Passive Management, Active Management

3.

The Blockchain: A Gentle Introduction

Number of pages: 5 Posted: 19 Dec 2016
Jan Witte
University College London - Department of Mathematics
Downloads 1,332 (15,351)
Citation 4

Abstract:

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Data Security, Blockchain, Bitcoin, Cryptocurrency

4.

Synthetic Financial Data: An Application to Regulatory Compliance for Broker-Dealers

Journal of Financial Transformation (Forthcoming)
Number of pages: 12 Posted: 23 Sep 2019 Last Revised: 23 Apr 2020
J.B. Heaton and Jan Witte
One Hat Research LLC and University College London - Department of Mathematics
Downloads 157 (203,089)

Abstract:

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Synthetic Data, Machine Learning, Best Interest, Compliance