Table of Contents

Winter 2019; Volume 1,Issue 1

A

  1. Aldridge, Irene

    1. Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization
      Irene Aldridge
      The Journal of Financial Data Science Winter 2019, 1 (1) 45-63; DOI: https://doi.org/10.3905/jfds.2019.1.045

  2. Arnott, Rob

    1. A Backtesting Protocol in the Era of Machine Learning
      Rob Arnott, Campbell R. Harvey and Harry Markowitz
      The Journal of Financial Data Science Winter 2019, 1 (1) 64-74; DOI: https://doi.org/10.3905/jfds.2019.1.064

B

  1. Bew, David

    1. Modeling Analysts’ Recommendations via Bayesian Machine Learning
      David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
      The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075

D

  1. Das, Sanjiv R.

    1. Dynamic Systemic Risk: Networks in Data Science
      Sanjiv R. Das, Seoyoung Kim and Daniel N. Ostrov
      The Journal of Financial Data Science Winter 2019, 1 (1) 141-158; DOI: https://doi.org/10.3905/jfds.2019.1.1.141

  2. de Prado, Marcos López

    1. A Data Science Solution to the Multiple-Testing Crisis in Financial Research
      Marcos López de Prado
      The Journal of Financial Data Science Winter 2019, 1 (1) 99-110; DOI: https://doi.org/10.3905/jfds.2019.1.099
    2. Open Access
      Editor’s Letter
      Frank J. Fabozzi, Marcos López de Prado and Joseph Simonian
      The Journal of Financial Data Science Winter 2019, 1 (1) 1-3; DOI: https://doi.org/10.3905/jfds.2019.1.001

F

  1. Fabozzi, Frank J.

    1. Triumph of the Empiricists: The Birth of Financial Data Science
      Joseph Simonian and Frank J. Fabozzi
      The Journal of Financial Data Science Winter 2019, 1 (1) 10-13; DOI: https://doi.org/10.3905/jfds.2019.1.010
    2. Open Access
      Editor’s Letter
      Frank J. Fabozzi, Marcos López de Prado and Joseph Simonian
      The Journal of Financial Data Science Winter 2019, 1 (1) 1-3; DOI: https://doi.org/10.3905/jfds.2019.1.001

H

  1. Harvey, Campbell R.

    1. A Backtesting Protocol in the Era of Machine Learning
      Rob Arnott, Campbell R. Harvey and Harry Markowitz
      The Journal of Financial Data Science Winter 2019, 1 (1) 64-74; DOI: https://doi.org/10.3905/jfds.2019.1.064
    2. Modeling Analysts’ Recommendations via Bayesian Machine Learning
      David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
      The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075

I

  1. Itano, Daniel

    1. A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model
      Joseph Simonian, Chenwei Wu, Daniel Itano and Vyshaal Narayanam
      The Journal of Financial Data Science Winter 2019, 1 (1) 32-44; DOI: https://doi.org/10.3905/jfds.2019.1.032

K

  1. Kim, Seoyoung

    1. Dynamic Systemic Risk: Networks in Data Science
      Sanjiv R. Das, Seoyoung Kim and Daniel N. Ostrov
      The Journal of Financial Data Science Winter 2019, 1 (1) 141-158; DOI: https://doi.org/10.3905/jfds.2019.1.1.141

  2. Klevak, Julia

    1. A Practical Approach to Advanced Text Mining in Finance
      Julia Klevak, Joshua Livnat and Kate Suslava
      The Journal of Financial Data Science Winter 2019, 1 (1) 122-129; DOI: https://doi.org/10.3905/jfds.2019.1.1.122

  3. Kolm, Petter N.

    1. Dynamic Replication and Hedging: A Reinforcement Learning Approach
      Petter N. Kolm and Gordon Ritter
      The Journal of Financial Data Science Winter 2019, 1 (1) 159-171; DOI: https://doi.org/10.3905/jfds.2019.1.1.159

L

  1. Ledford, Anthony

    1. Modeling Analysts’ Recommendations via Bayesian Machine Learning
      David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
      The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075

  2. Livnat, Joshua

    1. A Practical Approach to Advanced Text Mining in Finance
      Julia Klevak, Joshua Livnat and Kate Suslava
      The Journal of Financial Data Science Winter 2019, 1 (1) 122-129; DOI: https://doi.org/10.3905/jfds.2019.1.1.122

M

  1. Madhavan, Ananth

    1. Fine-Tuning Private Equity Replication Using Textual Analysis
      Ananth Madhavan and Aleksander Sobczyk
      The Journal of Financial Data Science Winter 2019, 1 (1) 111-121; DOI: https://doi.org/10.3905/jfds.2019.1.1.111

  2. Markowitz, Harry

    1. A Backtesting Protocol in the Era of Machine Learning
      Rob Arnott, Campbell R. Harvey and Harry Markowitz
      The Journal of Financial Data Science Winter 2019, 1 (1) 64-74; DOI: https://doi.org/10.3905/jfds.2019.1.064

  3. Monk, Ashby

    1. Rethinking Alternative Data in Institutional Investment
      Ashby Monk, Marcel Prins and Dane Rook
      The Journal of Financial Data Science Winter 2019, 1 (1) 14-31; DOI: https://doi.org/10.3905/jfds.2019.1.1.014

N

  1. Narayanam, Vyshaal

    1. A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model
      Joseph Simonian, Chenwei Wu, Daniel Itano and Vyshaal Narayanam
      The Journal of Financial Data Science Winter 2019, 1 (1) 32-44; DOI: https://doi.org/10.3905/jfds.2019.1.032

O

  1. Ostrov, Daniel N.

    1. Dynamic Systemic Risk: Networks in Data Science
      Sanjiv R. Das, Seoyoung Kim and Daniel N. Ostrov
      The Journal of Financial Data Science Winter 2019, 1 (1) 141-158; DOI: https://doi.org/10.3905/jfds.2019.1.1.141

P

  1. Porter, Sheridan

    1. Introducing Objective Benchmark-Based Attribution in Private Equity
      Sidney C. Porter and Sheridan Porter
      The Journal of Financial Data Science Winter 2019, 1 (1) 130-140; DOI: https://doi.org/10.3905/jfds.2019.1.1.130

  2. Porter, Sidney C.

    1. Introducing Objective Benchmark-Based Attribution in Private Equity
      Sidney C. Porter and Sheridan Porter
      The Journal of Financial Data Science Winter 2019, 1 (1) 130-140; DOI: https://doi.org/10.3905/jfds.2019.1.1.130

  3. Prins, Marcel

    1. Rethinking Alternative Data in Institutional Investment
      Ashby Monk, Marcel Prins and Dane Rook
      The Journal of Financial Data Science Winter 2019, 1 (1) 14-31; DOI: https://doi.org/10.3905/jfds.2019.1.1.014

R

  1. Radnor, Sam

    1. Modeling Analysts’ Recommendations via Bayesian Machine Learning
      David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
      The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075

  2. Ritter, Gordon

    1. Dynamic Replication and Hedging: A Reinforcement Learning Approach
      Petter N. Kolm and Gordon Ritter
      The Journal of Financial Data Science Winter 2019, 1 (1) 159-171; DOI: https://doi.org/10.3905/jfds.2019.1.1.159

  3. Rook, Dane

    1. Rethinking Alternative Data in Institutional Investment
      Ashby Monk, Marcel Prins and Dane Rook
      The Journal of Financial Data Science Winter 2019, 1 (1) 14-31; DOI: https://doi.org/10.3905/jfds.2019.1.1.014

S

  1. Simonian, Joseph

    1. A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model
      Joseph Simonian, Chenwei Wu, Daniel Itano and Vyshaal Narayanam
      The Journal of Financial Data Science Winter 2019, 1 (1) 32-44; DOI: https://doi.org/10.3905/jfds.2019.1.032
    2. Triumph of the Empiricists: The Birth of Financial Data Science
      Joseph Simonian and Frank J. Fabozzi
      The Journal of Financial Data Science Winter 2019, 1 (1) 10-13; DOI: https://doi.org/10.3905/jfds.2019.1.010
    3. Open Access
      Editor’s Letter
      Frank J. Fabozzi, Marcos López de Prado and Joseph Simonian
      The Journal of Financial Data Science Winter 2019, 1 (1) 1-3; DOI: https://doi.org/10.3905/jfds.2019.1.001

  2. Sinclair, Andrew

    1. Modeling Analysts’ Recommendations via Bayesian Machine Learning
      David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
      The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075

  3. Sobczyk, Aleksander

    1. Fine-Tuning Private Equity Replication Using Textual Analysis
      Ananth Madhavan and Aleksander Sobczyk
      The Journal of Financial Data Science Winter 2019, 1 (1) 111-121; DOI: https://doi.org/10.3905/jfds.2019.1.1.111

  4. Suslava, Kate

    1. A Practical Approach to Advanced Text Mining in Finance
      Julia Klevak, Joshua Livnat and Kate Suslava
      The Journal of Financial Data Science Winter 2019, 1 (1) 122-129; DOI: https://doi.org/10.3905/jfds.2019.1.1.122

W

  1. Wu, Chenwei

    1. A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model
      Joseph Simonian, Chenwei Wu, Daniel Itano and Vyshaal Narayanam
      The Journal of Financial Data Science Winter 2019, 1 (1) 32-44; DOI: https://doi.org/10.3905/jfds.2019.1.032
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