The Journal of Financial Data Science: 1 (1)

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Winter 2019; Volume 1,Issue 1

Editor’s Letter

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

Triumph of the Empiricists: The Birth of Financial Data Science

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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

Rethinking Alternative Data in Institutional Investment

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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

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

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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

Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization

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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

A Backtesting Protocol in the Era of Machine Learning

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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

Modeling Analysts’ Recommendations via Bayesian Machine Learning

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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

A Data Science Solution to the Multiple-Testing Crisis in Financial Research

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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

Fine-Tuning Private Equity Replication Using Textual Analysis

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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

A Practical Approach to Advanced Text Mining in Finance

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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

Introducing Objective Benchmark-Based Attribution in Private Equity

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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

Dynamic Systemic Risk: Networks in Data Science

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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

Dynamic Replication and Hedging: A Reinforcement Learning Approach

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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

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The Journal of Financial Data Science

Index by author

Editor’s Letter

Triumph of the Empiricists: The Birth of Financial Data Science

Rethinking Alternative Data in Institutional Investment

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization

A Backtesting Protocol in the Era of Machine Learning

Modeling Analysts’ Recommendations via Bayesian Machine Learning

A Data Science Solution to the Multiple-Testing Crisis in Financial Research

Fine-Tuning Private Equity Replication Using Textual Analysis

A Practical Approach to Advanced Text Mining in Finance

Introducing Objective Benchmark-Based Attribution in Private Equity

Dynamic Systemic Risk: Networks in Data Science

Dynamic Replication and Hedging: A Reinforcement Learning Approach

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Index by author

Editor’s Letter

Triumph of the Empiricists: The Birth of Financial Data Science

Rethinking Alternative Data in Institutional Investment

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization

A Backtesting Protocol in the Era of Machine Learning

Modeling Analysts’ Recommendations via Bayesian Machine Learning

A Data Science Solution to the Multiple-Testing Crisis in Financial Research

Fine-Tuning Private Equity Replication Using Textual Analysis

A Practical Approach to Advanced Text Mining in Finance

Introducing Objective Benchmark-Based Attribution in Private Equity

Dynamic Systemic Risk: Networks in Data Science

Dynamic Replication and Hedging: A Reinforcement Learning Approach

Explore our content to discover more relevant research

In this issue

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