Table of Contents

Spring 2021; Volume 3,Issue 2

A

  1. Ahluwalia, Harshdeep Singh

    1. The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach
      Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz and Joseph H. Davis
      The Journal of Financial Data Science Spring 2021, 3 (2) 9-20; DOI: https://doi.org/10.3905/jfds.2021.3.2.009

  2. Aliaga-Díaz, Roger A.

    1. The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach
      Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz and Joseph H. Davis
      The Journal of Financial Data Science Spring 2021, 3 (2) 9-20; DOI: https://doi.org/10.3905/jfds.2021.3.2.009

D

  1. Davis, Joseph H.

    1. The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach
      Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz and Joseph H. Davis
      The Journal of Financial Data Science Spring 2021, 3 (2) 9-20; DOI: https://doi.org/10.3905/jfds.2021.3.2.009

F

  1. Fabozzi, Francesco A.

    1. Open Access
      Managing Editor’s Letter
      Francesco A. Fabozzi
      The Journal of Financial Data Science Spring 2021, 3 (2) 1-3; DOI: https://doi.org/10.3905/jfds.2021.3.2.001

  2. Fallahgoul, Hasan

    1. Inside the Mind of Investors During the COVID-19 Pandemic: Evidence from the StockTwits Data
      Hasan Fallahgoul
      The Journal of Financial Data Science Spring 2021, 3 (2) 134-148; DOI: https://doi.org/10.3905/jfds.2021.1.058

G

  1. Galakis, John

    1. Style Rotation Revisited
      John Galakis, Ioannis Vrontos and Spyridon Vrontos
      The Journal of Financial Data Science Spring 2021, 3 (2) 110-133; DOI: https://doi.org/10.3905/jfds.2021.1.059

J

  1. Jaeger, Markus

    1. Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
      Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel
      The Journal of Financial Data Science Spring 2021, 3 (2) 51-69; DOI: https://doi.org/10.3905/jfds.2021.1.056

K

  1. Krügel, Stephan

    1. Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
      Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel
      The Journal of Financial Data Science Spring 2021, 3 (2) 51-69; DOI: https://doi.org/10.3905/jfds.2021.1.056

L

  1. Leung, Edward

    1. The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
      Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh
      The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

  2. Lim, Bryan

    1. Building Cross-Sectional Systematic Strategies by Learning to Rank
      Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts
      The Journal of Financial Data Science Spring 2021, 3 (2) 70-86; DOI: https://doi.org/10.3905/jfds.2021.1.060

  3. Lohre, Harald

    1. The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
      Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh
      The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

M

  1. Mischlich, David

    1. The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
      Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh
      The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

  2. Mulvey, John M.

    1. A Machine Learning Approach in Regime-Switching Risk Parity Portfolios
      A. Sinem Uysal and John M. Mulvey
      The Journal of Financial Data Science Spring 2021, 3 (2) 87-108; DOI: https://doi.org/10.3905/jfds.2021.1.057

P

  1. Papenbrock, Jochen

    1. Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
      Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel
      The Journal of Financial Data Science Spring 2021, 3 (2) 51-69; DOI: https://doi.org/10.3905/jfds.2021.1.056

  2. Poh, Daniel

    1. Building Cross-Sectional Systematic Strategies by Learning to Rank
      Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts
      The Journal of Financial Data Science Spring 2021, 3 (2) 70-86; DOI: https://doi.org/10.3905/jfds.2021.1.060

  3. Puglia, Michael

    1. Open Access
      Neural Networks, the Treasury Yield Curve, and Recession Forecasting
      Michael Puglia and Adam Tucker
      The Journal of Financial Data Science Spring 2021, 3 (2) 149-175; DOI: https://doi.org/10.3905/jfds.2021.1.061

R

  1. Roberts, Stephen

    1. Building Cross-Sectional Systematic Strategies by Learning to Rank
      Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts
      The Journal of Financial Data Science Spring 2021, 3 (2) 70-86; DOI: https://doi.org/10.3905/jfds.2021.1.060

S

  1. Schwendner, Peter

    1. Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
      Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel
      The Journal of Financial Data Science Spring 2021, 3 (2) 51-69; DOI: https://doi.org/10.3905/jfds.2021.1.056

  2. Shea, Yifei

    1. The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
      Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh
      The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

  3. Stroh, Maximilian

    1. The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
      Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh
      The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

T

  1. Tucker, Adam

    1. Open Access
      Neural Networks, the Treasury Yield Curve, and Recession Forecasting
      Michael Puglia and Adam Tucker
      The Journal of Financial Data Science Spring 2021, 3 (2) 149-175; DOI: https://doi.org/10.3905/jfds.2021.1.061

U

  1. Uysal, A. Sinem

    1. A Machine Learning Approach in Regime-Switching Risk Parity Portfolios
      A. Sinem Uysal and John M. Mulvey
      The Journal of Financial Data Science Spring 2021, 3 (2) 87-108; DOI: https://doi.org/10.3905/jfds.2021.1.057

V

  1. Vrontos, Ioannis

    1. Style Rotation Revisited
      John Galakis, Ioannis Vrontos and Spyridon Vrontos
      The Journal of Financial Data Science Spring 2021, 3 (2) 110-133; DOI: https://doi.org/10.3905/jfds.2021.1.059

  2. Vrontos, Spyridon

    1. Style Rotation Revisited
      John Galakis, Ioannis Vrontos and Spyridon Vrontos
      The Journal of Financial Data Science Spring 2021, 3 (2) 110-133; DOI: https://doi.org/10.3905/jfds.2021.1.059

W

  1. Wang, Haifeng

    1. The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach
      Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz and Joseph H. Davis
      The Journal of Financial Data Science Spring 2021, 3 (2) 9-20; DOI: https://doi.org/10.3905/jfds.2021.3.2.009

Z

  1. Zohren, Stefan

    1. Building Cross-Sectional Systematic Strategies by Learning to Rank
      Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts
      The Journal of Financial Data Science Spring 2021, 3 (2) 70-86; DOI: https://doi.org/10.3905/jfds.2021.1.060
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