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The Journal of Financial Data Science

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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
Joseph Simonian
is the director of quantitative research at Natixis Investment Managers in Boston, MA
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Chenwei Wu
is a quantitative analyst at Natixis Investment Managers in Boston, MA
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Daniel Itano
is a senior quantitative analyst at Natixis Investment Managers in Boston, MA
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Vyshaal Narayanam
is a data science co-op at Natixis Investment Managers in Boston, MA
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Abstract

Factor models are by now ubiquitous in finance and form an integral part of investment practice. The most common models in the investment industry are linear, a development that is no doubt the result of their familiarity and relative simplicity. Linear models, however, often fail to capture important information regarding asset behavior. To address the latter shortcoming, the authors show how to use random forests, a machine learning algorithm, to produce factor frameworks that improve upon more traditional models in terms of their ability to account for nonlinearities and interaction effects among variables, as well as their higher explanatory power. The authors also demonstrate, by means of a simple example, how combining the random forest algorithm with another machine learning framework known as association rule learning can produce viable trading strategies. Machine learning methods thus show themselves to be effective tools for both ex post risk decomposition and ex ante investment decision-making.

TOPICS: Factor-based models, big data/machine learning, portfolio management/multi-asset allocation

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The Journal of Financial Data Science: 1 (1)
The Journal of Financial Data Science
Vol. 1, Issue 1
Winter 2019
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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, Vyshaal Narayanam
The Journal of Financial Data Science Jan 2019, 1 (1) 32-44; DOI: 10.3905/jfds.2019.1.032

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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, Vyshaal Narayanam
The Journal of Financial Data Science Jan 2019, 1 (1) 32-44; DOI: 10.3905/jfds.2019.1.032
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  • Article
    • Abstract
    • BASIC FEATURES OF FACTOR MODELS
    • MACHINE LEARNING AND THE RANDOM FOREST ALGORITHM
    • BUILDING FACTOR MODELS USING RANDOM FORESTS
    • USING FEATURE IMPORTANCES TO DERIVE PSEUDO-BETAS
    • TRADING APPLICATION: BUILDING A SECTOR ROTATION STRATEGY USING THE RF FFC MODEL AND ASSOCIATION RULE LEARNING
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF

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