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The Journal of Financial Data Science

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Crowdsourced Investment Research Through Tournaments

Marcos López de Prado and Frank J. Fabozzi
The Journal of Financial Data Science Winter 2020, 2 (1) 86-93; DOI: https://doi.org/10.3905/jfds.2019.1.016
Marcos López de Prado
is the CIO of True Positive Technologies LP, in New York, NY, and a professor of practice at Cornell University in Ithaca, NY
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Frank J. Fabozzi
is a professor of finance at EDHEC Business School in Nice, France
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Abstract

Traditionally, the development of investment strategies has required domain-specific knowledge and access to restricted datasets. Hence, the majority of data scientists do not research investment opportunities because they lack one or both of these requirements. In this article, the authors discuss the merits of tournaments as a crowdsourcing paradigm for investment research. Tournaments can overcome these research barriers, hence enabling the wide population of data scientists to contribute to the development of investment strategies.

TOPICS: Simulations, big data/machine learning

Key Findings

  • • There are several barriers to the development of investment strategies: domain-specific knowledge barrier, budgetary constraints and confidentiality restrictions, inability to monetize the value of data, and backtest overfitting.

  • • Tournaments offer a solution for overcoming the barriers associated with developing investment strategies.

  • • The modern investment process the authors suggest allows data scientists without an investment background to contribute forecasts to a systematic asset manager.

  • • This does not mean that tournaments obviate the need for financial expertise. On the contrary, deep financial expertise is required to cast the problem crowdsourced through the tournament.

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Crowdsourced Investment Research Through Tournaments
Marcos López de Prado, Frank J. Fabozzi
The Journal of Financial Data Science Jan 2020, 2 (1) 86-93; DOI: 10.3905/jfds.2019.1.016

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Crowdsourced Investment Research Through Tournaments
Marcos López de Prado, Frank J. Fabozzi
The Journal of Financial Data Science Jan 2020, 2 (1) 86-93; DOI: 10.3905/jfds.2019.1.016
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