The Artificial Intelligence Approach to Picking Stocks

Borghi, Riccardo; Rossi, Giuliano

doi:10.1002/9781119751182.ch4

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Tan, Quek, and Cheng (2011), Tsai et al (2011), Geva and Zahavi (2014, Nuij et al (2014), and Krauss, Do, and Huck (2017) provide examples of the application of this approach to the equity market. In a recent paper, Borghi and De Rossi (2020) experiment with an ensemble of random forest models, NN, gradient-boosted trees, and regularized regressions to predict stock returns. Their main conclusion is that a trading strategy based on model combinations tends to outperform strategies based on individual ML models.…”

Section: Explore Ways To Interpret Nns Statisticallymentioning

confidence: 99%

Machine Learning for Active Portfolio Management

Bartram

Branke

Rossi³

et al. 2021

JFDS

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KEY FINDINGSn Machine learning (ML) methods have several advantages that can lead to successful applications in active portfolio management, including the ability to capture nonlinear patterns and a focus on prediction through ensemble learning.n ML methods can be applied to different steps of the investment process, including signal generation, portfolio construction, and trade execution, with reinforcement learning expected to play a more significant role in the industry.n Empirically, the investment performance of ML-based active exchange-traded funds is mixed.

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Section: Explore Ways To Interpret Nns Statisticallymentioning

confidence: 99%

Machine Learning for Active Portfolio Management

Bartram

Branke

Rossi³

et al. 2021

JFDS

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“…ML strategies tend to have higher turnover, and hence higher implementation costs, further emphasizing the need to approach such complex techniques with caution. Borghi and de Rossi ( 2020 ) estimate a series of models along the lines of Gu et al ( 2020 ) and apply trading constraints when optimizing the portfolio, i.e. they limit turnover and the amount traded in each stock based on its average daily (trading) volume.…”

Section: Notable Species In the Factor Zoomentioning

confidence: 99%

Navigating the factor zoo around the world: an institutional investor perspective

et al. 2021

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The literature on cross-sectional stock return predictability has documented over 450 factors. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying “true” factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. From a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors is dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income, and commodities. These factors can serve as meaningful ingredients to factor-based portfolio construction.

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