How Do Investors Learn as Data Becomes Bigger? Evidence From a Fintech Platform

Guecioueur, Ahmed

doi:10.2139/ssrn.3708476

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Comparison With Existing Results1

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2022

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“…It is worth noting that economic agents may use a sparse and parsimonious view of the world, due, for example, to bounded rationality (Gabaix (2014)) or model uncertainty (Guecioueur (2020)), and their demand functions may therefore depend only on a sparse set of characteristics (Nagel (2021)). Interesting topics for future research are linking such sparse demand functions to the sparsity in return predictability that we detect in our setting, and linking investor clienteles to our estimated partition of the cross section of firms.…”

Section: Comparison With Existing Resultsmentioning

confidence: 99%

Uncovering Sparsity and Heterogeneity in Firm-Level Return Predictability Using Machine Learning

Evgeniou¹,

Guecioueur

Prieto³

2022

J. Financ. Quant. Anal.

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We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

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Section: Comparison With Existing Resultsmentioning

confidence: 99%