GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models
Gupta (2023) studies news sentiment and text signals in equities using a large language model. The abstract mentions excess returns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The walkforward test results show promising outperformance wrt S&P500 returns.”
AssetEquitiesindices
StrategyNews sentiment
TechniqueLarge language model
GeographyUS
Also stated: Annual, Excess return, Text and news, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodNews sentiment and text signals by year
FamilyNews sentiment and text signals148Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 854
AssetEquities97
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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