LLM-Driven Investment Models: Can Large Language Models Extract Alpha from Earnings Call Transcripts?
Ghosal (2026) studies news sentiment and text signals in equities using a large language model over 2019–2023. The abstract mentions alpha, an out-of-sample test and transaction costs but not a benchmark, multiple testing or the size of the universe.
“Compared with a TF-IDF benchmark model, LLM embeddings produce stronger risk-adjusted performance and improved portfolio stability.”
AssetEquities
StrategyNews sentimentmachine learning
TechniqueLarge language model
Sample window2019–2023
Also stated: Alpha, Fundamentals, Text and news, out-of-sample test, costs
Missing from abstract: benchmark, 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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