Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis
A 2023 paper by Glasserman and Lin on machine learning return prediction in equities, using a large language model: the abstract mentions excess returns, profits and an out-of-sample test and leaves out a benchmark, transaction costs, multiple testing and the size of the universe.
“In-sample (within the LLM training window), we find, surprisingly, that the anonymized headlines outperform, indicating that the distraction effect has a greater impact than look-ahead bias.”
AssetEquities
StrategyMachine learningnews sentiment
TechniqueLarge language model
ClaimExcess returnprofit
Also stated: Text and news, out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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