4 citations · 4 across the 3 of their papers we have counts for
Showing q-fin.GNShow all
3 papers · 1 filter
q-fin.GN2025
Anonymization and Information Loss
Ke Wu, Baozhong Yang, Zhenkun Ying +1
Anonymizing financial texts prevents large language models (LLMs) from exploiting look-ahead bias, but inadvertently weakens the extracted signal. We propose a framework disentangl…
q-fin.GN2024
The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts
Edward Li, Min Shen, Zhiyuan Tu +1
Large language models (LLMs) promise to democratize financial analysis by reducing information-processing costs. Yet equal access does not ensure equal outcomes, as the locus of fr…
q-fin.GN2024★ 4 cited
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
Shuaiyu Chen, T. Clifton Green, Huseyin Gulen +1
We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While st…