RSS Parrot

Intraday Prediction of Operating-Rate Deviations from the Policy Rate: Evidence from Peru

https://d.repec.org/n?u=RePEc:gii:giihei:heidwp17-2026&r=&r=cmp

Published: July 16, 2026 00:00

This paper develops an intraday early-warning framework to predict deviations of the volumeweighted overnight interbank rate from the BCRP policy rate after the close of the Central Bank’s second intervention window. We study both upward deviations,…

Using AI to Let History Speak About Bank Runs

https://d.repec.org/n?u=RePEc:fip:fednls:103501&r=&r=cmp

Published: July 7, 2026 00:00

Banking crises are commonly associated with bank runs and banking panics, yet our empirical understanding of bank runs is constrained by a lack of bank-level data. In a new paper, we use large language models (LLMs) to extract information on bank runs from…

Place Marketing or Civic Information? Classifying Municipal Tweets using Machine Learning

https://d.repec.org/n?u=RePEc:hhs:iuiwop:1563&r=&r=cmp

Published: June 22, 2026 00:00

How do municipalities use social media? For place marketing, civic information, or dialogue with citizens? We address this question by classifying 35, 930 tweets from 15 municipal Twitter accounts in the Skåne region of Sweden, using a machine learning…

(Human) Attention Is (Still) All You Need: Human Oversight Makes Ai-Assisted Social Science

https://d.repec.org/n?u=RePEc:cam:camdae:2643&r=&r=cmp

Published: June 12, 2026 00:00

Large language models (LLMs) are increasingly delegated tasks once reserved for trained researchers: generating hypotheses, choosing specifications, drafting conclusions. Whether this delegation produces trustworthy science is not solely a technical…

Model Diversity Over Model Size: Unanimous LLM Ensembles Correct Over-Classification in Survey Coding

https://d.repec.org/n?u=RePEc:osf:socarx:er6mz_v1&r=&r=cmp

Published: June 5, 2026 00:00

Large language models are increasingly used to classify open-ended survey responses, but they systematically over-classify, assigning categories too liberally on ambiguous cases and producing high sensitivity but low precision. This problem is most severe…

High Agreement, Different Stories: How LLM Classifiers Reshape Demographic Patterns in Survey Data

https://d.repec.org/n?u=RePEc:osf:socarx:85kyd_v1&r=&r=cmp

Published: June 3, 2026 00:00

What we learn from open-ended survey data depends on who—or what—does the coding. Large Language Models (LLMs) promise to democratize qualitative analysis, but do high agreement rates translate into equivalent thematic findings? This study compares eight…

Large Language Models for Statistical Analysis: Can they Replace Domain-Specific Software Packages?

https://d.repec.org/n?u=RePEc:osf:metaar:zj5pc_v1&r=&r=cmp

Published: May 21, 2026 00:00

Large language models (LLMs) represent one of the most significant advances in artificial intelligence in recent decades. Although LLMs are widely used to generate statistical code in domain-specific languages such as R, Python, and SAS, they are…

Following the Crowd: Literature Support and the Capabilities of Autonomous Research Agents

https://d.repec.org/n?u=RePEc:gii:giihei:heidwp14-2026&r=&r=cmp

Published: May 18, 2026 00:00

Machine-learning models often perform poorly when asked to generalize beyond the support of the training distribution. This paper asks whether the same limitation shapes the research capabilities of autonomous large language model (LLM) agents: do they…

Automating Evidence Synthesis: A Comparative Evaluation of Large Language Models for Data Extraction

https://d.repec.org/n?u=RePEc:ris:adbewp:022484&r=&r=cmp

Published: May 15, 2026 00:00

Systematic reviews and meta-analyses (SRMAs) are important tools for evidence synthesis but have historically required substantial manual effort, particularly during the data extraction phase. To address this bottleneck, we developed and evaluated an…

When LLM Signals Hurt: A Coverage-Density Analysis of LLM-Augmented Reinforcement Learning for Stock Trading

https://d.repec.org/n?u=RePEc:osf:socarx:nxvdp_v1&r=&r=cmp

Published: May 14, 2026 00:00

We evaluate LLM-augmented reinforcement learning for stock trading on Nasdaq- 100 (2019–2023) and report a previously unmeasured experimental phenomenon: the relationship between LLM signal coverage density and trading performance is non-monotonic, with a…

LLM + Neutrosophic fsQCA: Inter-Narrative Causal Consistency and Paraconsistent Detection in Media Accounts of Urban Violence in Guayaquil

https://d.repec.org/n?u=RePEc:osf:socarx:yxdsb_v1&r=&r=cmp

Published: May 7, 2026 00:00

Urban violence in Guayaquil, Ecuador has reached crisis levels, yet causal explanations remain fragmented across local and international media. This paper introduces N-fsQCA, a Neutrosophic extension of fuzzy-set Qualitative Comparative Analysis (fsQCA),…

Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting

https://d.repec.org/n?u=RePEc:hal:journl:hal-05562231&r=&r=cmp

Published: May 1, 2026 00:00

This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price forecasting in the German, French, and Norwegian markets. Regimes are inferred via a disentangled sticky hierarchical Dirichlet process hidden…