Skill UI

This guide provides best practices for empirical design and microstructure measurement in financial markets. It covers cleaning high-frequency TAQ/order-book data, constructing robust liquidity, spread, and price-impact measures, and meticulously documenting all sample filters and calculation assumptions. Essential for academic research where measurement rigor and transparency are paramount.

A detailed guide for constructing a comprehensive robustness ledger in empirical financial research, particularly for microstructural data. It structures checks to address common academic threats, such as measurement bias, sample selection, microstructure noise (e.g., bid-ask bounce), and flawed inference, ensuring findings are methodologically sound and highly defensible in top-tier journals.

This comprehensive guide assists in executing and documenting rigorous empirical finance research, tailored for top-tier journals like JFQA. It covers the entire pipeline, including complex financial data construction (CRSP, Compustat), advanced data cleaning (winsorizing, outlier handling), and sophisticated econometrics (fixed effects, clustered standard errors, Fama-MacBeth). The core focus is achieving absolute reproducibility, ensuring that all filters, robustness checks, and analytical steps are meticulously documented for archival and peer review.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This skill guides authors through the rigorous, multi-stage submission process for the Journal of Financial and Quantitative Analysis (JFQA). It acts as a detailed router, providing the necessary next steps—from topic selection and identifying credible design (DID, IV, RDD) to structuring the contribution, managing data archives, and preparing the rebuttal. It ensures manuscripts meet the high quantitative and academic standards required by this top-tier finance journal.

This guide provides advanced methodological principles for conducting theoretically grounded heterogeneity analysis in financial research. It emphasizes that any dimension used for splitting the data (e.g., ownership, bank type, financial constraint) must be predicted by the underlying economic mechanism, rather than being an arbitrary data exploration. It details best practices, statistical rigor (group coefficient difference testing), and common pitfalls to avoid.

This comprehensive guide helps academics transform raw financial data observations into rigorous and high-impact research questions suitable for top-tier finance journals. It teaches a structured methodology involving anchoring the core financial mechanism, integrating specific institutional contexts within China, and defining a clear incremental academic contribution.

A detailed guide for researchers tackling advanced financial-mathematics problems. It outlines the stringent criteria for suitability in top-tier journals, emphasizing methodological novelty, mathematical rigor, and deep theoretical contribution (e.g., stochastic analysis, structural theorems) over routine data computation. Use this to refine and scope your research proposal.

This skill guide teaches how to use a CLI tool to programmatically create, modify, and validate complex Excel (.xlsx) files. It covers everything from building robust financial models and dashboards to parsing raw data and ensuring the output adheres to professional standards, including zero formula errors and proper print layouts. Ideal for data engineering and business intelligence workflows.

This guide provides comprehensive rules for organizing the Internet Appendix (IA) for submissions to top academic journals, such as The Review of Financial Studies (RFS). It clarifies which material—such as full robustness checks, detailed proofs, or data construction methods—should reside in the main manuscript and which belongs in the supplementary IA. This ensures the core paper remains impactful, clean, and structurally rigorous.

Comprehensive guidelines for auditing and reproducing high-stakes empirical financial research. This resource details the rigorous standards required for top-tier finance journals, covering sample construction, identifying variables, advanced econometric modeling (e.g., panel data, factor pricing), handling international cross-market data, and establishing a fully reproducible data pipeline. Essential for ensuring the robustness and credibility of finance findings.

This tool generates comprehensive regulatory compliance reports specifically tailored for financial institutions operating in Japan. It adheres strictly to the guidelines set by the Japanese Financial Services Agency (FSA), ensuring the output is formatted correctly and delivered in Japanese (ja_JP) as mandated by Japanese regulations. Users simply provide the required compliance data and reporting period to generate official, legally compliant reports.