Skill UI

This guide provides rigorous conventions for constructing self-contained tables and figures for submissions to high-tier finance journals (e.g., JFI). It focuses on structuring exhibits—such as sample construction, event studies, and fixed-effect regressions—to make complex econometric findings accessible and persuasive to banking and intermediation specialists. It guides the visual narrative, ensuring every exhibit is self-sufficient and methodologically transparent.

A comprehensive guide for researchers tackling endogeneity in market microstructure. It outlines advanced econometric designs—such as Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), and rigorous event studies—necessary to establish credible causal links between market variables (like liquidity and price impact) and macro-level events for top-tier finance journals.

This guide provides a detailed strategy for authors responding to reviewers' comments (R&R or reject-and-resubmit) for top-tier academic journals, particularly in finance and econometrics. It teaches systematic methods for structuring the rebuttal letter, addressing measurement artifacts, managing endogeneity concerns, and maintaining the transparency of core results to maximize acceptance chances.

This guide provides rigorous best practices for authors presenting empirical evidence in microstructure finance papers. It ensures that tables and figures are self-contained, verifiable, and immediately convey the core mechanism and headline results to a skeptical academic referee, moving beyond simple descriptive statistics.

This router guides researchers through the entire lifecycle of a manuscript submitted to the Journal of Financial Markets (JFM). It provides a structured sequence of skills, from topic selection and literature positioning to empirical design, robustness checking, and rebuttal preparation. It ensures the focus remains strictly on market microstructure mechanisms, preventing common pitfalls like over-reliance on corporate finance models.

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 comprehensive guide to designing highly defensible and causal research studies for top-tier finance journals, such as the Journal of Financial and Quantitative Analysis (JFQA). It covers advanced econometric methods for corporate finance, asset pricing, and policy shocks, including Fixed Effects, Staggered Difference-in-Differences (DID), Instrumental Variables (IV), and Regression Discontinuity Design (RDD). Focuses on addressing endogeneity, reporting economic magnitude, and ensuring robust inference.

Use this skill to rigorously evaluate whether a finance research question meets the high quantitative and empirical standards required by the Journal of Financial and Quantitative Analysis (JFQA). It guides authors through a comprehensive checklist, assessing financial object, quantitative core, identification feasibility, and novelty, helping authors optimize their academic contribution before investing time and submission fees.

This skill adapts scholarly drafts to the specific, rigorous style of the Journal of Financial and Quantitative Analysis (JFQA). It enforces quantitative prose, strict formatting (e.g., 100-word abstract, TNR 12pt, double-spacing), and structural rules necessary for high-impact finance submissions. Use it to polish content, ensure precision, and guarantee compliance with top academic journal standards.

This guide provides advanced econometric methodologies crucial for establishing causality in financial and macro research, particularly when analyzing policy shocks (e.g., LPR changes, regulatory reforms). It details the correct application of Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and high-frequency analysis, ensuring robust empirical design for top-tier journals.

This guide helps researchers structure the institutional background section of advanced finance manuscripts. It emphasizes moving beyond general descriptions to precisely articulate regulatory rules, market segmentation, and ownership structures. The goal is to transform descriptive text into clear evidence for identifying exogeneity and analyzing policy shocks in empirical studies.

A comprehensive guide on transforming preliminary research findings into publication-quality manuscripts for top-tier finance journals. It covers best practices for slimming over-wide regression tables, generating standardized figures (IRF, Event Study), ensuring continuous numbering, and adhering to strict academic formatting standards.