Skill UI

A comprehensive methodological guide for conducting rigorous quantitative research in finance, covering asset pricing, corporate finance, and econometrics. This skill defines the necessary standards for factor construction, portfolio sorting, advanced estimators (Fama-MacBeth, GMM), robust standard error clustering, and multiple-testing discipline. It ensures research designs are academically sound, reproducible, and suitable for top-tier financial journals.

Provides a comprehensive framework for constructing a robust battery of empirical tests, tailored for high-impact academic journals like JFE. This guide covers alternative measures, specifications, subsamples, and falsification tests, ensuring research findings are highly credible and capable of withstanding rigorous academic scrutiny.

This guide provides expert standards for creating rigorous, self-contained tables and figures for top-tier academic journals (like JFE). It covers detailed conventions, such as structuring exhibits to convey a single, clear message, consistently reporting statistical metrics (SEs/t-stats), and ensuring figures visualize patterns rather than merely decorate. Essential for submitting polished, publication-ready empirical research.

This tool guides researchers in scoping and rigorously testing a research topic for submission to top-tier finance journals, such as the Journal of Financial Economics (JFE). It helps users refine an economic question, establish a novel contribution, identify plausible causal links, and assess the paper's depth against high academic standards (e.g., Jensen or Fama-DFA prizes). It is crucial for transforming a general academic idea into a publishable, high-impact manuscript.

This guide provides a comprehensive framework for rigorously identifying and defending the methodological core of academic papers in finance and banking. It covers both empirical tracks (e.g., staggered DID, RDD, causal design for bank data) and theoretical tracks (defining necessary assumptions and analytical discipline). It helps researchers move beyond default methods to address complex challenges like internal versus external validity and causality separation.

Provides a structured guide for authors responding to review and revision (R&R) comments for top-tier finance journals like the Journal of Financial Intermediation (JFI). It teaches how to structure point-by-point rebuttals, address identification challenges (empirical and theoretical), and maintain a credible, evidence-based tone essential for single-blind academic review.

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.

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 comprehensive guide to positioning research contributions for top-tier financial microstructure journals, such as the Journal of Financial Markets (JFM). It teaches authors how to articulate their novel findings by anchoring them within established academic lineages (e.g., Glosten-Milgrom) and explicitly identifying the gap relative to 3-5 closest prior works. Focus must be placed on market mechanics and trading-process advances, rather than general financial outcomes.

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.

A comprehensive guide for researchers preparing manuscripts for top-tier financial journals. It teaches advanced strategies for conducting a pre-submission pre-mortem, anticipating specific econometric objections (e.g., endogeneity, measurement artifacts), and systematically triaging reviewer feedback (fatal, fixable, defensible). Ideal for improving research robustness and maximizing acceptance chances.

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.